The Impact of Intraday Timing of Earnings Announcements on the Bid-Ask Spread and Depth

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投资银行学的英语作文

Investment banking is a specialized area of finance that deals with the raising of capital by corporations,governments,and other entities through the issuance of securities. Here are some key points to consider when writing an essay on investment banking:1.Introduction to Investment Banking:Begin by defining investment banking and explaining its role in the financial markets.Mention how it differs from commercial banking and retail banking.2.History of Investment Banking:Provide a brief historical overview of the development of investment banking,including the evolution of securities markets and the role of investment banks in major financial events.3.Services Offered by Investment Banks:Equity Capital Markets ECM:Discuss how investment banks help companies raise equity capital through initial public offerings IPOs,followon offerings,and other equity transactions.Debt Capital Markets DCM:Explain the process of issuing bonds and other debt instruments to raise funds for corporations and governments.Mergers and Acquisitions MA:Describe the advisory services provided by investment banks in facilitating mergers,acquisitions,and other corporate restructuring activities. Asset Management:Mention how investment banks manage assets on behalf of clients, including investment strategies and portfolio management.4.Regulatory Framework:Discuss the regulatory environment surrounding investment banking,including the impact of laws such as the GlassSteagall Act and the DoddFrank Act in the United States.5.Risk Management:Explain the various risks associated with investment banking activities,such as market risk,credit risk,and operational risk,and how these are managed by investment banks.6.Economic Impact:Analyze the role of investment banks in economic growth,job creation,and the efficient allocation of capital.7.Challenges and Controversies:Address the challenges faced by the industry,such as the2008financial crisis,and the ethical controversies surrounding certain practices.8.Technological Advancements:Discuss how technology,particularly fintech,is transforming the investment banking industry,including the use of blockchain for securities transactions and artificial intelligence for risk analysis and trading.9.Future of Investment Banking:Conclude with a discussion on the future trends in investment banking,such as the potential impact of globalization,the rise of sustainable finance,and the increasing importance of digital currencies.10.Conclusion:Summarize the main points of your essay,emphasizing the significance of investment banking in the global economy and the ongoing changes shaping the industry.Remember to use clear and concise language,provide examples to support your arguments,and cite reliable sources to enhance the credibility of your essay.。

银行招聘考试英语题集

银行招聘考试英语题集题目一1. What is the role of a bank in the economy?题目二2. Explain the concept of interest rates and how they affect the economy.题目三3. What is the difference between a savings account and a checking account?题目四4. What are the key factors to consider when evaluating a borrower's creditworthiness?题目五5. Describe the process of issuing a loan from a bank's perspective. 题目六6. What are the main functions of central banks in a country?题目七7. Explain the concept of inflation and how it impacts the economy. 题目八8. What are the risks associated with investing in the stock market? 题目九9. Discuss the role of technology in the banking industry and its impact on customer experience.题目十10. What are the ethical considerations that banks should take into account when dealing with customers?题目十一11. Describe the steps involved in opening a bank account.题目十二12. What are the main types of financial products offered by banks?题目十三13. Explain the concept of foreign exchange and its importance in international trade.题目十四14. Discuss the role of regulations in the banking industry and their impact on consumer protection.题目十五题目十六16. Explain the concept of money laundering and the measures banks can take to prevent it.题目十七题目十八题目十九19. Describe the process of issuing and managing credit cards.题目二十20. Explain the concept of financial risk management and its importance for banks.以上是银行招聘考试英语题集的题目，希望对您的备考有所帮助。

The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity

The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity Author(s): Marc J. MelitzSource: Econometrica, Vol. 71, No. 6 (Nov., 2003), pp. 1695-1725Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ./page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at .Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@.The Econometric Society is collaborating with JSTOR to digitize, preserve and extend access to Econometrica.。

美国公司法证券法历年经典论文列表

美国是世界上公司法、证券法研究最为发达的国家之一，在美国法学期刊（Law Review & Journals）上每年发表400多篇以公司法和证券法为主题的论文。

自1994年开始，美国的公司法学者每年会投票从中遴选出10篇左右重要的论文，重印于Corporate Practice Commentator，至2008年，已经评选了15年，计177篇论文入选。

以下是每年入选的论文列表：2008年（以第一作者姓名音序为序）：1.Anabtawi, Iman and Lynn Stout. Fiduciary duties for activist shareholders. 60 Stan. L. Rev. 1255-1308 (2008).2.Brummer, Chris. Corporate law preemption in an age of global capital markets. 81 S. Cal. L. Rev. 1067-1114 (2008).3.Choi, Stephen and Marcel Kahan. The market penalty for mutual fund scandals. 87 B.U. L. Rev. 1021-1057 (2007).4.Choi, Stephen J. and Jill E. Fisch. On beyond CalPERS: Survey evidence on the developing role of public pension funds in corporate governance. 61 V and. L. Rev. 315-354 (2008).5.Cox, James D., Randall S. Thoma s and Lynn Bai. There are plaintiffs and…there are plaintiffs: An empirical analysis of securities class action settlements. 61 V and. L. Rev. 355-386 (2008).6.Henderson, M. Todd. Paying CEOs in bankruptcy: Executive compensation when agency costs are low. 101 Nw. U. L. Rev. 1543-1618 (2007).7.Hu, Henry T.C. and Bernard Black. Equity and debt decoupling and empty voting II: Importance and extensions. 156 U. Pa. L. Rev. 625-739 (2008).8.Kahan, Marcel and Edward Rock. The hanging chads of corporate voting. 96 Geo. L.J. 1227-1281 (2008).9.Strine, Leo E., Jr. Toward common sense and common ground? Reflections on the shared interests of managers and labor in a more rational system of corporate governance. 33 J. Corp. L. 1-20 (2007).10.Subramanian, Guhan. Go-shops vs. no-shops in private equity deals: Evidence and implications.63 Bus. Law. 729-760 (2008).2007年：1.Baker, Tom and Sean J. Griffith. The Missing Monitor in Corporate Governance: The Directors’ & Officers’ Liability Insurer. 95 Geo. L.J. 1795-1842 (2007).2.Bebchuk, Lucian A. The Myth of the Shareholder Franchise. 93 V a. L. Rev. 675-732 (2007).3.Choi, Stephen J. and Robert B. Thompson. Securities Litigation and Its Lawyers: Changes During the First Decade After the PSLRA. 106 Colum. L. Rev. 1489-1533 (2006).4.Coffee, John C., Jr. Reforming the Securities Class Action: An Essay on Deterrence and Its Implementation. 106 Colum. L. Rev. 1534-1586 (2006).5.Cox, James D. and Randall S. Thomas. Does the Plaintiff Matter? An Empirical Analysis of Lead Plaintiffs in Securities Class Actions. 106 Colum. L. Rev. 1587-1640 (2006).6.Eisenberg, Theodore and Geoffrey Miller. Ex Ante Choice of Law and Forum: An Empirical Analysis of Corporate Merger Agreements. 59 V and. L. Rev. 1975-2013 (2006).7.Gordon, Jeffrey N. The Rise of Independent Directors in the United States, 1950-2005: Of Shareholder V alue and Stock Market Prices. 59 Stan. L. Rev. 1465-1568 (2007).8.Kahan, Marcel and Edward B. Rock. Hedge Funds in Corporate Governance and Corporate Control. 155 U. Pa. L. Rev. 1021-1093 (2007).ngevoort, Donald C. The Social Construction of Sarbanes-Oxley. 105 Mich. L. Rev. 1817-1855 (2007).10.Roe, Mark J. Legal Origins, Politics, and Modern Stock Markets. 120 Harv. L. Rev. 460-527 (2006).11.Subramanian, Guhan. Post-Siliconix Freeze-outs: Theory and Evidence. 36 J. Legal Stud. 1-26 (2007). (NOTE: This is an earlier working draft. The published article is not freely available, and at SLW we generally respect the intellectual property rights of others.)2006年：1.Bainbridge, Stephen M. Director Primacy and Shareholder Disempowerment. 119 Harv. L. Rev. 1735-1758 (2006).2.Bebchuk, Lucian A. Letting Shareholders Set the Rules. 119 Harv. L. Rev. 1784-1813 (2006).3.Black, Bernard, Brian Cheffins and Michael Klausner. Outside Director Liability. 58 Stan. L. Rev. 1055-1159 (2006).4.Choi, Stephen J., Jill E. Fisch and A.C. Pritchard. Do Institutions Matter? The Impact of the Lead Plaintiff Provision of the Private Securities Litigation Reform Act. 835.Cox, James D. and Randall S. Thomas. Letting Billions Slip Through Y our Fingers: Empirical Evidence and Legal Implications of the Failure of Financial Institutions to Participate in Securities Class Action Settlements. 58 Stan. L. Rev. 411-454 (2005).6.Gilson, Ronald J. Controlling Shareholders and Corporate Governance: Complicating the Comparative Taxonomy. 119 Harv. L. Rev. 1641-1679 (2006).7.Goshen , Zohar and Gideon Parchomovsky. The Essential Role of Securities Regulation. 55 Duke L.J. 711-782 (2006).8.Hansmann, Henry, Reinier Kraakman and Richard Squire. Law and the Rise of the Firm. 119 Harv. L. Rev. 1333-1403 (2006).9.Hu, Henry T. C. and Bernard Black. Empty V oting and Hidden (Morphable) Ownership: Taxonomy, Implications, and Reforms. 61 Bus. Law. 1011-1070 (2006).10.Kahan, Marcel. The Demand for Corporate Law: Statutory Flexibility, Judicial Quality, or Takeover Protection? 22 J. L. Econ. & Org. 340-365 (2006).11.Kahan, Marcel and Edward Rock. Symbiotic Federalism and the Structure of Corporate Law.58 V and. L. Rev. 1573-1622 (2005).12.Smith, D. Gordon. The Exit Structure of V enture Capital. 53 UCLA L. Rev. 315-356 (2005).2005年：1.Bebchuk, Lucian Arye. The case for increasing shareholder power. 118 Harv. L. Rev. 833-914 (2005).2.Bratton, William W. The new dividend puzzle. 93 Geo. L.J. 845-895 (2005).3.Elhauge, Einer. Sacrificing corporate profits in the public interest. 80 N.Y.U. L. Rev. 733-869 (2005).4.Johnson, . Corporate officers and the business judgment rule. 60 Bus. Law. 439-469 (2005).haupt, Curtis J. In the shadow of Delaware? The rise of hostile takeovers in Japan. 105 Colum. L. Rev. 2171-2216 (2005).6.Ribstein, Larry E. Are partners fiduciaries? 2005 U. Ill. L. Rev. 209-251.7.Roe, Mark J. Delaware?s politics. 118 Harv. L. Rev. 2491-2543 (2005).8.Romano, Roberta. The Sarbanes-Oxley Act and the making of quack corporate governance. 114 Y ale L.J. 1521-1611 (2005).9.Subramanian, Guhan. Fixing freezeouts. 115 Y ale L.J. 2-70 (2005).10.Thompson, Robert B. and Randall S. Thomas. The public and private faces of derivative lawsuits. 57 V and. L. Rev. 1747-1793 (2004).11.Weiss, Elliott J. and J. White. File early, then free ride: How Delaware law (mis)shapes shareholder class actions. 57 V and. L. Rev. 1797-1881 (2004).2004年：1Arlen, Jennifer and Eric Talley. Unregulable defenses and the perils of shareholder choice. 152 U. Pa. L. Rev. 577-666 (2003).2.Bainbridge, Stephen M. The business judgment rule as abstention doctrine. 57 V and. L. Rev. 83-130 (2004).3.Bebchuk, Lucian Arye and Alma Cohen. Firms' decisions where to incorporate. 46 J.L. & Econ. 383-425 (2003).4.Blair, Margaret M. Locking in capital: what corporate law achieved for business organizers in the nineteenth century. 51 UCLA L. Rev. 387-455 (2003).5.Gilson, Ronald J. and Jeffrey N. Gordon. Controlling shareholders. 152 U. Pa. L. Rev. 785-843 (2003).6.Roe, Mark J. Delaware 's competition. 117 Harv. L. Rev. 588-646 (2003).7.Sale, Hillary A. Delaware 's good faith. 89 Cornell L. Rev. 456-495 (2004).8.Stout, Lynn A. The mechanisms of market inefficiency: an introduction to the new finance. 28 J. Corp. L. 635-669 (2003).9.Subramanian, Guhan. Bargaining in the shadow of takeover defenses. 113 Y ale L.J. 621-686 (2003).10.Subramanian, Guhan. The disappearing Delaware effect. 20 J.L. Econ. & Org. 32-59 (2004)11.Thompson, Robert B. and Randall S. Thomas. The new look of shareholder litigation: acquisition-oriented class actions. 57 V and. L. Rev. 133-209 (2004).2003年：1.A yres, Ian and Stephen Choi. Internalizing outsider trading. 101 Mich. L. Rev. 313-408 (2002).2.Bainbridge, Stephen M. Director primacy: The means and ends of corporate governance. 97 Nw. U. L. Rev. 547-606 (2003).3.Bebchuk, Lucian, Alma Cohen and Allen Ferrell. Does the evidence favor state competition in corporate law? 90 Cal. L. Rev. 1775-1821 (2002).4.Bebchuk, Lucian Arye, John C. Coates IV and Guhan Subramanian. The Powerful Antitakeover Force of Staggered Boards: Further findings and a reply to symposium participants. 55 Stan. L. Rev. 885-917 (2002).5.Choi, Stephen J. and Jill E. Fisch. How to fix Wall Street: A voucher financing proposal for securities intermediaries. 113 Y ale L.J. 269-346 (2003).6.Daines, Robert. The incorporation choices of IPO firms. 77 N.Y.U. L. Rev.1559-1611 (2002).7.Gilson, Ronald J. and David M. Schizer. Understanding venture capital structure: A taxexplanation for convertible preferred stock. 116 Harv. L. Rev. 874-916 (2003).8.Kahan, Marcel and Ehud Kamar. The myth of state competition in corporate law. 55 Stan. L. Rev. 679-749 (2002).ngevoort, Donald C. Taming the animal spirits of the stock markets: A behavioral approach to securities regulation. 97 Nw. U. L. Rev. 135-188 (2002).10.Pritchard, A.C. Justice Lewis F. Powell, Jr., and the counterrevolution in the federal securities laws. 52 Duke L.J. 841-949 (2003).11.Thompson, Robert B. and Hillary A. Sale. Securities fraud as corporate governance: Reflections upon federalism. 56 V and. L. Rev. 859-910 (2003).2002年：1.Allen, William T., Jack B. Jacobs and Leo E. Strine, Jr. Function over Form: A Reassessment of Standards of Review in Delaware Corporation Law. 26 Del. J. Corp. L. 859-895 (2001) and 56 Bus. Law. 1287 (2001).2.A yres, Ian and Joe Bankman. Substitutes for Insider Trading. 54 Stan. L. Rev. 235-254 (2001).3.Bebchuk, Lucian Arye, Jesse M. Fried and David I. Walker. Managerial Power and Rent Extraction in the Design of Executive Compensation. 69 U. Chi. L. Rev. 751-846 (2002).4.Bebchuk, Lucian Arye, John C. Coates IV and Guhan Subramanian. The Powerful Antitakeover Force of Staggered Boards: Theory, Evidence, and Policy. 54 Stan. L. Rev. 887-951 (2002).5.Black, Bernard and Reinier Kraakman. Delaware’s Takeover Law: The Uncertain Search for Hidden V alue. 96 Nw. U. L. Rev. 521-566 (2002).6.Bratton, William M. Enron and the Dark Side of Shareholder V alue. 76 Tul. L. Rev. 1275-1361 (2002).7.Coates, John C. IV. Explaining V ariation in Takeover Defenses: Blame the Lawyers. 89 Cal. L. Rev. 1301-1421 (2001).8.Kahan, Marcel and Edward B. Rock. How I Learned to Stop Worrying and Love the Pill: Adaptive Responses to Takeover Law. 69 U. Chi. L. Rev. 871-915 (2002).9.Kahan, Marcel. Rethinking Corporate Bonds: The Trade-off Between Individual and Collective Rights. 77 N.Y.U. L. Rev. 1040-1089 (2002).10.Roe, Mark J. Corporate Law’s Limits. 31 J. Legal Stud. 233-271 (2002).11.Thompson, Robert B. and D. Gordon Smith. Toward a New Theory of the Shareholder Role: "Sacred Space" in Corporate Takeovers. 80 Tex. L. Rev. 261-326 (2001).2001年：1.Black, Bernard S. The legal and institutional preconditions for strong securities markets. 48 UCLA L. Rev. 781-855 (2001).2.Coates, John C. IV. Takeover defenses in the shadow of the pill: a critique of the scientific evidence. 79 Tex. L. Rev. 271-382 (2000).3.Coates, John C. IV and Guhan Subramanian. A buy-side model of M&A lockups: theory and evidence. 53 Stan. L. Rev. 307-396 (2000).4.Coffee, John C., Jr. The rise of dispersed ownership: the roles of law and the state in the separation of ownership and control. 111 Y ale L.J. 1-82 (2001).5.Choi, Stephen J. The unfounded fear of Regulation S: empirical evidence on offshore securities offerings. 50 Duke L.J. 663-751 (2000).6.Daines, Robert and Michael Klausner. Do IPO charters maximize firm value? Antitakeover protection in IPOs. 17 J.L. Econ. & Org. 83-120 (2001).7.Hansmann, Henry and Reinier Kraakman. The essential role of organizational law. 110 Y ale L.J. 387-440 (2000).ngevoort, Donald C. The human nature of corporate boards: law, norms, and the unintended consequences of independence and accountability. 89 Geo. L.J. 797-832 (2001).9.Mahoney, Paul G. The political economy of the Securities Act of 1933. 30 J. Legal Stud. 1-31 (2001).10.Roe, Mark J. Political preconditions to separating ownership from corporate control. 53 Stan. L. Rev. 539-606 (2000).11.Romano, Roberta. Less is more: making institutional investor activism a valuable mechanism of corporate governance. 18 Y ale J. on Reg. 174-251 (2001).2000年：1.Bratton, William W. and Joseph A. McCahery. Comparative Corporate Governance and the Theory of the Firm: The Case Against Global Cross Reference. 38 Colum. J. Transnat’l L. 213-297 (1999).2.Coates, John C. IV. Empirical Evidence on Structural Takeover Defenses: Where Do We Stand?54 U. Miami L. Rev. 783-797 (2000).3.Coffee, John C., Jr. Privatization and Corporate Governance: The Lessons from Securities Market Failure. 25 J. Corp. L. 1-39 (1999).4.Fisch, Jill E. The Peculiar Role of the Delaware Courts in the Competition for Corporate Charters. 68 U. Cin. L. Rev. 1061-1100 (2000).5.Fox, Merritt B. Retained Mandatory Securities Disclosure: Why Issuer Choice Is Not Investor Empowerment. 85 V a. L. Rev. 1335-1419 (1999).6.Fried, Jesse M. Insider Signaling and Insider Trading with Repurchase Tender Offers. 67 U. Chi. L. Rev. 421-477 (2000).7.Gulati, G. Mitu, William A. Klein and Eric M. Zolt. Connected Contracts. 47 UCLA L. Rev. 887-948 (2000).8.Hu, Henry T.C. Faith and Magic: Investor Beliefs and Government Neutrality. 78 Tex. L. Rev. 777-884 (2000).9.Moll, Douglas K. Shareholder Oppression in Close Corporations: The Unanswered Question of Perspective. 53 V and. L. Rev. 749-827 (2000).10.Schizer, David M. Executives and Hedging: The Fragile Legal Foundation of Incentive Compatibility. 100 Colum. L. Rev. 440-504 (2000).11.Smith, Thomas A. The Efficient Norm for Corporate Law: A Neotraditional Interpretation of Fiduciary Duty. 98 Mich. L. Rev. 214-268 (1999).12.Thomas, Randall S. and Kenneth J. Martin. The Determinants of Shareholder V oting on Stock Option Plans. 35 Wake Forest L. Rev. 31-81 (2000).13.Thompson, Robert B. Preemption and Federalism in Corporate Governance: Protecting Shareholder Rights to V ote, Sell, and Sue. 62 Law & Contemp. Probs. 215-242 (1999).1999年（以第一作者姓名音序为序）：1.Bankman, Joseph and Ronald J. Gilson. Why Start-ups? 51 Stan. L. Rev. 289-308 (1999).2.Bhagat, Sanjai and Bernard Black. The Uncertain Relationship Between Board Composition and Firm Performance. 54 Bus. Law. 921-963 (1999).3.Blair, Margaret M. and Lynn A. Stout. A Team Production Theory of Corporate Law. 85 V a. L. Rev. 247-328 (1999).4.Coates, John C., IV. “Fair V alue” As an A voidable Rule of Corporate Law: Minority Discounts in Conflict Transactions. 147 U. Pa. L. Rev. 1251-1359 (1999).5.Coffee, John C., Jr. The Future as History: The Prospects for Global Convergence in Corporate Governance and Its Implications. 93 Nw. U. L. Rev. 641-707 (1999).6.Eisenberg, Melvin A. Corporate Law and Social Norms. 99 Colum. L. Rev. 1253-1292 (1999).7.Hamermesh, Lawrence A. Corporate Democracy and Stockholder-Adopted By-laws: Taking Back the Street? 73 Tul. L. Rev. 409-495 (1998).8.Krawiec, Kimberly D. Derivatives, Corporate Hedging, and Shareholder Wealth: Modigliani-Miller Forty Y ears Later. 1998 U. Ill. L. Rev. 1039-1104.ngevoort, Donald C. Rereading Cady, Roberts: The Ideology and Practice of Insider Trading Regulation. 99 Colum. L. Rev. 1319-1343 (1999).ngevoort, Donald C. Half-Truths: Protecting Mistaken Inferences By Investors and Others.52 Stan. L. Rev. 87-125 (1999).11.Talley, Eric. Turning Servile Opportunities to Gold: A Strategic Analysis of the Corporate Opportunities Doctrine. 108 Y ale L.J. 277-375 (1998).12.Williams, Cynthia A. The Securities and Exchange Commission and Corporate Social Transparency. 112 Harv. L. Rev. 1197-1311 (1999).1998年：1.Carney, William J., The Production of Corporate Law, 71 S. Cal. L. Rev. 715-780 (1998).2.Choi, Stephen, Market Lessons for Gatekeepers, 92 Nw. U. L. Rev. 916-966 (1998).3.Coffee, John C., Jr., Brave New World?: The Impact(s) of the Internet on Modern Securities Regulation. 52 Bus. Law. 1195-1233 (1997).ngevoort, Donald C., Organized Illusions: A Behavioral Theory of Why Corporations Mislead Stock Market Investors (and Cause Other Social Harms). 146 U. Pa. L. Rev. 101-172 (1997).ngevoort, Donald C., The Epistemology of Corporate-Securities Lawyering: Beliefs, Biases and Organizational Behavior. 63 Brook. L. Rev. 629-676 (1997).6.Mann, Ronald J. The Role of Secured Credit in Small-Business Lending. 86 Geo. L.J. 1-44 (1997).haupt, Curtis J., Property Rights in Firms. 84 V a. L. Rev. 1145-1194 (1998).8.Rock, Edward B., Saints and Sinners: How Does Delaware Corporate Law Work? 44 UCLA L. Rev. 1009-1107 (1997).9.Romano, Roberta, Empowering Investors: A Market Approach to Securities Regulation. 107 Y ale L.J. 2359-2430 (1998).10.Schwab, Stewart J. and Randall S. Thomas, Realigning Corporate Governance: Shareholder Activism by Labor Unions. 96 Mich. L. Rev. 1018-1094 (1998).11.Skeel, David A., Jr., An Evolutionary Theory of Corporate Law and Corporate Bankruptcy. 51 V and. L. Rev. 1325-1398 (1998).12.Thomas, Randall S. and Martin, Kenneth J., Should Labor Be Allowed to Make Shareholder Proposals? 73 Wash. L. Rev. 41-80 (1998).1997年：1.Alexander, Janet Cooper, Rethinking Damages in Securities Class Actions, 48 Stan. L. Rev. 1487-1537 (1996).2.Arlen, Jennifer and Kraakman, Reinier, Controlling Corporate Misconduct: An Analysis of Corporate Liability Regimes, 72 N.Y.U. L. Rev. 687-779 (1997).3.Brudney, Victor, Contract and Fiduciary Duty in Corporate Law, 38 B.C. L. Rev. 595-665 (1997).4.Carney, William J., The Political Economy of Competition for Corporate Charters, 26 J. Legal Stud. 303-329 (1997).5.Choi, Stephen J., Company Registration: Toward a Status-Based Antifraud Regime, 64 U. Chi. L. Rev. 567-651 (1997).6.Fox, Merritt B., Securities Disclosure in a Globalizing Market: Who Should Regulate Whom. 95 Mich. L. Rev. 2498-2632 (1997).7.Kahan, Marcel and Klausner, Michael, Lockups and the Market for Corporate Control, 48 Stan. L. Rev. 1539-1571 (1996).8.Mahoney, Paul G., The Exchange as Regulator, 83 V a. L. Rev. 1453-1500 (1997).haupt, Curtis J., The Market for Innovation in the United States and Japan: V enture Capital and the Comparative Corporate Governance Debate, 91 Nw. U.L. Rev. 865-898 (1997).10.Skeel, David A., Jr., The Unanimity Norm in Delaware Corporate Law, 83 V a. L. Rev. 127-175 (1997).1996年：1.Black, Bernard and Reinier Kraakman A Self-Enforcing Model of Corporate Law, 109 Harv. L. Rev. 1911 (1996)2.Gilson, Ronald J. Corporate Governance and Economic Efficiency: When Do Institutions Matter?, 74 Wash. U. L.Q. 327 (1996)3. Hu, Henry T.C. Hedging Expectations: "Derivative Reality" and the Law and Finance of the Corporate Objective, 21 J. Corp. L. 3 (1995)4.Kahan, Marcel & Michael Klausner Path Dependence in Corporate Contracting: Increasing Returns, Herd Behavior and Cognitive Biases, 74 Wash. U. L.Q. 347 (1996)5.Kitch, Edmund W. The Theory and Practice of Securities Disclosure, 61 Brooklyn L. Rev. 763 (1995)ngevoort, Donald C. Selling Hope, Selling Risk: Some Lessons for Law From Behavioral Economics About Stockbrokers and Sophisticated Customers, 84 Cal. L. Rev. 627 (1996)7.Lin, Laura The Effectiveness of Outside Directors as a Corporate Governance Mechanism: Theories and Evidence, 90 Nw. U.L. Rev. 898 (1996)lstein, Ira M. The Professional Board, 50 Bus. Law 1427 (1995)9.Thompson, Robert B. Exit, Liquidity, and Majority Rule: Appraisal's Role in Corporate Law, 84 Geo. L.J. 1 (1995)10.Triantis, George G. and Daniels, Ronald J. The Role of Debt in Interactive Corporate Governance. 83 Cal. L. Rev. 1073 (1995)1995年：公司法：1.Arlen, Jennifer and Deborah M. Weiss A Political Theory of Corporate Taxation,. 105 Y ale L.J. 325-391 (1995).2.Elson, Charles M. The Duty of Care, Compensation, and Stock Ownership, 63 U. Cin. L. Rev. 649 (1995).3.Hu, Henry T.C. Heeding Expectations: "Derivative Reality" and the Law and Finance of the Corporate Objective, 73 Tex. L. Rev. 985-1040 (1995).4.Kahan, Marcel The Qualified Case Against Mandatory Terms in Bonds, 89 Nw. U.L. Rev. 565-622 (1995).5.Klausner, Michael Corporations, Corporate Law, and Networks of Contracts, 81 V a. L. Rev. 757-852 (1995).6.Mitchell, Lawrence E. Cooperation and Constraint in the Modern Corporation: An Inquiry Into the Causes of Corporate Immorality, 73 Tex. L. Rev. 477-537 (1995).7.Siegel, Mary Back to the Future: Appraisal Rights in the Twenty-First Century, 32 Harv. J. on Legis. 79-143 (1995).证券法：1.Grundfest, Joseph A. Why Disimply? 108 Harv. L. Rev. 727-747 (1995).2.Lev, Baruch and Meiring de V illiers Stock Price Crashes and 10b-5 Damages: A Legal Economic, and Policy Analysis, 47 Stan. L. Rev. 7-37 (1994).3.Mahoney, Paul G. Mandatory Disclosure as a Solution to Agency Problems, 62 U. Chi. L. Rev. 1047-1112 (1995).4.Seligman, Joel The Merits Do Matter, 108 Harv. L. Rev. 438 (1994).5.Seligman, Joel The Obsolescence of Wall Street: A Contextual Approach to the Evolving Structure of Federal Securities Regulation, 93 Mich. L. Rev. 649-702 (1995).6.Stout, Lynn A. Are Stock Markets Costly Casinos? Disagreement, Mark Failure, and Securities Regulation, 81 V a. L. Rev. 611 (1995).7.Weiss, Elliott J. and John S. Beckerman Let the Money Do the Monitoring: How Institutional Investors Can Reduce Agency Costs in Securities Class Actions, 104 Y ale L.J. 2053-2127 (1995).1994年：公司法：1.Fraidin, Stephen and Hanson, Jon D. Toward Unlocking Lockups, 103 Y ale L.J. 1739-1834 (1994)2.Gordon, Jeffrey N. 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海外文献原文-推荐参考文献列表

海外文献推荐-第一期参考文献：[1] I-Cheng Yeh, Che-Hui Lien, Tao-Ming Ting, 2015, Building multi-factor stock selection models using balanced split regression trees with sorting normalisation and hybrid variables, Foresight and Innovation Policy, V ol. 10, No. 1, 48-74[2] Eugene F.Fama, KennethR.French, 2015, A Five-factor Asset Pricing Model, Journal of Financial Economics 116, 1-22[3] Achim BACKHAUS, Aliya ZHAKANOV A ISIKSAL, 2016, The Impact of Momentum Factors on Multi Asset Portfolio, Romanian Journal of Economic Forecasting XIX (4), 146-169[4] Francisco Barillas, Jay Shanken, 2016, Which Alpha? Review of Financial Studies海外文献推荐-第二期参考文献：[1] PRA VEEN KUMAR, DONGMEI LI, 2016, Capital Investment, Innovative Capacity, and Stock Returns, The Journal of Finance, VOL. LXXI, NO. 5, 2059-2094[2] Houda Ben Mabrouk, Abdelfettah Bouri, 2013, New insight on the CAPM: a copula-based approach Tunisian and international evidence, Accounting and Finance, Vol. 4, No. 1, 35-62 [3] FERHAT AKBAS, 2016, The Calm before the Storm, The Journal of Finance, VOL. LXXI, NO. 1,225-266海外文献推荐-第三期参考文献：[1] Yufeng Han, Guofu Zhou, Yingzi Zhu, 2016, A trend factor: Any economic gains from using information over investment horizons? Journal of Financial Economics 122, 352-375[2] Andrea Frazzini, LasseHeje Pedersen, 2014, Betting against beta, Journal of Financial Economics 111, 1-25[3] Doron Avramov, Si Cheng, and Allaudeen Hameed, 2016, Time-Varying Liquidity and Momentum Profits, JOURNAL OF FINANCIAL AND QUANTITATIVE ANAL YSI, Vol. 51, No. 6, 1897-1923[4] Nicholas Barberis, Abhiroop Mukherjee, Baolian Wang, 2014, Prospect Theory and Stock Returns: An Empirical Test, Review of Financial Studies海外文献推荐-第四期参考文献：[1] Brad M. Barber, Xing Huang, Terrance Odean, 2014, Which risk factors matter to investors? Evidence from mutual fund flows, Review of Financial Studies[2] MICHAEL J. COOPER, HUSEYIN GULEN, & MICHAEL J. SCHILL. (2008). Asset growth and the cross‐section of stock returns. Social Science Electronic Publishing, 63(4), 1609–1651.[3]Bollerslev, T., Li, S. Z., & Todorov, V. (2016). Roughing up beta: continuous versus discontinuous betas and the cross section of expected stock returns. Journal of Financial Economics, 120(3), 464-490.[4]Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment ⋆. Journal of Financial Economics, 104(2), 272-287.海外文献推荐-第五期参考文献：[1] Nicole Choi, Mark Fedenia, Tatyana Sokolyk, 2017, Portfolio Concentration and Performance of Institutional Investors Worldwide, Journal of Financial Economics[2]Cronqvist, H., Siegel, S., & Yu, F. (2015). Value versus growth investing: why do differentinvestors have different styles? ☆. Journal of Financial Economics, 117(2), 333-349.[3]Rapach, D. E., Ringgenberg, M. C., & Zhou, G. (2016). Short interest and aggregate stock returns . Journal of Financial Economics, 121(1), 46-65.[4]Novy-Marx, R. (2013). The other side of value: the gross profitability premium ☆. Journal of Financial Economics, 108(1), 1-28.海外文献推荐-第六期参考文献：[1] Suk Joon Byun, Sonya S. Limy, and Sang Hyun Yun, 2012, Continuing Overreaction and Stock Return Predictability, Journal of Financial and Quantitative Analysis[2]Eugene F. Fama, & Kenneth R. French. (2016). International tests of a five-factor asset pricing model. Journal of Financial Economics, 123.[3]Keloharju, M., Linnainmaa, J. T., & Nyberg, P. (2016). Return seasonalities. Journal of Finance, 71(4), n/a-n/a.[4]Seasholes, M. S., & Wu, G. (2007). Predictable behavior, profits, and attention. Journal of Empirical Finance, 14(5), 590-610.[5]PA VEL SAVOR, & MUNGO WILSON. (2016). Earnings announcements and systematic risk. The Journal of Finance, 71(1).海外文献推荐-第七期参考文献：[1] Cary Frydman and Colin Camerer, 2016, Neural Evidence of Regret and its Implications for Investor Behavior, Review of Financial Studies 29, 3108-3139[2] Haghani, V., & Dewey, R. (2016). A case study for using value and momentum at the asset class level. Journal of Portfolio Management, 42(3), 101-113.[3] Tarun, C., Amit, G., & Narasimhan, J. (2011). Buyers versus sellers: who initiates trades, and when?. Journal of Financial & Quantitative Analysis, 51(5), 1467-1490.[4] Hartzmark, M. S. (2015). The worst, the best, ignoring all the rest: the rank effect and trading behavior. Review of Financial Studies, 28(4), 1024.[5] Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122(2), 221-247.海外文献推荐-第八期参考文献：[1]Hua, R., Kantsyrev, D., & Qian, E. (2012). Factor-timing model.Journal of Portfolio Management,39(1), 75-87.[2]Leshem, R., Goldberg, L. R., & Cummings, A. (2015). 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Journal of Finance, 71(1).[3]Chow, V., & Lai, C. W. (2015). Conditional sharpe ratios. Finance Research Letters, 12, 117-133.海外文献推荐-第十一期参考文献：[1] Mladina, P. (2017). Illuminating hedge fund returns to improve portfolio construction. Social Science Electronic Publishing, 41(3), 127-139.[2] Choi, N., Fedenia, M., Skiba, H., & Sokolyk, T. (2016). Portfolio concentration and performance of institutional investors worldwide. Journal of Financial Economics.[3] Martijn Boons, 2016, State variables, macroeconomic activity, and the cross section of individual stocks, Journal of Financial Economics 119, 489-511海外文献推荐-第十二期参考文献：[1] Blanchett, D., & Ratner, H. (2015). Building efficient income portfolios. Journal of Portfolio Management, 41(3), 117-125.[2] Özde Öztekin. (2015). Capital structure decisions around the world: which factors are reliably important?. Journal of Financial & Quantitative Analysis, 50(3).[3] 2015, Does the number of stocks in a portfolio influence performance? 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USXX.海外文献推荐-第十九期参考文献：[1]Copeland, M., & Copeland, T. (2016). Vix versus size. Journal of Portfolio Management, 42(3), 76-83.[2]Kritzman, M., & Turkington, D. (2016). Stability-adjusted portfolios. Journal of Portfolio Management, 42(5), 113-122.海外文献推荐-第二十期参考文献：[1]Benos, E., Brugler, J., Hjalmarsson, E., & Zikes, F. (2016). Interactions among high-frequency traders. Journal of Financial & Quantitative Analysis, 52, 1-28.[2]Richardson, S., Sloan, R., & You, H. (2011). What makes stock prices move? fundamentals vs. investor recognition. Financial Analysts Journal, 68(2), 30-50.海外文献推荐-第二十一期参考文献：[1]Bogousslavsky, V. (2016). Infrequent rebalancing, return autocorrelation, and seasonality. Journal of Finance, 71(6), 2967-3006.[2]Marcos, L. D. P. (2015). The future of empirical finance. Journal of Portfolio Management, 41(4), 140-144.海外文献推荐-第二十二期参考文献：[1] Fabian, H., & Marcel, P. (2016). Estimating beta. Journal of Financial & Quantitative Analysis, 51(4), 1437-1466.[2] Christopher Cheung, George Hoguet, & Sunny Ng. (2014). Value, size, momentum, dividend yield, and volatility in china’s a-share market. Journal of Portfolio Management, 41(5), 57-70.海外文献推荐-第二十三期参考文献：[1]Mclean, R. D., & Zhao, M. (2014). The business cycle, investor sentiment, and costly external finance.Journal of Finance, 69(3), 1377–1409.[2]Kaniel, R., & Parham, R. (2017). The impact of media attention on consumer and mutual fund investment decisions. Journal of Financial Economics, 123, págs. 337-356海外文献推荐-第二十四期参考文献：[1]Chang, X., Chen, Y., & Zolotoy, L. (2017). Stock liquidity and stock price crash risk. Journal of Financial & Quantitative Analysis.[2]Bisetti, E., Favero, C. A., Nocera, G., & Tebaldi, C. (2013). A multivariate model of strategic asset allocation with longevity risk. Ssrn Electronic Journal.海外文献推荐-第二十五期参考文献：[1] Lou, X., & Shu, T. (2013). 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Social Science Electronic Publishing.[2] Moreira, A., and Muir, T. (2017). Volatility‐Managed Portfolios. Journal of Finance, 72(4).海外文献推荐-第二十九期参考文献：[1]Wahalab S. Style investing, comovement and return predictability ☆[J]. Journal of Financial Economics, 2013, 107(1).[2]Pástor Ľ, Stambaugh R F, Taylor L A. Do funds make more when they trade more?[J]. The Journal of Finance, 2017, 72(4): 1483-1528.海外文献推荐-第三十期参考文献：[1] K Hou, C Xue, L Zhang, Digesting Anomalies: An Investment Approach, NBER Working Papers, 2015, 28(3)[2]Berk, J. B., & Binsbergen, J. H. V. (2013). Measuring skill in the mutual fund industry. Journal of Financial Economics, 118(1), 1-20.海外文献推荐-第三十一期参考文献：[1]Klein, Rudolf F. and V. K. Chow. "Orthogonalized factors and systematic risk decomposition." Quarterly Review of Economics & Finance 53.2(2013):175-187.[2]Sorensen E H, Hua R, Qian E E. Contextual Fundamentals, Models, and Active Management[J]. Journal of Portfolio Management 32.1(2005):23-36.海外文献推荐-第三十二期参考文献：[1] Hong, H. Torous, W. & Valkanov, R. (2007). Do industries lead stock markets? Journal of Financial Economics,83 (2), 367-396.[2]Dhillon, J. Ilmanen, A. & Liew, J. (2016). Balancing on the life cycle: target-date funds need better diversification. Journal of Portfolio Management, 42(4), 12-27.海外文献推荐-第三十三期参考文献：[1]Kenneth Froot and Melvyn Teo, Style Investing and Institutional Investors, JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS V ol. 43, No. 4, Dec. 2008, pp. 883–906.[2]Israel R, Palhares D, Richardson S A. Common factors in corporate bond returns[J]. Social Science Electronic Publishing, 2015.海外文献推荐-第三十四期参考文献：[1] DM Smith, N Wang, Y Wang, EJ Zychowicz, Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry，Journal of Financial & Quantitative Analysis, 2016 , 51 (6) :1991-2013[2]Ronen Israel, Sarah Jiang, and Adrienne Ross (2018). 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The Journal of Derivatives Spring 2018, 25 (3) 7-32海外文献推荐-第四十二期参考文献：[1] Robert Capone, Adam Akant, (2016), Trend Following Strategies in Target-Date Funds, AQR Capital Management.[2] Loh, R. K., & Stulz, R. M. (2018). Is sell‐side research more valuable in bad times?. Journal of Finance, 73(3): 959-1013.海外文献推荐-第四十三期参考文献：[1] Asness, C. S., Frazzini, A., Israel, R., & Moskowitz, T. J. (2015). Fact, fiction, and value investing. Final version published in Journal of Portfolio Management, V ol. 42, No.1[2] Gu, S., Kelly, B. T., & Xiu, D. (2018). Empirical asset pricing via machine learning. Social Science Electronic Publishing.海外文献推荐-第四十四期参考文献：[1] David P. Morton, Elmira Popova, Ivilina Popova, Journal of Banking & Finance 30 (2006) 503–518海外文献推荐-第四十五期参考文献：[1] Lleo, S., & Ziemba, W. T. (2017). A tale of two indexes: predicting equity market downturns in china. Social Science Electronic Publishing海外文献推荐-第四十六期参考文献：[1] Alquist, R., Israel, R., & Moskowitz, T. J. (2018). 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Mutual fund performance evaluation with active peer benchmarks[J]. Journal of Financial economics, 2014, 112(1): 1-29.海外文献推荐-第七十四期参考文献：[1]Michael Stein and Svetlozar T. Rachev. Style Neutral Funds of Funds: Diversification or Deadweight? [J]. Journal of Asset Management, February 2011, V olume 11, Issue 6, pp 417–434海外文献推荐-第七十五期参考文献：[1] Elisabeth Kashner, 2019.01.31, Bogle led this investing Fee War, ;[2] Cinthia Murphy,2017,03.31, how to launch a successful ETF, ;[3] Drew V oros, 2019.01.23, how a small ETF Issuer Competes, ；[4] 2019.01.04, Invesco focusing on scale,海外文献推荐-第七十六期参考文献：[1] Shpak I , Human B , Nardon A . Idiosyncratic momentum in commodity futures[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十六期参考文献：[1] Ehsani S , Linnainmaa J T . Factor Momentum and the Momentum Factor[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十七期参考文献：[1] Iuliia Shpak*, Ben Human and Andrea Nardon. 2017.09.11, Idiosyncratic momentum in commodity futures. ResearchGate海外文献推荐-第七十八期参考文献：[1] Joel Hasbrouck. High-Frequency Quoting: Short-Term V olatility in Bids and Offers. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS海外文献推荐-第七十九期参考文献：[1] Tarun Gupta and Bryan Kelly. Factor Momentum Everywhere. Institutional Investor Journals海外文献推荐-第八十期参考文献：[1] MICHAEL A. BABYAK , P H D. What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models. S T A T I S T I C A L C O R N E R海外文献推荐-第八十一期参考文献：[1] Eric Jondeau , Qunzi Zhang , Xiaoneng Zhu. Average Skewness Matters.海外文献推荐-第八十二期参考文献：[1] JOHN A. HASLEM. Morningstar Mutual Fund Measures and Selection Model. THE JOURNAL OF WEALTH MANAGEMENT海外文献推荐-第八十三期参考文献：[1] EUGENE F. FAMA and KENNETH R. FRENCH. Luck versus Skill in the Cross-Section of Mutual Fund Returns. THE JOURNAL OF FINANCE海外文献推荐-第八十四期参考文献：[1] How Transparent Are ETFs?[2] Lara Crigger. Nontransparent Active: Next ETF Revolution?.海外文献推荐-第八十五期参考文献：[1] Olivier Rousse and Benoît Sévi. 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营运资金管理外文文献翻译

文献出处:Enqvist, Julius, Michael Graham, and Jussi Nikkinen. "The impact of working capital management on firm profitability in different business cycles: evidence from Finland." Research in International Business and Finance 32 (2014): 36-49.原文The impact of working capital management on firm profitability in different business cycles: Evidence from Finland1. IntroductionThis paper investigates the effect of the business cycle on the link between working capital, the difference between current assets and current liabilities, and corporate performance. Efficient working capital management is recognized as an important aspect of financial management practices in all organizational forms. In acknowledgement of this importance, the CFO Magazine publishes an annual study of corporate working capital management performance in many countries. The extensive literature indicates that it impacts directly on corporate liquidity ( Kim et al., 1998 and Opler et al., 1999), profitability (e.g., Shin and Soenen, 1998, Deloof, 2003, Lazaridis and Tryfonidis, 2006 and Ukaegbu, 2014), and solvency (e.g.,Berryman, 1983 and Peel and Wilson, 1994).It is reasonable to assume that economy-wide fluctuations exogenous to the operations of the firm play an important role in the demand for firms’ products and any financing decision. Korajczyk and Levy (2003), for instance, suggest that firms time debt issuance based on economic conditions. Also, given that retained earnings are a significant component of working capital, business cycles can be said to affect all enterprises financing source through its effect on economic growth and sales. For example, when company sales weaken it engenders earning declines, thereby, affecting an important source of working capital. The recent global economic downturn with crimping consumer demand is an excellent example of this. The crisis,characterized by plummeting sales, put a squeeze on corporate revenues and profit margins, and subsequently, working capital requirements. This has brought renewed focus on working capital management at companies all over the world.The literature on working capital, however, only includes a handful of studies examining the impact of the business cycle on working capital. An early study by Merville and Tavis (1973) examined the relationship between firm working capital policies and business cycle. More recent studies have investigated the degree to which firms’ reliance on bank borrowing to finance working capital is cyclical (Einarsson and Marquis, 2001), the significance of firms’ external dependence for financing needs on the link between industry growth and business the cycle in the short term (Braun and Larrain, 2005), and the influence of business indicators on the determinants of working capital management (Chiou et al., 2006). These studies have independently linked working capital to corporate profitability and the business cycle. No study, to the best of our knowledge, has examined the simultaneous working capital–profitability and business cycle effects. There is therefore a substantial gap in the literature which this paper seeks to fill. Firms may have an optimal level of working capital that maximizes their value. However, optimal levels may change to reflect business conditions. Consequently, we contribute to the literature by re-examining the relationship between working capital management and corporate profitability by investigating the role business cycle plays in this relationship.We investigate this important relationship using a sample of firms listed on the Helsinki Stock Exchange and an extended study period of 18 years, between 1990 and 2008. Finnish firms tend to react strongly to changes in the business cycle, a characteristic that can be observed from the volatility of the Nasdaq OMX Helsinki stock index. The index usually declines quickly in poor economic states, but also makes fast recoveries. Finland, therefore, presents an excellent representative example of how the working capital–profitability relationship may change in different economic states. The choice of Finland is also significant as it also offers a representative Nordic perspective of this important working capital–profitability relationship. Hitherto no academic study has examined the workingcapital–profitability relationship in the Nordic region, to the best of our knowledge. Surveys on working capital management in the Nordic region carried out by Danske Bank and Ernst & Young in 2009 show, however, that many companies rated their working capital management performance as average, with a growing focus on optimizing working capital in the future. The surveys are, however, silent on how this average performance affected profitability. This gives further impetus for our study.Our results point to a number of interesting findings. First, we find that firms can enhance their profitability by increasing working capital efficiency. This is a significant result because many Nordic firms find it hard to turn good policy intentions on working capital management into reality (Ernst and Young, 2009). Economically, firms may gain by paying increasing attention to efficient working capital practices. Our empirical finding, therefore, should motivate firms to implement new work processes as a matter of necessity. We also found that working capital management is relatively more important in low economic states than in the economic boom state, implying working capital management should be included in firms’ financial planning. This finding corroborates evidence from the survey results in the Nordic region. Specifically, the survey results by Ernst and Young (2009) indicate that the largest potential for improvement in working capital could be found within the optimization of internal processes. This suggests that this area is not prioritized in times of business growth which is typical of the general economic expansion periods and is exposed in economic downturns.The remainder of this paper is organized as follows: Section 2 presents a brief review of the literature presents the hypotheses for empirical testing. Sections 3 and 4 discuss data and models to be estimated. The empirical results are presented in Section 5 and Section 6 concludes.2. Related literature and hypotheses2.1. Literature reviewMany firms have invested significant amounts in working capital and a number of studies have examined the determinants of this investment. For example Kim et al. (1998) and Opler et al. (1999), Chiou et al. (2006) and D’Mello et al. (2008) find thatthe availability of external financing is a determinant of liquidity. Thus restricted access to capital markets requires firms to hold larger cash reserves. Other studies show that firms with weaker corporate governance structures hold smaller cash reserves (Harford et al., 2008). Furthermore firms with excess cash holding as well as weak shareholder rights undertake more acquisitions. However there is a higher likelihood of value-decreasing acquisitions (Harford, 1999). Kieschnick and Laplante (2012) provide evidence linking working capital management to shareholder wealth. They find that the incremental dollar invested in net operating capital is less valuable than the incremental dollar held in cash for the average firm. The findings reported in the paper further suggest that the valuation of the incremental dollar invested in net operating working is significantly influenced by a firm's future sales expectations, its debt load, its financial constraints, and its bankruptcy risk. Further the value of the incremental dollar extended in credit to one's customers has a greater effect on shareholder wealth than the incremental dollar invested in inventories for the average firm. Taken together the results indicate the significance of working capital management to the firm's residual claimants, and how financing impacts these effects.A thin thread of the literature links business cycles to working capital. In a theoretical model, Merville and Tavis (1973) posit that investment and financing decisions relating to working capital should be made in chorus as components of each impact on the optimal policies of the others. The optimal working capital policy of the firm is, therefore, made within a systems context, components of which are related spatially over time in a chance-constrained format. Uncertainty in the wider business environment directly affects the system. For example, short run demand fluctuations disrupt anticipated incoming cash flows, and the collection of receivables faces increased uncertainty. The model provides a structure enabling corporate managers to solve complex inventory and credit policies for short term financial planning.In an empirical study, Einarsson and Marquis (2001) find that the degree to which companies rely on bank financing to cover their working capital requirements in the U.S. is countercyclical; it increases as the state of the economy weakens. Furthermore, Braun and Larrain (2005) find that high working capital requirementsar e a key determinant of a business’ dependence on external financing. They show that firms that are highly dependent on external financing are more affected by recessions, and should take more precautions in preparing for declines in the economic environment, including ensuring a secure level of working capital reserves during times of crisis. Additionally, Chiou et al. (2006) recognize the importance of the state of the economy and includes business indicators in their study of working capital determinants. They find a positive relationship between business indicator and working capital requirements.The relationship between profitability and working capital management in various markets has also attracted intense interest. In a comprehensive study, Shin and Soenen (1998) document a strong inverse relationship between working capital efficiency and profitability across U.S. industries. This inverse relationship is supported by Deloof (2003), Lazaridis and Tryfonidis (2006), and Garcia-Teruel and Martinez-Solano (2007)for Belgian non-financial firms, Greek listed firms, and Spanish small and medium size enterprises (SME), respectively. There are, however, significant divergences in the results relating to the effect of the various components of working capital on profitability. For example, whereas Deloof (2003) find a negative and statistically significant relationship between account payable and profitability, Garcia-Teruel and Martinez-Solano (2007) find no such measurable influences in a sample of Spanish SMEs.2.2. Hypotheses developmentThe cash conversion cycle (CCC), a useful and comprehensive measure of working capital management, has been widely used in the literature (see for example Deloof, 2003 and Gill et al., 2010). The CCC, measured in days, is the length of time between a company's expenditure for the procurement of raw materials and the collection of sales of finished goods. We adopt this as our measure of working capital management in this study. Previous studies have established a link between profitability and the CCC in different countries and market segments.Efficient working capital management practices aims to shorten the CCC to optimize to levels that best suites the requirements of the specific company (Hager,1976). A short CCC indicates quick collection of receivables and delays in payments to suppliers. This is associated with profitability given that it improves corporate efficiency in its use of working capital. Deloof (2003), however, posits that low inventory levels, tight trade credit policies and utilizing obtained trade credit as a means of financing can increase risks of inventory stock-outs, decrease sales stimulants and increase accounts payable costs by forgoing given cash discounts. Managers must, therefore, always consider the tradeoff between liquidity and profitability when managing working capital. A faster rise in the cost of higher investment in working capital relative to the benefits of holding more inventories and/or granting trade credit to customers may lead to decrease in corporate profitability. Deloof (2003), Wang (2002), Lazaridis and Tryfonidis (2006), and Gill et al. (2010) all propose a negative relationship between the cash conversion cycle and corporate profitability. Following this, we propose a general hypothesis stating the expected negative relationship between the cash conversion cycle and corporate profitability:6. ConclusionsWorking capital, the difference between current assets and current liabilities, is used to fund a business’ daily operations due to t he time lag between buying raw materials for production and receiving funds from the sale of the final product. With vast amounts invested in working capital, it can be expected that the management of these assets would significantly affect the profitability of a company. Consequently, companies strive to achieve optimize levels of working capital by paying bills as late as possible, turning over inventories quickly, and collecting on account receivables quickly. The optimal level, though, may vary to reflect business conditions. This study examines the role business cycle plays in the working capital-corporate profitability relationship using a sample of Finnish listed companies from years 1990 to 2008.We utilize the cash conversion cycle (CCC), defined as the length of time between a company's expenditure for the procurement of raw materials and the collection of sales of finished goods, as our measure of working capital. We further make use of 2 measures of profitability, return on assets and gross operating income.We document a negative relationship between cash conversion cycle and corporate profitability. Our results also show that companies can achieve higher profitability levels by managing inventories efficiently and lowering accounts receivable collection times. Furthermore shorter account payable cycles enhance corporate profitability. These results, which largely mirror findings from other countries, indicate effective management of firm's total working capital as well as its individual components has a significant effect on corporate profitability levels.Our results also show that economic conditions exhibit measurable influences on the working capital-profitability relationship. The low economic state is generally found to have negative effects on corporate profitability. In particular, we find that the impact of efficient working capital (CCC) on operational profitability increases in economic downturns. We also find that the impact of efficient inventory management and accounts receivables conversion periods, subsets of CCC, on profitability increase in economic downturns.Overall the results indicate that investing in working capital processes and incorporating working capital efficiency into everyday routines is essential for corporate profitability. As a result, firms should include working capital management in their financial planning processes. Additionally, firms generate income and employment. The reduced demand in economic downturns depletes working capital of firms and threatens their stability and, implicitly, their important function as generators of employment and income. National economic policy aimed at boosting cash flows of firms may increase business ability to finance working capital internally, especially during economic down turns.译文营运资金管理对不同商业周期公司盈利能力的影响：证据来自芬兰1.引言本文研究商业周期与营运资本两者之间的联系,流动资产和流动负债之间的区别,以及公司业绩问题。

导读文章写作格式及范例

股票价格….(题目12号字加黑居中)（空一行）原文作者：Richard G. Sloan 综述作者：张然(10.5号字居中)(空一行)（Sloan, R. 1996. Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings? The Accounting Review 71: 289-315.） (10.5号字居中，格式同参考文献)(空一行)说明：1、全篇中文选用宋体10.5号字，英文选用Times New Roman字体；2、A4纸型，文章不打印页码；3、均为1.2倍行距；4、页边距设为：上2.54cm，下2.54cm，左3.17 cm，右3.17 cm参考文献: (9号字)Every review paper must include a list of references containing only those works cited. Each entry should contain all data necessary for unambiguous identification. With the author-date system, use the following format:1. Arrange citations in alphabetical order according to surname of the first author or the name of the institution responsible for the citation.2. Use author’s initials instead of proper names.3. Date of publication should be placed immediately after author’s name.4. Titles of journals should not be abbreviated.5. Multiple works by the same author(s) in the same year are distinguished by letters after the date.Sample entries are as follows:American Accounting Association, Committee on Concepts and Standards for External Financial Reports. 1977. Statement on Accounting Theory and Theory Acceptance. Sarasota, FL: AAA.Demski, J. S., and D. E. M. Sappington. 1989. Hierarchical structure and responsibility accounting. Journal of Accounting Research 27: 40–58.Dye, R., B. Balachandran, and R. Magee. 1989. Contingent fees for audit firms. Working paper, Northwestern University, Evanston, IL.Fabozzi, F., and I. Pollack, eds. 1987. The Handbook of Fixed Income Securities. 2nd edition. Homewood, IL: Dow Jones-Irwin.Kahneman, D., P. Slovic, and A. Tversky, eds. 1982. Judgment Under Uncertainty: Heuristics and Biases. Cambridge, U.K.: Cambridge University Press.Porcano, T. M. 1984a. Distributive justice and tax policy. The Accounting Review 59: 619–636.————. 1984b. The perceived effects of tax policy on corporate investment intentions. The Journal of the American Taxation Association 6 (Fall): 7–19.Shaw, W. H. 1985. Empirical evidence on the market impact of the safe harbor leasing law. Ph.D. dissertation, The University of Texas at Austin.Sherman, T. M., ed. 1984. Conceptual Framework for Financial Accounting. Cambridge, MA: Harvard Business School.文献导读写作注意事项：1、每篇文章导读在2500字左右，比较复杂的文章可以长些2、导读的结构文章在本书体系中的位置及前后关系文章研究的核心问题文章的假设或预测文章选择的研究环境：数据、变量、数据年度研究方法研究结果评论：文章的贡献和不足；对未来研究的启示3、写作导读的过程中不必过于重视文章使用的理论模型和复杂的统计，计量方法。

实证论文经典(2)-Beaver(JAR增刊,1968)张琦20060904

The Information Content of Annual Earnings Announcemபைடு நூலகம்nts
WILLIAM H. BEAVER*
The infonnation content of earnings is an issue of obvious import^ance and is a focal point for many measurement controversies in accounting. This paper empirically examines the extent to which, common stock investors perceive earnings to posses infonnational value. The study directs its attention to investor reaction to earnings announcements, as refiected in the volume and price movements of common stocks in the weeks surrounding the announcement date. Valuation theory has long posited a relationship between earnings and the value of common stock. Miller and Modigliani postulate that one important element in determining the value of common stock is the product of earnings times the appropriate earnings multiplier for that risk class.^ Graham, Dodd, and Cottle take a similar position with respect to the computation of their "intrinsic value" of common stock securities.^ MM also provide empirical evidence that si^gests if reported earnings are adjusted for measiu'ement errors through the use of instrumental variables, the adjusted earnings are useful in the prediction of the market value of electric utility firms. In fact, the evidence indicated that the earnings term was the most important explanatory'- variable in the valuation equation.' 1'he relationship is a necessary condition for earnings to have information content,

财务分析第四版课后习题答案参考文献

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The Impact of Uncertainty Shocks

Econometrica,Vol.77,No.3(May,2009),623–685THE IMP ACT OF UNCERTAINTY SHOCKSB Y N ICHOLAS B LOOM1Uncertainty appears to jump up after major shocks like the Cuban Missile crisis,the assassination of JFK,the OPEC I oil-price shock,and the9/11terrorist attacks.Thispaper offers a structural framework to analyze the impact of these uncertainty shocks.I build a model with a time-varying second moment,which is numerically solved andestimated usingﬁrm-level data.The parameterized model is then used to simulate amacro uncertainty shock,which produces a rapid drop and rebound in aggregate out-put and employment.This occurs because higher uncertainty causesﬁrms to temporar-ily pause their investment and hiring.Productivity growth also falls because this pausein activity freezes reallocation across units.In the medium term the increased volatilityfrom the shock induces an overshoot in output,employment,and productivity.Thus,uncertainty shocks generate short sharp recessions and recoveries.This simulated im-pact of an uncertainty shock is compared to vector autoregression estimations on actualdata,showing a good match in both magnitude and timing.The paper also jointly esti-mates labor and capital adjustment costs(both convex and nonconvex).Ignoring capitaladjustment costs is shown to lead to substantial bias,while ignoring labor adjustmentcosts does not.K EYWORDS:Adjustment costs,uncertainty,real options,labor and investment.1.INTRODUCTIONU NCERTAINTY APPEARS TO dramatically increase after major economic and political shocks like the Cuban missile crisis,the assassination of JFK,the OPEC I oil-price shock,and the9/11terrorist attacks.Figure1plots stock-market volatility—one proxy for uncertainty—which displays large bursts of uncertainty after major shocks,which temporarily double(implied)volatility on average.2These volatility shocks are strongly correlated with other mea-sures of uncertainty,like the cross-sectional spread ofﬁrm-and industry-level earnings and productivity growth.Vector autoregression(VAR)estimations suggest that they also have a large real impact,generating a substantial drop and rebound in output and employment over the following6months.1This article was the main chapter of my Ph.D.thesis,previously called“The Impact of Uncer-tainty Shocks:A Firm-Level Estimation and a9/11Simulation.”I would like to thank my advisors Richard Blundell and John V an Reenen;the co-editor and the referees;my formal discussants Susantu Basu,Russell Cooper,Janice Eberly,Eduardo Engel,John Haltiwanger,V alerie Ramey, and Chris Sims;Max Floetotto;and many seminar audiences.Financial support of the ESRC and the Sloan Foundation is gratefully acknowledged.2Inﬁnancial markets implied share-returns volatility is the canonical measure for uncertainty. Bloom,Bond,and V an Reenen(2007)showed thatﬁrm-level share-returns volatility is signif-icantly correlated with a range of alternative uncertainty proxies,including real sales growth volatility and the cross-sectional distribution ofﬁnancial analysts’forecasts.While Shiller(1981) has argued that the level of stock-price volatility is excessively high,Figure1suggests that changes in stock-price volatility are nevertheless linked with real andﬁnancial shocks.©2009The Econometric Society DOI:10.3982/ECTA6248624NICHOLAS BLOOMF IGURE1.—Monthly U.S.stock market volatility.Notes:Chicago Board of Options Exchange VXO index of percentage implied volatility,on a hypothetical at the money S&P100option 30days to expiration,from1986onward.Pre-1986the VXO index is unavailable,so actual monthly returns volatilities are calculated as the monthly standard deviation of the daily S&P500 index normalized to the same mean and variance as the VXO index when they overlap from1986 onward.Actual and VXO are correlated at0.874over this period.A brief description of the na-ture and exact timing of every shock is contained in Appendix A.The asterisks indicate that for scaling purposes the monthly VXO was capped at50.Uncapped values for the Black Monday peak are58.2and for the credit crunch peak are64.4.LTCM is Long T erm Capital Management. Uncertainty is also a ubiquitous concern of policymakers.For example,af-ter9/11the Federal Open Market Committee(FOMC),worried about exactly the type of real-options effects analyzed in this paper,stated in October2001 that“the events of September11produced a marked increase in uncertainty [...]depressing investment by fostering an increasingly widespread wait-and-see attitude.”Similarly,during the credit crunch the FOMC noted that“Sev-eral[survey]participants reported that uncertainty about the economic out-look was leadingﬁrms to defer spending projects until prospects for economic activity became clearer.”Despite the size and regularity of these second-moment(uncertainty) shocks,there is no model that analyzes their effects.This is surprising given the extensive literature on the impact ofﬁrst-moment(levels)shocks.This leaves open a wide variety of questions on the impact of major macroeco-nomic shocks,since these typically have both aﬁrst-and a second-moment component.The primary contribution of this paper is to structurally analyze these types of uncertainty shocks.This is achieved by extending a standardﬁrm-levelTHE IMP ACT OF UNCERTAINTY SHOCKS625 model with a time-varying second moment of the driving process and a mix of labor and capital adjustment costs.The model yields a central region of inac-tion in hiring and investment space due to nonconvex adjustment costs.Firms only hire and invest when business conditions are sufﬁciently good,and only ﬁre and disinvest when they are sufﬁciently bad.When uncertainty is higher, this region of inaction expands—ﬁrms become more cautious in responding to business conditions.I use this model to simulate the impact of a large temporary uncertainty shock andﬁnd that it generates a rapid drop,rebound,and overshoot in em-ployment,output,and productivity growth.Hiring and investment rates fall dramatically in the4months after the shock because higher uncertainty in-creases the real-option value to waiting,soﬁrms scale back their plans.Once uncertainty has subsided,activity quickly bounces back asﬁrms address their pent-up demand for labor and capital.Aggregate productivity growth also falls dramatically after the shock because the drop in hiring and investment reduces the rate of reallocation from low to high productivityﬁrms,which drives the majority of productivity growth in the model as in the real economy.3Again productivity growth rapidly bounces back as pent-up reallocation occurs.In the medium term the increased volatility arising from the uncertainty shock generates a“volatility overshoot.”The reason is that mostﬁrms are lo-cated near their hiring and investment thresholds,above which they hire/invest and below which they have a zone of inaction.So small positive shocks gener-ate a hiring and investment response while small negative shocks generate no response.Hence,hiring and investment are locally convex in business condi-tions(demand and productivity).The increased volatility of business condi-tions growth after a second-moment shock therefore leads to a medium-term rise in labor and capital.In sum,these second-moment effects generate a rapid slowdown and bounce-back in economic activity,entirely consistent with the empirical evi-dence.This is very different from the much more persistent slowdown that typically occurs in response to the type ofﬁrst-moment productivity and/or demand shock that is usually modelled in the literature.4This highlights the importance to policymakers of distinguishing between the persistentﬁrst-moment effects and the temporary second-moment effects of major shocks.I then evaluate the robustness of these predictions to general equilibrium ef-fects,which for computational reasons are not included in my baseline model. T o investigate this I build the falls in interest rates,prices,and wages that oc-cur after actual uncertainty shocks into the simulation.This has little short-run effect on the simulations,suggesting that the results are robust to general equi-librium effects.The reason is that the rise in uncertainty following a second-moment shock not only generates a slowdown in activity,but it also makesﬁrms 3See Foster,Haltiwanger,and Krizan(2000,2006).4See,for example,Christiano,Eichenbaum,and Evans(2005)and the references therein.626NICHOLAS BLOOMtemporarily extremely insensitive to price changes.This raises a second policy implication that the economy will be particularly unresponsive to monetary or ﬁscal policy immediately after an uncertainty shock,suggesting additional cau-tion when thinking about the policy response to these types of events.The secondary contribution of this paper is to analyze the importance of jointly modelling labor and capital adjustment costs.For analytical tractability and aggregation constraints the empirical literature has estimated either labor or capital adjustment costs individually,assuming the other factor isﬂexible, or estimated them jointly,assuming only convex adjustment costs.5I jointly estimate a mix of labor and capital adjustment costs(both convex and non-convex)by exploiting the properties of homogeneous functions to reduce the state space.The estimation uses simulated method of moments onﬁrm-level data to overcome the identiﬁcation problem associated with the limited sample size of macro data.Iﬁnd moderate nonconvex labor adjustment costs and sub-stantial nonconvex capital adjustment costs.I alsoﬁnd that assuming capital adjustment costs only—as is standard in the investment literature—generates an acceptable overallﬁt,while assuming labor adjustment costs only—as is standard in the labor demand literature—produces a poorﬁt.The analysis of uncertainty shocks links with the earlier work of Bernanke (1983)and Hassler(1996)who highlighted the importance of variations in un-certainty.6In this paper I quantify and substantially extend their predictions through two major advances:ﬁrst,by introducing uncertainty as a stochas-tic process which is critical for evaluating the high-frequency impact of major shocks and,second,by considering a joint mix of labor and capital adjustment costs which is critical for understanding the dynamics of employment,invest-ment,and productivity.This framework also suggests a range of future research.Looking at individ-ual events,it could be used,for example,to analyze the uncertainty impact of trade reforms,major deregulations,tax changes,or political elections.It also suggests there is a trade-off between policy“correctness”and“decisiveness”—it may be better to act decisively(but occasionally incorrectly)than to deliber-5See,for example:on capital,Cooper and Haltiwanger(1993),Caballero,Engel,and Halti-wanger(1995),Cooper,Haltiwanger,and Power(1999),and Cooper and Haltiwanger(2006);on labor,Hammermesh(1989),Bertola and Bentolila(1990),Davis and Haltiwanger(1992),Ca-ballero and Engel(1993),Caballero,Engel,and Haltiwanger(1997),and Cooper,Haltiwanger, and Willis(2004);on joint estimation with convex adjustment costs,Shapiro(1986),Hall(2004), and Merz and Yashiv(2007);see Bond and V an Reenen(2007)for a full survey of the literature.6Bernanke developed an example of uncertainty in an oil cartel for capital investment,while Hassler solved a model with time-varying uncertainty andﬁxed adjustment costs.There are of course many other linked recent strands of literature,including work on growth and volatility such as Ramey and Ramey(1995)and Aghion,Angeletos,Banerjee,and Manova(2005),on investment and uncertainty such as Leahy and Whited(1996)and Bloom,Bond,and V an Reenen (2007),on the business-cycle and uncertainty such as Barlevy(2004)and Gilchrist and Williams (2005),on policy uncertainty such as Adda and Cooper(2000),and on income and consumption uncertainty such as Meghir and Pistaferri(2004).THE IMP ACT OF UNCERTAINTY SHOCKS627 ate on policy,generating policy-induced uncertainty.For example,when the Federal Open Markets Committee discussed the negative impact of uncer-tainty after9/11it noted that“A key uncertainty in the outlook for investment spending was the outcome of the ongoing Congressional debate relating to tax incentives for investment in equipment and software”(November6th,2001). Hence,in this case Congress’s attempt to revive the economy with tax incen-tives may have been counterproductive due to the increased uncertainty the lengthy policy process induced.More generally,the framework in this paper also provides one response to the“where are the negative productivity shocks?”critique of real business cy-cle theories.7In particular,since second-moment shocks generate large falls in output,employment,and productivity growth,it provides an alternative mech-anism toﬁrst-moment shocks for generating recessions.Recessions could sim-ply be periods of high uncertainty without negative productivity shocks.En-couragingly,recessions do indeed appear in periods of signiﬁcantly higher un-certainty,suggesting an uncertainty approach to modelling business cycles(see Bloom,Floetotto,and Jaimovich(2007)).T aking a longer-run perspective this paper also links to the volatility and growth literature,given the large negative impact of uncertainty on output and productivity growth.The rest of the paper is organized as follows:in Section2,I empirically in-vestigate the importance of jumps in stock-market volatility;in Section3,I set up and solve my model of theﬁrm;in Section4,I characterize the solution of the model and present the main simulation results;in Section5,I outline my simulated method of moments estimation approach and report the parameter estimates using U.S.ﬁrm data;and in Section6,I run some robustness test on the simulation results.Finally,Section7offers some concluding remarks.Data and programs are provided in an online supplement(Bloom(2009)).2.DO JUMPS IN STOCK-MARKET VOLATILITY MATTER?T wo key questions to address before introducing any models of uncertainty shocks are(i)do jumps8in the volatility index in Figure1correlate with other measures of uncertainty and(ii)do these have any impact on real economic outcomes?In Section2.1,I address theﬁrst question by presenting evidence showing that stock-market volatility is strongly linked to other measures of pro-ductivity and demand uncertainty.In Section2.2,I address the second question by presenting vector autoregression(VAR)estimations showing that volatil-ity shocks generate a short-run drop in industrial production of1%,lasting about6months,and a longer-run overshoot.First-moment shocks to the in-terest rates and stock-market levels generate a much more gradual drop and 7See the extensive discussion in King and Rebelo(1999).8I tested for jumps in the volatility series using the bipower variation test of Barndorff-Nielsen and Shephard(2006)and found statistically signiﬁcant evidence for jumps.See Appendix A.1.628NICHOLAS BLOOMrebound in activity lasting2to3years.A full data description for both sections is contained in Appendix A.92.1.Empirical Evidence on the Links Between Stock-MarketVolatility and UncertaintyThe evidence presented in T able I shows that a number of cross-sectional measures of uncertainty are highly correlated with time-series stock-market volatility.Stock-market volatility has also been previously used as a proxy for uncertainty at theﬁrm level(e.g.,Leahy and Whited(1996)and Bloom,Bond, and V an Reenen(2007)).Columns1–3of T able I use the cross-sectional standard deviation ofﬁrms’pretax proﬁt growth,taken from the quarterly accounts of public companies. As can be seen from column1stock-market time-series volatility is strongly correlated with the cross-sectional spread ofﬁrm-level proﬁt growth.All vari-ables in T able I have been normalized by their standard deviations(SD).The coefﬁcient implies that the2.47SD rise in stock-market time-series volatility that occurred on average after the shocks highlighted in Figure1would be as-sociated with a1.31SD(1 31=2 47×0 532)rise in the cross-sectional spread of the growth rate of proﬁts,a large increase.Column2reestimates this in-cluding a full set of quarterly dummies and a time trend,ﬁnding very similar results.10Column3also includes quarterly standard industrial criterion(SIC) three-digit industry controls and againﬁnds similar results,11suggesting that idiosyncraticﬁrm-level shocks are driving the time-series variations in volatil-ity.Columns4–6use a monthly cross-sectional stock-return measure and show that this is also strongly correlated with the stock-return volatility index. Columns7and8report the results from using the standard deviation of annual ﬁve-factor T otal Factor Productivity(TFP)growth within the National Bureau of Economic Research(NBER)manufacturing industry data base.There is also a large and signiﬁcant correlation of the cross-sectional spread of industry productivity growth and stock-market volatility.Finally,columns9and10use a measure of the dispersion across macro forecasters over their predictions for future gross domestic product(GDP),calculated from the Livingstone half-yearly survey of professional forecasters.Once again,periods of high stock-market volatility are signiﬁcantly correlated with cross-sectional dispersion,in this case in terms of disagreement across macro forecasters.9All data and programﬁles are also available at /~nbloom/.10This helps to control for any secular changes in volatility(see Davis,Haltiwanger,Jarmin, and Miranda(2006)).11This addresses the type of concerns that Abraham and Katz(1986)raised about Lillien’s (1982)work on unemployment,where time-series variations in cross-sectional unemployment appeared to be driven by heterogeneous responses to common macro shocks.THE IMP ACT OF UNCERTAINTY SHOCKS 629TABLE IT HE S TOCK -M ARKET V OLATILITY I NDEX R EGRESSED ON C ROSS -S ECTIONAL M EASURES OF U NCERTAINTY aExplanatory V ariable Is Period by Period Cross-Sectional Standard Deviation of Dependent V ariable Is Stock-Market Volatility b12345678910Firm proﬁt growth,c Compustat quarterly 0.5320.5260.469(0.064)(0.092)(0.115)Firm stock returns,d CRSP monthly 0.5430.5440.570(0.037)(0.038)(0.037)Industry TFP growth,e SIC 4-digit yearly 0.4290.419(0.119)(0.125)GDP forecasts,f Livingstone half-yearly 0.6140.579(0.111)(0.121)Time trend No Y es Y es No Y es Y es No Y es No Y es Month/quarter/half-year dummies g No Y es Y es No Y es Y es n/a n/a No Y es Controls for SIC 3-digit industry h No No Y es No No Y es n/a n/a n/a n/a R 20.2870.3010.2380.2870.3390.3730.2820.2840.3320.381Time span 62Q3–05Q162M7–06M121962–199662H2–98H2Average units in cross section i 32735542557.4Observations in regression 1715343563a Each column reports the coefﬁcient from regressing the time series of stock-market volatility on the within period cross-sectional standard deviation (SD)of the explanatory variable calculated from an underlying panel.All variables normalized to a SD of 1.Standard errors are given in italics in parentheses below.So,for example,column 1reports that the stock-market volatility index is on average 0.532SD higher in a quarter when the cross-sectional spread of ﬁrms’proﬁt growth is 1SD higher.b The stock-market volatility index measures monthly volatility on the U.S.stock market and is plotted in Figure 1.The quarterly,half-yearly,and annual values are calculated by averaging across the months within the period.c The standard deviation of ﬁrm proﬁt growth measures the within-quarter cross-sectional spread of proﬁt growth rates normalized by average sales,deﬁned as (proﬁts t −proﬁts t −1)/(0 5×sales t +0 5×sales t −1)and uses ﬁrms with 150+quarters of data in Compustat quarterly accounts.d The standard deviation of ﬁrm stock returns measures the within month cross-sectional standard deviation of ﬁrm-level stock returns for ﬁrm with 500+months of data in the Center for Research in Securities Prices (CRSP)stock-returns ﬁle.e The standard deviation of industry TFP growth measures the within-year cross-industry spread of SIC 4-digit manufacturing TFP growth rates,calculated using the ﬁve-factor TFP growth ﬁgures from the NBER data base.f The standard deviation of GDP forecasts comes from the Philadelphia Federal Reserve Bank’s biannual Livingstone survey,calculated as the (standard deviation /mean)of forecasts of nominal GDP 1year ahead,using half-years with 50+forecasts,linearly detrended to remove a long-run downward drift.g Month/quarter/half-year dummies refers to quarter,month,and half-year controls for period effects.h Controls for SIC 3-digit industry denotes that the cross-sectional spread is calculated with SIC 3-digit by period dummies so the proﬁt growth and stock returns are measured relative to the industry period average.i Average units in cross section refers to the average number of units (ﬁrms,industries,or forecasters)used to measure the cross-sectional spread.630NICHOLAS BLOOM2.2.VAR Estimates on the Impact of Stock-Market Volatility ShocksT o evaluate the impact of uncertainty shocks on real economic outcomes I estimate a range of VARs on monthly data from June1962to June 2008.12The variables in the estimation order are log(S&P500stock mar-ket index),a stock-market volatility indicator(described below),Federal Funds Rate,log(average hourly earnings),log(consumer price index),hours, log(employment),and log(industrial production).This ordering is based on the assumptions that shocks instantaneously inﬂuence the stock market(levels and volatility),then prices(wages,the consumer price index(CPI),and interest rates),andﬁnally quantities(hours,employment,and output).Including the stock-market levels as theﬁrst variable in the VAR ensures the impact of stock-market levels is already controlled for when looking at the impact of volatility shocks.All variables are Hodrick–Prescott(HP)detrended(λ=129,600)in the baseline estimations.The main stock-market volatility indicator is constructed to take a value1 for each of the shocks labelled in Figure1and a0otherwise.These17shocks were explicitly chosen as those events when the peak of HP detrended volatility level rose signiﬁcantly above the mean.13This indicator function is used to en-sure that identiﬁcation comes only from these large,and arguably exogenous, volatility shocks rather than from the smaller ongoingﬂuctuations.Figure2plots the impulse response function of industrial production(the solid line with plus symbols)to a volatility shock.Industrial production displays a rapid fall of around1%within4months,with a subsequent recovery and re-bound from7months after the shock.The1standard-error bands(dashed lines)are plotted around this,highlighting that this drop and rebound is sta-tistically signiﬁcant at the5%level.For comparison to aﬁrst-moment shock, the response to a1%impulse to the Federal funds rate(FFR)is also plot-ted(solid line with circular symbols),displaying a much more persistent drop and recovery of up to0.7%over the subsequent2years.14Figure3repeats the same exercise for employment,displaying a similar drop and recovery in activ-ity.Figures A1,A2,and A3in the Appendix conﬁrm the robustness of these VAR results to a range of alternative approaches over variable ordering,vari-able inclusion,shock deﬁnitions,shock timing,and detrending.In particular, these results are robust to identiﬁcation from uncertainty shocks deﬁned by the10exogenous shocks arising from wars,OPEC shocks,and terror events.12Note that this period excludes most of the Credit Crunch,which is too recent to have full VAR data available.I would like to thank V alerie Ramey and Chris Sims(my discussants)for their initial VAR estimations and subsequent discussions.13The threshold was1.65standard deviations above the mean,selected as the5%one-tailed signiﬁcance level treating each month as an independent observation.The VAR estimation also uses the full volatility series(which does not require deﬁning shocks)andﬁnds very similar results, as shown in Figure A1.14The response to a5%fall the stock-market levels(not plotted)is very similar in size and magnitude to the response to a1%rise in the FFR.THE IMP ACT OF UNCERTAINTY SHOCKS631F IGURE2.—VAR estimation of the impact of a volatility shock on industrial production.Notes: Dashed lines are1standard-error bands around the response to a volatility shock.3.MODELLING THE IMP ACT OF AN UNCERTAINTY SHOCKIn this section I model the impact of an uncertainty shock.I take a standard model of theﬁrm15and extend it in two ways.First,I introduce uncertainty as a stochastic process to evaluate the impact of the uncertainty shocks shown in Figure1.Second,I allow a joint mix of convex and nonconvex adjustment costs for both labor and capital.The time-varying uncertainty interacts withF IGURE3.—VAR estimation of the impact of a volatility shock on employment.Notes:Dashed lines are1standard-error bands around the response to a volatility shock.15See,for example,Bertola and Caballero(1994),Abel and Eberly(1996),or Caballero and Engel(1999).632NICHOLAS BLOOMthe nonconvex adjustment costs to generate time-varying real-option effects, which driveﬂuctuations in hiring and investment.I also build in temporal and cross-sectional aggregation by assumingﬁrms own large numbers of produc-tion units,which allows me to estimate the model’s parameters onﬁrm-level data.3.1.The Production and Revenue FunctionEach production unit has a Cobb–Douglas16production function(3.1)F( A K L H)= AKα(LH)1−αin productivity( A),capital(K),labor(L),and hours(H).Theﬁrm faces an isoelastic demand curve with elasticity(ε),Q=BP−εwhere B is a(potentially stochastic)demand shifter.These can be combined into a revenue function R( A B K L H)= A1−1/εB1/εKα(1−1/ε)(LH)(1−α)(1−1/ε). For analytical tractability I deﬁne a=α(1−1/ε)and b=(1−α)(1−1/ε),and substitute A1−a−b= A1−1/εB1/ε,where A combines the unit-level productivity and demand terms into one index,which for expositional simplicity I will refer to as business conditions.With these redeﬁnitions we have17S(A K L H)=A1−a−b K a(LH)bWages are determined by undertime and overtime hours around the standard working week of40hours.Following the approach in Caballero and Engel (1993),this is parameterized as w(H)=w1(1+w2Hγ),where w1,w2,andγare parameters of the wage equation to be determined empirically.3.2.The Stochastic Process for Demand and ProductivityI assume business conditions evolve as an augmented geometric random walk.Uncertainty shocks are modelled as time variations in the standard devi-ation of the driving process,consistent with the stochastic volatility measure of uncertainty in Figure1.16While I assume a Cobb–Douglas production function,other supermodular homogeneous unit revenue functions could be used.For example,when replacing(3.1)with a constant elas-ticity of substitution aggregator over capital and labor,where F( A K L H)= A(α1Kσ+α2(LH)σ)1/σ,I obtained similar simulation results.17This reformulation to A as the stochastic variable to yield a jointly homogeneous revenue function avoids long-run effects of uncertainty reducing or increasing output because of convexity or concavity in the production function.See Abel and Eberly(1996)for a discussion.Business conditions are in fact modelled as a multiplicative compositeof three separate random walks18:a macro-level component(A Mt ),aﬁrm-level component(A Fi t ),and a unit-level component(A Ui j t),where A i j t=A Mt A Fi tA Ui j tand i indexesﬁrms,j indexes units,and t indexes time.The macro-level component is modelled asA Mt =A Mt−1(1+σt−1W Mt) W Mt∼N(0 1)(3.2)whereσt is the standard deviation of business conditions and W Mt is a macro-level independent and identically distributed(i.i.d.)normal shock.Theﬁrm-level component is modelled asA Fi t =A Fi t−1(1+μi t+σt−1W F i t) W F i t∼N(0 1)(3.3)whereμi t is aﬁrm-level drift in business conditions and W Fi t is aﬁrm-leveli.i.d.normal shock.The unit-level component is modelled asA Ui j t =A Ui j t−1(1+σt−1W Ui j t) W Ui j t∼N(0 1)(3.4)where W Ui j t is a unit-level i.i.d.normal shock.I assume W Mt,W Fi t,and W Ui j tareall independent of each other.While this demand structure may seem complex,it is formulated to ensure that(i)units within the sameﬁrm have linked investment behavior due to com-monﬁrm-level business conditions,and(ii)they display some independent be-havior due to the idiosyncratic unit-level shocks,which is essential for smooth-ing under aggregation.This demand structure also assumes that macro-,ﬁrm-, and unit-level uncertainty are the same.19This is broadly consistent with the re-sults from T able I forﬁrm and macro uncertainty,which show these are highly 18A random-walk driving process is assumed for analytical tractability,in that it helps to deliver a homogenous value function(details in the next section).It is also consistent with Gibrat’s law. An equally plausible alternative assumption would be a persistent AR(1)process,such as the following based on Cooper and Haltiwanger(2006):log(A t)=α+ρlog(A t−1)+v t,where v t∼N(0 σt−1),ρ=0 885.T o investigate this alternative I programmed another monthly simulationwith autoregressive business conditions and no labor adjustment costs(so I could drop the labor state)and all other modelling assumptions the same.I found in this setup there were still large real-options effects of uncertainty shocks on output,as plotted in Figure S1in the supplemental material(Bloom(2009)).19This formulation also generates business-conditions shocks at the unit level(ﬁrm level)that have three(two)times more variance than at the macro level.This appears to be inconsistent with actual data,since establishment data on things like output and employment are many times more volatile than the macro equivalent.However,it is worth noting two points.First,micro data also typically have much more measurement error than macro data so this could be causing the much greater variance of micro data.In stock-returns data,one of the few micro and macro indicators with almost no measurement error,ﬁrm stock returns have twice the variance of aggregate returns consistent with the modelling assumption.Second,because of the nonlinearities in the investment and hiring response functions(due to nonconvex adjustment costs),output and input growth is much more volatile at the unit level than at the macro level,which is smoothed by aggregation.So。

football 英语作文

Football,known as soccer in the United States,is a sport that has captivated the hearts of millions around the world.It is a game that brings people together,transcending cultural and linguistic barriers.Here is an essay exploring the essence of football and its impact on society.The Origins and Evolution of FootballFootball has a rich history that dates back to ancient civilizations.The earliest forms of the game can be traced back to China,where a game called Cuju was played as early as the2nd and3rd centuries BC.However,the modern game of football as we know it today originated in England in the mid19th century.The Cambridge Rules,established in1848, laid the foundation for the sports formalization.The Rules and Structure of the GameFootball is played with two teams,each consisting of11players,including a goalkeeper. The objective is to score goals by getting the ball into the opposing teams net.The team with the most goals at the end of the match wins.The game is regulated by a set of rules overseen by a referee,who ensures fair play and enforces penalties for rule violations. The Physical and Mental DemandsFootball requires a combination of physical fitness,agility,and mental fortitude.Players must be able to run for extended periods,possess quick reflexes,and have the stamina to compete at a high level for the full duration of the match.Additionally,teamwork and strategy are crucial,as players must work together to outmaneuver their opponents. The Cultural Significance of FootballFootball has become more than just a sport it is a cultural phenomenon.It is a source of national pride,with international tournaments like the FIFA World Cup drawing billions of viewers from around the globe.The sport also serves as a platform for social cohesion, bringing communities together to celebrate their shared passion for the game.The Economic ImpactThe football industry is a significant economic driver,generating billions of dollars in revenue each year.It supports a vast network of professionals,from players and coaches to marketing and management teams.Football clubs,particularly in Europe,have become global brands,attracting sponsorships and investments from multinational corporations.The Role of Football in Social DevelopmentBeyond its entertainment value,football plays a crucial role in social development.It is used as a tool for social inclusion,providing opportunities for disadvantaged youth to engage in physical activity and develop life skills.Football also promotes health and wellbeing,encouraging individuals to adopt active lifestyles.The Future of FootballAs football continues to grow in popularity,there is a focus on innovation and sustainability.Advances in technology are transforming the game,from virtual reality training to goalline technology.There is also an increasing emphasis on environmental responsibility,with efforts to reduce the sports carbon footprint and promote ecofriendly practices.ConclusionFootball is a sport that has the power to unite people from all walks of life.Its universal appeal,combined with its capacity to foster community and promote health,makes it an integral part of our global culture.As the sport evolves,it will continue to inspire, challenge,and bring joy to fans across the world.。

Baker et al (2008)中文英文论文翻译

• The first emphasizes that investors are less than fully rational. It views managerial financing and investment decisions as rational responses to securities market mispricing. 第一次强调投资者是少于完全理性。它认为筹资和投资的管理决定是对证券市场错误定价的理性反应，。
[1]. Irrational investors approach非理性投资者的方法
• Assuming that securities market arbitrage is imperfect, and thus that prices can be too high or too low.假设证券市场套利的就是不完美的并因而那价格可以太高或太低 • Two key building blocks: 构建基块的两个关键 1.Irrational investors must influence securities prices. This requires limits on arbitrage.非理性投资者必须影响证券价格。这就要求对套利限制 2.Managers must be smart in the sense of being able to distinguish market prices and fundamental value.经理们必须能够区分市场价格和基本价值的意义上的智能。 • Why assume that managers are “smart” in the sense of being able to identify mispricing?为什么假设管理者是"智能"意义上的能够识别错误定价吗？ 1.Having superior information about their own firm or creating an information advantage by managing earnings.拥有自己的公司的高级信息或通过管理收入创造信息优势。 2.Having fewer constraints than equally “smart” money managers including short horizons and short-sales constraints.具有比同样"智能"钱经理包括短的视野和卖空约束较少约束。

新冠肺炎疫情对我国资本市场的影响性分析及对策

Northern Finance Journal北方金融1引言2020年初，一场突如其来的新冠肺炎疫情席卷武汉，给鼠年春节埋上了一层阴霾。

作为一次突发公共安全卫生事件，新冠病毒由于其高隐蔽性、高传染性的特点给我国和世界造成了巨大的人员伤亡和经济损失。

但万幸的是，在经历了2003年非典的教训后，我国加强了公共安全卫生建设，提高了面临突发事件的反应机制，配以我国制度上的快决策力、高执行力、强贯彻力，再加上社会各阶层、各地区的同心协力，最终在抗击新冠肺炎的防疫战斗中取得了阶段性的胜利，为全球防疫争取到了宝贵的时间，为全球抗疫提供了珍贵的经验。

根据疫情的发展和我国的抗疫历程，疫情可以分为三个阶段，分别是高峰期、缓解期和消除期。

新冠疫情在中国已经得到基本控制，我国已经进入了疫情缓解阶段，但大部分欧美国家仍处于疫情高峰期。

新冠肺炎疫情不仅仅对全球各国的公民生命健康造成威胁，对世界经济更是一次巨大的打击，其中对资本市场的影响更为突出，具体表现在两个方面：一方面新冠肺炎疫情会通过预期来影响资本市场，另一方面疫情也会对实体经济产生冲击进一步对资本市场产生冲击。

本文将会以我国资本市场为例，简单介绍发生新冠疫情后，我国资本市场，尤其是证券市场的波动和走势，来阶段性分析新冠疫情对我国资本市场的冲击影响，然后结合目前国内外形势，找出我国资本市场面临的风险，从而提出提高我国资本市场抵抗力的几点建议。

2新冠肺炎疫情背景下的中国资本市场新冠肺炎疫情爆发时期正值我国春节期新冠肺炎疫情对我国资本市场的影响性分析及对策李婧（中国人民大学汉青学院，北京海淀区100089）内容摘要：从第一例新冠肺炎患者2019年12月1日确诊以来，截至2020年4月18日，全球新冠累计确诊病例已突破200万，其中国内累计确诊82735人，累计死亡4642人。

国外累计确诊2166468人，累计死亡149609人，其中确诊人数排在首位的依次是美国、意大利、西班牙、德国。

Bartov E., D. Givoly, and C. Hayn. 2002.

Journal of Accounting and Economics33(2002)173–204The rewards to meeting or beating earningsexpectations$Eli Bartov a,Dan Givoly b,Carla Hayn c,*a Stern School of Business,New York University,New York,NY10012-1118,USAb Graduate School of Management,University of California at Irvine,Irvine,CA92697-3125,USAc The Anderson Graduate School of Management,University of California at Los Angeles,Los Angeles,CA90095-1481,USAReceived2March2001;received in revised form5December2001AbstractThis paperﬁnds thatﬁrms that meet or beat current analysts’earnings expectations(MBE) enjoy a higher return over the quarter thanﬁrms with similar quarterly earnings forecast errors that fail to meet these expectations.Further,such a premium to MBE,although somewhat smaller,exists in the cases where MBE is likely to have been achieved through earnings or expectations management.Theﬁndings also indicate that the premium to MBE is a leading indicator of future performance.This premium and its predictive ability are only marginally affected by whether the MBE is genuine or the result of earnings or expectations management.r2002Elsevier Science B.V.All rights reserved.JEL classiﬁcation:G14;M41Keywords:Earnings expectation;Analysts’forecast;Expectations management;Earnings management; Loss$We gratefully acknowledge the helpful comments of Bill Baber,Michael Brennan,Jack Hughes, Patricia Hughes,Jim Ohlson,Joshua Ronen,Jerry Zimmerman,Ross Watts(the editor),Leonard Soffer (the referee)and participants of the accounting workshops at the University of British Columbia,Emory University,Pennsylvania State University,the University of Rochester,Tel-Aviv University,the University of Washington at St.Louis,the2001Annual Conference on Financial Economics and Accounting held at the University of Michigan,and the Annual Corporate Earnings Analysis Seminar sponsored by the Center for Investment Research.The capable computer assistance provided by Ashok Natarajan is appreciated.We thank Thomson/First Call(I/B/E/S International,Inc.)for providing the data on analysts’earnings forecasts.*Corresponding author.Tel.:+1-310-206-9225;fax:+1-310-825-3165.E-mail address:chayn@(C.Hayn).0165-4101/02/$-see front matter r2002Elsevier Science B.V.All rights reserved.PII:S0165-4101(02)00045-91.IntroductionMeeting or beating analysts’forecasts of earnings is a notion well entrenched in today’s corporate culture.From corporate boards’deliberations to ﬁnancial press reports and Internet chats,emphasis is placed on whether a company meets its earnings forecasts.The following comment typiﬁes the view of the ﬁnancial press regarding the importance of meeting Wall Street’s expectations:In January,for the 41st time in 42quarters since it went public,Microsoft reported earnings that meet or beat Wall Street estimates y .This is what chief executives and chief ﬁnancial ofﬁcers dream of:quarter after blessed quarter of not disappointing Wall Street.Sure,they dream about other things y But the simplest,most visible,most merciless measure of corporate success in the 1990s has become this one:Did you make your earnings last quarter?(see Fox,1997,p.77).The importance assigned to meeting earnings expectations is not surprising given the valuation relevance of earnings information.Recent anecdotal evidence,however,suggests that companies are not merely passive observers in the game of meeting or beating contemporaneous analysts’expectations (hereafter referred to as MBE).Rather,they are active players who try to win the game by altering reported earnings or managing analysts’expectations (see for example McGee,1997;Vickers,1999).The motivations often suggested for such a behavior are to maximize the share price,to boost management’s credibility for being able to meet the expectations of the company’s constituents (e.g.,stockholders and creditors),and to avoid litigation costs that could potentially be triggered by unfavorable earnings surprises.In this paper,we test whether,after controlling for the earnings forecast error for the period,there is a market premium to ﬁrms that MBE formed just prior to the release of quarterly earnings.Note that ﬁnding a premium to ﬁrms that meet or beat market expectations,after controlling for the earnings forecast error for the period ,is quite distinct from the well-established ﬁnding in the literature of a positive relation between earnings and stock returns ﬁrst documented by Ball and Brown (1968).For a premium to MBE to exist,the return over the period must be a function of not only unexpected earnings for the period (measured relative to the expectations held at the beginning of the period)but also the manner by which earnings expectations changed over the period,or the expectation path .This point is further discussed in Section 3.Exploring the MBE phenomenon further,we examine the extent to which the data on earnings forecast revisions and earnings surprises are consistent with expectations management or earnings management.Expectations management takes place whenever management purposefully dampens analysts’earnings forecasts to produce a positive earnings surprise (or avoid a negative earnings surprise)upon the earnings release.Earnings management generally involves using accrual accounting in order to produce earnings that surpass the forecasted earnings target.In the cases where earnings or expectations are likely to have been managed,we examine whether the premium to MBE still exists.Finally,various explanations for the potential payoffs from an MBE strategy are explored that are consistent with investor rationality.E.Bartov et al./Journal of Accounting and Economics 33(2002)173–204174E.Bartov et al./Journal of Accounting and Economics33(2002)173–204175 Based on a sample of nearly130,000quarterly earnings forecasts made between the years1983and1997and covering approximately65,000ﬁrm-quarters,we ﬁnd that,in line with previous research,instances in which companies meet or beat contemporaneous analysts’estimates have increased considerably in recent years.The trend is common to all quarterly reporting periods and is also present in the annual period.It is observed for both large and smallﬁrms.On average, analysts’forecasts made at the beginning of the period overestimate earnings (see similarﬁndings by Bareﬁeld and Comiskey,1975;Brown,1997;Richardson et al.,1999,among others).However as the end of the reporting period approaches, analysts’optimism(i.e.,their overestimation of earnings)turns,as evidenced by the predominance of downward revisions in earnings estimates,into pessimism (i.e.,underestimation of earnings).Further,the proportion of negative forecast error cases(measured relative to analysts’earnings forecasts made at the beginning of the quarter)that ends with a zero or positive earnings surprise(measured relative to the most recent analysts’earnings estimate)is greater than the proportion of positive or zero forecast error cases that ends with a negative surprise.Theseﬁndings are consistent with expectations management taking place late in the reporting period.Our primaryﬁndings show that investors rewardﬁrms whose earnings meet or beat analysts’estimates.After controlling for the quarterly forecast error(measured relative to analysts’earnings forecasts made at the beginning of the quarter),the quarter’s abnormal returns are positively and signiﬁcantly associated with the earnings surprise for the quarter(measured as the difference between reported earnings and the most recent earnings estimate at the time of the earnings announcement).The average return over quarters ending with a positive earnings surprise is signiﬁcantly higher,by about3.2%,than the return over quarters that have the same overall quarterly earnings forecast error but end with a negative earnings surprise.These results suggest that,independent of theﬁrm’s absolute performance,there is a reward to meeting or beating analysts’earnings expectation and a penalty for failing to do so.Ending the period‘‘with a bang’’(i.e.,with a positive earnings surprise)results in a stock valuation that cannot be explained by the absolute level of theﬁrm’s performance.The results of a premium to MBE are unlikely to be driven by investors’overreaction to good news(see,for example,Zarowin,1989;DeBondt and Thaler, 1990).Such overreaction,if present,should lead to subsequent market reversals of the abnormal returns generated by the earnings surprise.Yet our tests based on an examination of abnormal returns over both a short window(consisting of the following quarter)and longer windows(up to three years following the earnings announcement)do not detect such a reversal.The premium to earnings surprises appears to be justiﬁed on economic grounds:Earnings surprises apparently possess information content with respect to future earnings as evidenced by the positive association between earnings surprises and futureﬁrm performance.While the reasons underlying this association are not investigated here,its presence suggests that investors rationally react to earnings surprises.We furtherﬁnd that earnings surprises that are likely to have been obtained through earnings or expectationsmanagement are associated with only a slightly lower premium and have marginally weaker predictive power with respect to future earnings.The paper is organized as follows.The next section reviews the recent research on the issue of MBE.Section 3presents the empirical design,followed by a description of the sample and the data in Section 4.Results are provided in the following sections.The paper concludes with a short summary and suggestions for future research.2.Recent studies on MBEThe phenomenon of meeting or beating expectations has recently attracted interest among researchers.Brown (2001)and Matsumoto (2001)ﬁnd a disproportional number of cases in recent years where earnings per share are slightly (by a few cents)above analysts’forecasts.They further ﬁnd an increase over the years in the number of cases where actual earnings per share are exactly on target.Degeorge et al.(1999)ascertain that the MBE strategy is one of three performance thresholds that management tries to meet.Evidence provided by other studies suggests that both earnings manipulation and expectations management are used to accomplish this objective.Burgstahler and Eames (1998)provide evidence that downward revisions of forecasts occur more frequently when the revision would be sufﬁcient to avoid a negative earnings surprise,suggesting managers’inﬂuence on analysts’forecast revisions.Such inﬂuence is also documented by Skinner (1997),Kasznik and Lev (1995),Francis et al.(1994)and Soffer et al.(2000),who show that companies increasingly tend to warn investors about forthcoming unfavorable earnings.This behavior is consistent with expectations management as a means of MBE.In addition to expectations management,Burgstahler and Eames (1998)ﬁnd that the time-series behavior of earnings is consistent with companies managing their earnings so as to meet analysts’expectations.Evidence consistent with earnings management to meet earnings forecasts is also provided by Kasznik (1999)and Payne and Robb (1997).Whether carried out through earnings manipulation,expectations management or both,the beneﬁts from an MBE strategy are not immediately apparent,unless MBE acts as a predictor of the future prospects of the ﬁrm.Speciﬁcally,for a policy of MBE carried out through earnings management to be successful,investors must be incapable of detecting management’s reporting objectives.Likewise,for an MBE policy achieved through expectations management to be successful,investors must be incapable of correcting for an extractable past pattern of earnings forecast revisions and forecast errors.A net reward to MBE through managing earnings expectations is questionable for yet another reason.Dampening earnings expectations prior to the earnings announcement in order to generate a positive earnings surprise would result in a negative price effect that should offset the positive announcement period return,leaving the total return for the period unchanged.In fact,past research (see Kasznik and Lev,1995;Soffer et al.,2000)shows a signiﬁcant decline in the stock price ofE.Bartov et al./Journal of Accounting and Economics 33(2002)173–204176E.Bartov et al./Journal of Accounting and Economics33(2002)173–204177 companies who warn investors about forthcoming unfavorable disclosures(thus lowering investors’earnings expectations).In a related study,Kasznik and McNichols(1999)use a valuation framework to examine whether MBE results in a higherﬁrm valuation and higher forecasted earnings,and the extent to which analysts forecasts of earnings fully incorporate the information contained in MBE.They document a valuation premium to MBE that is associated with future proﬁtability.However,this future proﬁtability is not fully captured by analysts’revisions of future earnings estimates.Lopez and Rees(2000)ﬁnd that the earnings response coefﬁcient(ERC)is signiﬁcantly higher forﬁrms that meet analysts’forecasts.Our analysis contributes to the literature in several respects.First,we provide evidence on the premium to MBE and relate it to several attributes of theﬁrm and the persistence of MBE incidence.In so doing,we use an information content/event study paradigm rather than a valuation framework,which increases the power of the tests by focusing on the exact arrival time of information to the market.1Second,by analyzing the expectation paths,we are able to make inferences about the manner by which management accomplishes the task of MBE,contributing to the research on management of earnings expectations.In particular,our research method allows us to distinguish between the two managerial tools for achieving MBE:earnings management and expectations management.Third,we examine the relation between the premium to MBE and the presence of expectations and earnings management. Finally,we examine alternative explanations for the premium to MBE.3.Terminology and hypotheses relating to the premium to MBE3.1.Relation between expectation paths and MBEMBE cases are,by deﬁnition,cases with a zero or positive earnings surprise.We examine the premium to MBE and analyze the extent to which MBE is achieved through expectations or earnings management.Expectations management takes place whenever management purposefully dampens analysts’earnings forecasts in order to produce a positive earnings surprise(or avoid a negative earnings surprise) upon the earnings release.To relate the MBE phenomenon to expectations management,we examine the‘‘path’’of expectation changes over the period. Different‘‘expectation paths’’are identiﬁed according to the sequence of earnings signals emanating from(1)the direction of analysts’forecast revisions during the period and(2)the sign of the earnings surprise upon earnings announcement.For example,one expectation path consists of a net upward revision in analysts’forecasts during the quarter(as evidenced by earnings forecasts at quarter end that are higher than the forecast initially issued for the quarter)followed by a positive earnings 1This approach avoids the difﬁculties associated with estimating basic parameters such as abnormal earnings,terminal value and the cost of capital required by tests based on valuation models(such as Ohlson,1995).surprise.Other expectation paths may consist of an upward revision and a negative earnings surprise,a downward revision and no earnings surprise,etc.3.2.TerminologyThe expectation path represents the sequence consisting of the direction of the net revision in analysts’forecasts (up,down or zero)and the sign of the earnings surprise (positive,negative or zero).To map out the expectation paths,the ﬁrst forecast and the last forecast for the quarter must be identiﬁed.We deﬁne the earliest forecast for quarter Q,F earliest ;as the ﬁrst forecast for the quarter made subsequent to the announcement of the previous quarter’s earnings.2The latest forecast for the quarter,F latest ;is the last forecast for the quarter made prior to the release of the earnings announcement for that quarter.The net revision in analysts’forecasts of earnings for the quarter (REV Q )is the difference between the latest earnings forecast and the earliest earnings forecast.The earnings surprise for the quarter (SURP Q )is deﬁned as the difference between the actual earnings number for the quarter (EPS Q )and F latest :The forecast error for the quarter (ERROR Q )is the difference between the actual earnings number and F earliest :Because the set of possible expectation paths differs somewhat for cases with positive,zero or negative forecast errors,we examine the paths separately for each error-sign group as shown in Table 1.3.3.Hypotheses relating to expectations managementIf the expectation path is not informative with respect to future ﬁrm performance and investors are rational,the course of the expectation path should not affect the abnormal return for the quarter.In particular,there should be no reward to an MBE strategy.Accordingly,the following hypothesis is advanced (expressed in its alternative form):H 1:After controlling for the forecast error,there is a premium to MBE.To better understand the nature of the premium (if any),we further test two additional hypotheses.One is that the premium to meeting expectations is similar to that associated with beating expectations.The second is that the premium to MBE and the penalty for failing to meet expectations are,per unit of surprise,of the same magnitude.Stated in their alternative forms,these hypotheses are that,after controlling for the forecast error for the period:H 2:The premium to meeting expectations is different from the premium to beating expectations.H 3:The premium to beating expectations is different from the penalty for failing to meet expectations.2Earnings forecasts for the current quarter made prior to the release of the previous quarter’s report were not considered since their subsequent revision is more likely to be correlated with the content of this report rather than with new information about the current quarter’s results.E.Bartov et al./Journal of Accounting and Economics 33(2002)173–2041783.4.Hypotheses regarding factors inﬂuencing the premium to MBE3.4.1.Financial position of the ﬁrmWhen a ﬁrm in ﬁnancial distress beats its earnings expectation,this conveys information about its ability to survive.That is,in addition to affecting future earnings projections,meeting or beating earnings expectations may alter investors’probability assessment regarding the future survival of the ﬁrm.Changes in the likelihood of survival are likely to be more valuable for ﬁrms in distress whose a priori probability of survival is considerably less than 1.0.Therefore,we expect a greater premium to MBE for ﬁrms in ﬁnancial distress.Accordingly,we test the hypothesis (stated in its alternative form)that,after controlling for the forecast error for the period:H 4:The premium to MBE of ﬁrms in ﬁnancial distress is larger than the premiumto MBE of ﬁnancially sound ﬁrms.3.4.2.MBE recurrenceInvestors’response to repeated instances of MBE depends on how such outcomes are perceived.If MBE is regarded as a signal of future performance,repeated instances of MBE would indicate earnings momentum and produce a greater premium than isolated cases of MBE.If,on the other hand,investors view repeated instances of MBE as a product of management intervention,the premium associated Table 1Deﬁnitions of expectation pathsForecast error group Revision:F latest ÀF earliest a Surprise:EPS ÀF latest(0or +indicatesmeeting or beatingexpectations)Expectations path (a path ending with ‘‘Zero’’or ‘‘Up’’indicates meeting or beating expectations)+ÀUp–Down Positive forecast errors (EPS ÀF earliest >0)+0Up–Zero ++Up–Up 0+Zero–Up À+Down–Up Zero forecast errors (EPS ÀF earliest ¼0)+ÀUp–Down 00Zero–Zero À+Down–Up +ÀUp–Down Negative forecast errors (EPS ÀF earliest o 0)0ÀZero–Down ÀÀDown–Down À0Down–Zero À+Down–Up a F earliest is the earliest forecast for the quarter.F latest is the latest forecast for the quarter.E.Bartov et al./Journal of Accounting and Economics 33(2002)173–204179with a persistent pattern of MBE would be lower than that associated with isolated cases of MBE.We thus test the following hypothesis (expressed in the alternative form):H 5:The premium to MBE of ‘‘habitual beaters’’of expectations is different fromthe premium to MBE of ‘‘sporadic beaters’’.4.Sample and dataThe sample consists of ﬁrm-quarter observations on the Thomas/First Call (I/B/E/S)database of analysts’forecasts that satisfy the following criteria:(1)There are at least two individual earnings forecasts (not necessarily by the sameanalyst)for the quarter,which are at least 20trading days apart.(2)The release date of the earliest forecast occurs at least three trading days afterthe release of the previous quarter’s earnings.(3)The release date of the latest forecast precedes the earnings release by at leastthree days.3The ﬁrst criterion ensures that there is an initial forecast and a subsequent forecast revision.These are required to be separated in time by at least 20days so that the second forecast is more likely to represent a true revision rather than a forecast issued almost concurrently with the initial forecast.The average length of time separating the two forecasts in our sample is 55trading days.The purpose of the second criterion is to prevent ‘‘stale’’forecasts (i.e.,those that are not revised following the previous quarter’s earnings announcement)from being included in the analysis.The third criterion is an attempt to ensure that the latest forecast is not ‘‘contaminated’’by knowledge of the actual earnings number.The total number of ﬁrm-quarters in the sample is 64,872(containing at least twice as many individual forecasts since our test design requires that both F earliest and F latest exist for each ﬁrm-quarter),spanning the period from January 1983to December 1997.The number of ﬁrm-quarters increases steadily from an average of about 400per ﬁscal quarter in the ﬁrst ﬁve years of the sample period to about 1,500per ﬁscal quarter in the last ﬁve years of the period.43For the second and third criteria,if more than one forecast is released on this day,the average value of the forecasts is used.4The primary analyses were also conducted with two alternative speciﬁcations of F earliest and F latest :Under one alternative speciﬁcation,we identify F latest (F earliest )as the latest (earliest)forecast made by the same analyst that produced F earliest (F latest ).Under the second alternative speciﬁcation,consensus forecasts (where each consensus reﬂects at least two individual analysts’forecasts)were used instead of individual forecasts.The results for these alternative speciﬁcations,which are not reported for the sake of brevity,were essentially the same as those reported in the paper.This is consistent with Brown and Han (1992)who found similar results using forecasts made by different analysts (comparable with our use of F earliest and F latest by different analysts)and forecasts produced by the same analyst.E.Bartov et al./Journal of Accounting and Economics 33(2002)173–204180E.Bartov et al./Journal of Accounting and Economics33(2002)173–204181Actual earnings numbers were retrieved from the I/B/E/S database.Other ﬁnancial accounting data were retrieved from Compustat.5Return data were obtained from the Center for Research on Security Prices(CRSP)database.5.Evidence on a premium to MBE5.1.Frequency of MBETo ascertain whether our sample is comparable to those employed by previous research with respect to the time series pattern of MBE,we produced the distribution of earnings surprises over time.Our results(not presented here)show that both MBE have become more prevalent in recent years,as documented by previous research(see,for example,Brown,1997)as well as contemporaneous studies(see Brown,2001;Lopez and Rees,2000;Matsumoto,2001).Speciﬁcally,weﬁnd that the proportion of favorable earnings surprises increased from about50%in the years 1983–1993to almost70%in the more recent period of1994–1997.Over these subperiods,the relative frequency of meeting earnings expectations(i.e.,a zero surprise)increased from9%to15%and the relative frequency of beating expectations(a positive surprise)increased from40%to52%.5.2.The reward to MBETo test for the existence of a premium to MBE(H1),we measure the incremental quarterly abnormal return of cases where expectations are met or beaten after controlling for the magnitude of the quarterly forecast error.In testing this hypothesis, we control for the magnitude of the forecast error by placingﬁrm-quarters within each error-sign group into portfolios based on the size of the forecast error calculated asðEPSÀF earliestÞ=j EPS j:Using5%forecast-error intervals results in nine equal-error-size portfolios for each of the positive-and negative-error groups,and one portfolio for the zero-error group.The portfolio approach used to control for the size of the forecast error has the advantage of not assuming any speciﬁc relation(e.g.,linear)between CAR and the forecast error.However,it has the drawback of not precluding variation of the forecast errors within the equal-error-size groups,a variation that could be potentially dependent on the path.To alleviate this drawback and to gain additional insights into the MBE phenomenon,we use a regression approach to control for the forecast error.Speciﬁcally,we test for an MBE premium(H1)and a differential premium to failing to meet,MBE(H2and H3)by estimating the following regression: CAR i;Q¼b0þb1ERROR i;Qþb2SURP i;Qþb3DMBE i;Qþb4DBEAT i;Qþb5DMBE i;QÃSURP i;Qþe i;Q;ð1Þ5In those instances where the I/B/E/S earnings number differed substantially(by more than50%)from the earnings number reported by Compustat and the difference could not be explained by a special item (since I/B/E/S reports an‘‘adjusted’’earnings number),we eliminated the observation.See Abarbanell and Lehavy(2000)for a discussion of the difﬁculties in comparing EPSﬁgures across databases.where i is the ﬁrm indexand Q designates the quarter.CAR is the beta-adjusted cumulative abnormal return over the period beginning two days following the date of F earliest and ending one day after the release of the quarter’s results.6The overall forecast error for the quarter,ERROR,and the end-of-quarter earnings surprise,SURP,are measured as described above and deﬂated by the ﬁrm’s stock price at the beginning of the quarter.DMBE and DBEAT are dummy variables that receive the value of 1.0if,respectively,SURP X 0(earnings met or exceeded expectations)and SURP>0(earnings exceeded expectations).Otherwise,these variables receive the value of zero.The interactive variable,DMBE*SURP,captures the extent to which the reward to beating expectations differs from the penalty for failing to meet expectations.7We expect b 1to be positive and signiﬁcant,in line with the ﬁndings of the vast body of research on the information content of earnings.Under the null of H 1,the coefﬁcients b 2and b 3are not expected to be signiﬁcantly different from zero.Under H 2,b 4should not be signiﬁcantly different from zero if the premium to beating expectations is similar to that for meeting them.Under the null of H 3,b 5should not be different from zero if the reward to MBE is comparable with the penalty for failing to meet expectations.Table 2reports the results of testing H 1(premium to MBE)and H 2(differential premium to beating versus merely meeting expectations)for the portfolio tests.The table presents the period abnormal returns by expectation path,controlling for the period’s forecast error.As noted earlier,this control is obtained through the construction of equal-error-size portfolios,in 5%increments.The table shows that within almost every error-size portfolio,the period abnormal return,CAR Q ,associated with beating expectations,that is,paths ending with a positive earnings surprise ( -Up paths),is signiﬁcantly higher than that associated with paths ending with an unfavorable earnings surprise ( -Down paths).8For example,positive error cases within the 0–5%error-size group that end with a positive earnings surprise have an average CAR of 2.9%while those in this same group that end with a negative earnings surprise show an average CAR of 1.5%.The comparable numbers for the 30–35%error-size group are an average CAR of about 8.5%versus an average CAR of 5.2%.96Alternative measures of abnormal returns for a period were computed:the cumulative beta-adjusted abnormal return (which assumes daily rebalancing)over the period,the period’s ‘‘buy-and-hold’’beta-adjusted abnormal return,the period’s cumulative size-adjusted returns,and an average ‘‘per-day’’measure of abnormal returns (to account for return intervals of different lengths).Use of these measures led to essentially the same results.7Note that this variable takes on a value of zero when expectations are met.8The average CAR Q for the -Down paths and that of the -Up paths is signiﬁcantly different (at the 0.01level or higher)for 13of the 18portfolios in Panel A.9Note that for the positive error cases,abnormal returns for the Up–Up path tend to be somewhat higher than for the two other MBE paths (Down–Up and Zero–Up).Similarly,for the negative error cases,abnormal returns for the Down–Down path tend to be lower than for the two other paths where expectations were not met (Up–Down and Zero–Down).Because our main concern is the distinction between the abnormal returns to MBE paths versus other paths,we offer no formal hypotheses regarding those differences.E.Bartov et al./Journal of Accounting and Economics 33(2002)173–204182。

成功与机会英语作文

Success and opportunity are two interconnected concepts that often go hand in hand. Heres a detailed English essay on the topic:Title:The Interplay of Success and OpportunityIntroduction:The journey to success is often paved with a series of opportunities,each presenting a chance to learn,grow,and excel.This essay explores the relationship between success and opportunity,how they influence each other,and the importance of seizing the right moments to achieve ones goals.The Definition of Success:Success is a subjective term,varying from person to person.For some,it might mean achieving professional milestones,while for others,it could be personal fulfillment or happiness.Regardless of the definition,success is often the result of hard work, dedication,and the ability to capitalize on opportunities.The Role of Opportunity:Opportunity is the chance or set of conditions that make it possible to do something or for something to happen.In the context of success,opportunities can be seen as doors that open up,allowing individuals to apply their skills,knowledge,and abilities to achieve their desired outcomes.The Importance of Recognizing Opportunities:Recognizing opportunities is a skill that can be developed.It requires an open mind,a keen eye for detail,and the ability to think creatively.Those who are adept at identifying opportunities are often the ones who find themselves on the path to success more frequently.Seizing Opportunities:Not all opportunities are created equal,and not all are worth pursuing.The ability to discern which opportunities are aligned with ones goals and values is crucial.Once identified,seizing these opportunities requires courage,initiative,and sometimes,a leap of faith.The Impact of Timing:Timing is a critical factor in the successopportunity equation.Being at the right place at the right time can significantly influence the outcome of an opportunity.However,timing is not just about luck it is also about being prepared and proactive.Case Studies:Throughout history,there are numerous examples of individuals who have achieved success by recognizing and seizing opportunities.From entrepreneurs like Steve Jobs, who saw the potential in personal computing,to scientists like Marie Curie,who discovered radium,these stories highlight the importance of opportunity in the path to success.Overcoming Challenges:Opportunities often come with challenges.Success stories are rarely without obstacles. The ability to overcome these challenges,learn from failures,and persist in the face of adversity is a hallmark of those who achieve success.Conclusion:In conclusion,success and opportunity are intertwined.Success is not a random event but a result of recognizing,seizing,and making the most of opportunities that come our way. It requires a combination of skill,timing,and perseverance.By understanding the importance of opportunities and being prepared to act on them,individuals can increase their chances of achieving their goals and finding success.Reflection:Reflecting on ones own experiences with opportunities and success can provide valuable insights into personal growth and development.It is through this reflection that we can better understand how to navigate our own paths to success in the future.。

intraday intensity index指标

intraday intensity index指标Intraday Intensity Index is a technical analysis indicator used to measure the level of buying and selling pressure in a particular stock or market during intraday (within a trading day) periods. The index is based on the concept that the intensity of trading activity can provide insights into the strength and direction of the price movement.The calculation of the Intraday Intensity Index involves analyzing the relationship between the price movement and the volume of trades. It takes into account the direction of the price change as well as the volume of trades executed at different price levels.The formula for calculating the Intraday Intensity Index is as follows:Intraday Intensity Index = (Close – Open) / (High – Low) * VolumeWhere:- Close is the closing price of the security- Open is the opening price of the security- High is the highest price reached during the trading period- Low is the lowest price reached during the trading period- Volume is the total volume of trades executed during the trading periodThe Intraday Intensity Index is plotted as a line chart and fluctuates between a range of -1 to +1. A positive value indicates a higher level of buying pressure, while a negative value indicates a higherlevel of selling pressure. Traders and analysts use this indicator to identify potential reversal points or confirm the strength of a trend. It is important to note that the Intraday Intensity Index is just one tool among many in technical analysis and should be used in conjunction with other indicators and analysis methods to make informed trading decisions.。

伯克希尔1995年之前的年度报告

伯克希尔1995年之前的年度报告英文版Berkshire Hathaway Annual Report Prior to 1995IntroductionBerkshire Hathaway, Inc., an investment holding company based in Omaha, Nebraska, has a storied history dating back to its inception in 1956. Prior to 1995, the company's annual reports were a testament to its unique investment philosophy, leadership principles, and remarkable growth under the guidance of Chairman and CEO, Warren Buffett.Investment PhilosophyBerkshire's investment approach before 1995 was centered around long-term value investing. Buffett emphasized the importance of identifying companies with durable competitive advantages, strong management teams, and reasonable valuations. He advocated for a patient and disciplined approach, avoiding market timing and speculative investments.Leadership PrinciplesBuffett's leadership style was evident in the annual reports, emphasizing the importance of integrity, transparency, and a focus on the long-term interests of shareholders. He emphasized the need for a strong culture of corporate governance, highlighting the role of independent directors and the importance of shareholder rights.Remarkable GrowthDespite the challenging economic environments during this period, Berkshire Hathaway achieved remarkable growth. The company's portfolio of businesses diversified across various industries, including insurance, utilities, retailing, and manufacturing. Buffett's ability to identify and capitalize on attractive investment opportunities, coupled with the company's strong financial position, fueled its expansion.ConclusionThe Berkshire Hathaway annual reports prior to 1995 offer a glimpse into the company's investment philosophy, leadershipprinciples, and remarkable growth under the guidance of Warren Buffett. These reports are a valuable resource for investors seeking to understand the foundation of Berkshire's success and the principles that have guided it for decades.中文版伯克希尔1995年之前的年度报告介绍伯克希尔·哈撒韦公司是一家位于内布拉斯加州奥马哈的投资控股公司，自1956年成立以来有着悠久的历史。

注册金融分析师一级(下午)-3

注册金融分析师一级(下午)-3(总分：120.00，做题时间：90分钟)一、{{B}}Afternoon Session{{/B}}(总题数：120，分数：120.00)1.Michael Jackson, CFA, works for a Privately Offered Fund Company as a portfolio manager. The firm purchases investment research about IT industry from a third-party independent research institution which the firm determines has a sound research basis and continuous reliability. Jackson communicates information from the third-party research to his retail and institutional clients but does not disclose the source. Jackson has most likely violated the Standards of Professional Conduct relating to:A. the Standards regarding disclosure of conflicts.B. the Standards regarding misrepresentation.C. the Standards regarding diligence and reasonable basis.（分数：1.00）A.B. √C.解析：[解析] 该CFA会员使用该第三方研究机构的投资研究成果是经过了公司的尽职调查，认为是有合理证据的并且持续可靠，所以该行为并没有违反勤勉尽责和合理证据(diligence and reasonable basis)的标准，但是他没有向会员指明所使用研究信息的出处，这一点违反了虚假陈述(misrepresentation)的规定，至于利益冲突的披露(disclosure of conflicts)在试题中并没有涉及。

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T HE I MPACT OF I NTRADAY T IMING OF E ARNINGS A NNOUNCEMENTS ONTHE B ID-ASK S PREAD AND D EPTHMaarten PronkTilburg UniversityP.O. Box 90153 5000 LE TILBURGThe NetherlandsPhone +31 (0)13 4662489 Fax +31 (0)13 4668001Pronk@uvt.nlAcknowledgements:I am grateful to my supervisors Douglas DeJong and Gerard Mertens, to Peter Easton, Laurence van Lent, Mort Pincus, Jeroen Suijs and Terry Warfield, and to the workshop participants at Erasmus University Rotterdam, Lancaster University, London Business School, Nyenrode University, Tilburg University, University of Iowa, University of Washington, the 2004 EAA Conference and the EIASM Capital Market Conference for many helpful suggestions and comments on earlier versions of this study. I gratefully acknowledge financial support from The Netherlands Organization for Scientific Research (NWO R46-404 and R46-435).on the Bid-ask Spread and DepthAbstract: Libby, Mathieu and Robb (2002) investigate, among other things, the impact of intraday timing of earnings announcements on the bid-ask spread and depth for a sample of firms listed on the Toronto Stock Exchange. They document, in a univariate setting, that the spread is relatively wider and the depth lower after announcements declared during non-trading hours than after announcements released during trading hours.This study extends their research by (a) investigating earnings announcements declared by firms traded on the NYSE or AMEX, (b) addressing this issue in a multivariate setting, (c) exploring before-open and after-close announcements separately, and (d) analyzing the impact by half-hour interval. Interestingly, my results indicate, opposite to the findings by Libby et al (2002), that the spread is relatively smaller and the depth higher after overnight announcements than after daytime announcements. These findings are robust to firm-specific factors, cross-listings, differences in the content of daytime and overnight releases, and intraday timing consistency. In addition, this effect occurs after before-open and after after-close announcements, and the analysis by half-hour interval reveals that the impact on the spread (depth) lasts for four (seven) trading half-hours.Keywords: Intraday timing, spreads, depths, earnings announcementson the Bid-ask Spread and Depth1. IntroductionLibby, Mathieu and Robb (2002) (hereafter LMR) investigate, among other things, the impact of intraday timing of earnings announcements on information asymmetry. They use the bid-ask spread and depth as measures for information asymmetry and explore, in a univariate setting, whether the spread and depth after an earnings announcement depend on the moment of the announcement. They find that large firms listed on the Toronto Stock Exchange (TSE) experience a wider spread and a lower depth after announcements during non-trading hours (‘overnight’) than after announcements during trading hours (‘daytime’). These findings suggest that information asymmetry is higher after announcements made during non-trading hours.I extend the research into the impact of intraday timing of earnings announcements in several ways. First, this study investigates a sample of daytime and overnight earnings announcements declared by firms traded on the New York Stock Exchange (NYSE) or American Stock Exchange (AMEX). Second, I address the question in a multivariate setting. It is explored whether the findings are robust to firm-specific factors, cross-listings, differences in the content of daytime and overnight releases, and intraday timing consistency. Third, announcements that are made before the opening of the market or after the close of the market are investigated separately to determine whether this effect occurs after both types of announcements. Finally, I investigate the impact on the spread and depth by half-hour interval to find out how long the effect lasts.In contrast to LMR, I find that firms that are listed on the NYSE or AMEX exhibit a smaller spread and a higher depth after overnight announcements than after daytimeannouncements. This finding occurs both in a univariate and multivariate setting. I also find that the effect occurs after before-open and after after-close announcements, and the analysis by half-hour interval reveals that the difference in the spread (depth) after overnight and daytime announcements only lasts for four (seven) trading half-hours.The evidence in this study strongly suggests that the results by LMR cannot be generalized to the American setting. In addition, academics and practitioners outside Canada and the United States should take into account that LMR’s findings may also not apply to their setting.The remainder of this study is organized as follows. In section 2, I discuss the relevant literature and develop the hypotheses. Section 3 explains the research design, while section 4 presents the empirical results. Section 5 concludes.2. Literature overview and hypothesis development2.1 Bid-ask spread and depthTo maintain liquidity, many organized exchanges use specialists, individuals who stand ready to buy or sell whenever investors wish to sell or buy. In return for providing liquidity, specialists are granted monopoly rights by the exchange to post different prices for purchases and sales: They buy at the bid price and sell at a higher ask price.The quoted bid-ask spread is the difference between the quoted bid and ask price. It is possible that traders negotiate with the specialist about the bid or ask price. Therefore, specialists sometimes buy at a price that is higher than the quoted bid price or sell at a price that is lower than the quoted ask price. The effective bid-ask spread takes this possibility into account. It is calculated as two times the absolute value of the difference between the quote midpoint (average of the bid and ask price) and the inside bid or ask price for a purchase or sale, respectively.Extant market microstructure literature shows that the quoted bid-ask spread consists of three components: order processing costs, inventory holding costs, and adverse selection costs. The order processing cost component represents a fee charged by specialists for standing ready to match buy and sell orders (Tinic 1972). The inventory holding cost component compensates dealers for managing the inventory (Stoll 1978; Ho and Stoll 1981). Finally, the adverse selection component represents a reward to specialists for taking on the risk of dealing with traders who may possess superior information (Copeland and Galai 1983; Glosten and Milgrom 1985). In other words, adverse selection costs arise because some investors are better informed about a security’s value than the specialist, and trading with such investors will, on average, be a losing proposition for the specialist. Since specialists have no way to distinguish between the informed and uninformed, they are forced to engage in these losing trades and must be rewarded accordingly (Campell, Lo and MacKinlay, 1997). Therefore, specialists include a reward for these costs in the bid-ask spread. As a result, specialists earn more when they trade with traders without private information. These additional revenues compensate them for the missed spreads and missed returns while trading with informed investors. This component of the spread depends on the probability of informed trading. The higher the probability of informed trading is, the higher the adverse selection component and, thus, the bid-ask spread.Lee, Mucklow and Ready (1993) state that the bid-ask spread is only one dimension of market liquidity. The other dimension is the depth: the number of shares specialists are willing to trade at the quoted bid and ask prices, respectively. Specialists may declare the depth strategically to protect against informed trading. If a specialist quotes a large depth, an informed investor can trade many shares at one time against the quoted bid or ask price. However, if the specialist declares a small depth, the informed investor can only trade small portions at a time. After each trade, the specialist has the opportunity to change the bid and ask prices. Therefore, the specialist may lose less money before discovering the implicationsof new information for the fundamental share price. Thus, when the probability of informed trading increases, the spread is expected to increase and the depth is expected to decrease.2.2 Information asymmetry after earnings announcementsThe days after earnings announcements form a period in which specialists may experience an increased probability of informed trading. Kim and Verrecchia (1994) and Livne (2000), for instance, argue that some investors gather private information through their superior capacities to interpret earnings news. Specialists are, therefore, confronted with an increased probability of informed trading and may set wider spreads and lower depths after earnings announcements than during a non-announcement period. Lee et al (1993) and Krinsky and Lee (1995) present empirical evidence on this issue and show that the spread and adverse selection component are wider and depths are lower during trading hours after earnings announcements than during a non-announcement period.12.3 Impact of the intraday timing of earnings announcements on information asymmetryAn increased threat of informed trading after earnings announcements exists until all investors with superior capacities to interpret earnings news, have traded on their earnings related private information. The trading period during which the threat of informed trading by these traders exists, may depend on the moment of the announcement. If earnings news is revealed during non-trading hours, for instance, investors have time to analyze the earnings news carefully before the first trading opportunity. In addition, the earnings news is likely more disseminated just before opening of the market than just after a daytime announcement. Therefore, the opening price on the exchange may reflect the public earnings signal and all private information of investors with superior capacities to interpret earnings releases. In that situation, there exists no period with an increased probability of informed trading afteropening of the market. Also in case that the opening price does not incorporate all private information, the trading period with a higher probability of informed trading may be shorter and the threat of informed trading lower than in case of a daytime announcement, because all investors have to analyze the earnings information during trading hours after daytime announcements.2Although it sounds intuitive that private information is impounded in the stock price earlier after an overnight than after a daytime announcement, it is also possible to argue an opposite relationship. These arguments relate to differences in the trading procedures that are used on the NYSE/AMEX at and after the opening of the market. Specifically, on the NYSE/AMEX a call auction opens trading. During this opening procedure, the specialist determines an opening price by balancing the buy and sell orders submitted overnight. If news is announced during trading hours, on the other hand, the first post-announcement price on the NYSE/AMEX is determined by the continuous auction mechanism in place after the opening of the market. Thus, the first post-announcement trade following an overnight announcement represents a batching and execution of many orders, whereas the first post-announcement trade following a daytime announcement represents the execution of only one order. After the open, specialists are charged with maintaining fair and orderly markets (e.g. maintaining a smooth sequence of prices and avoiding large price changes between successive trades) while competing with floor traders and limit orders (Greene and Watts 1996).Gennotte and Trueman (1996) demonstrate that market prices reflect better the valuation implications of earnings announcements when they are made during trading hours rather than during non-trading hours. A basic assumption of their model is that the firm’s1 Lee et al (1993) and Krinsky and Lee (1995) only investigate daytime announcements.2 It is also possible that the probability of informed trading is different after daytime and overnight announcements, because the content of the earnings announcements differs. This issue is discussed in section 3.managers, along with a subset of traders who closely follow the firm, are better able to make predictions about future profitability from current earnings that are other traders. The extent to which the post-announcement price set by the firm’s specialist reflects the information of these informed traders is determined by the specialist’s ability to discern from the post-announcement order flow the magnitude and direction of informed trading. His/her ability to do so, however, will be lessened to the extent that the order flow also includes orders from noise traders or from traders who are reacting to other disclosures. Trading that occurs subsequent to an overnight announcement is more likely to include such orders than is trading subsequent to a daytime release. Therefore, the post-announcement price may be less likely to reflect the information of the informed trades if the earnings disclosure is made during non-trading hours.In addition, Francis, Pagach and Stephan (1992) investigate price and volume reactions to overnight announcements on the NYSE. They find no evidence that investors’ opening trades reflect overnight accounting information. The absence of an opening reaction seems to be due to traders submitting only partial orders at the open. They argue that this behavior can be attributed either to investors’ reluctance to submit full orders because of their effect on opening prices or to investors’ postponing trades until they have observed opening prices. Contrary to Francis et al (1992), however, Greene and Watts (1996) investigate another sample of overnight announcements and provide evidence that the opening trade on the NYSE impounds most of the price response.Based on these arguments it is not possible to derive a directional hypothesis. In other words, it is an empirical question whether the spread (depth) is wider (lower) or smaller (higher) after overnight announcements compared to daytime announcements.33 It can be argued that specialists experience an increased probability of informed trading before earnings announcements and that intraday timing of earnings announcements also affects the bid-ask spread and depth during this period. Lee et al (1993) mention three reasons why the information asymmetry between specialists and informed investors may increase just before an earnings release,3. Research designI investigate the impact of intraday timing of earnings announcements on the bid-ask spread and the depth by estimation the following regression models:∆ qspread i (∆ espread i, ∆ depth i) = α + β1 Overnight i + β2 ∆ volume i + β3 |∆ price|i + β4 Good news i + β5 Size i + β6 Cross*Night i + β7 Inconsistent i + βk Ind dummies ki + εiThis section explains how all variables are calculated and why certain control variables are incorporated.3.1 Overnight and daytime announcementsThe data set consists of firms with a disclosure quality rating in ‘An Annual Review of Corporate Reporting Practices’ prepared by the Corporate Information Committee of the Association for Investment Management and Research (AIMR) for 1993/94, 1994/95 and 1995/96.4 The Dow Jones Interactive database is used to determine the date and time of thenamely: (a) a higher probability of leakage of value relevant information when earnings are known to the company, (b) the possibility that the officially filed information reaches investors earlier than specialists, and (c) the expectation of imminent earnings news may stimulate some traders to search for information immediately prior to the announcement. This second argument does not hold, however, for overnight announcements. In addition, Livne (2001) models the intraday timing of earnings announcements. He shows that investors with private information trade less aggressively before overnight than daytime announcements. These arguments suggest that the probability of informed trading increases less before overnight releases. I am, however, not able to present robust findings that are in line with this expectation. In fact, the percentage deviation of the spread (depth) from the median spread (depth) during the non-announcement period is not significantly different before daytime than before overnight announcements. Therefore, I focus in this study on the period after earnings announcements.4 The data set was developed for use in another project, which explains the focus on AIMR firms. Although a random sample is preferable, this selection criterion does not bias in favor of finding results. In fact, it may be more difficult to detect the items of interest with such firms. AIMR rated firms tend to be larger and to have higher stock prices (Welker 1995). In the literature, firm size is often used as a proxy for information availability in the market. When more information is available about firms before earnings announcements, the price reaction and probability of informed trading at announcements are expected to be smaller. Therefore, the change in spreads and depths may be smallerearnings announcements of these firms. For firms with an AIMR rating in the 1993/94 volume, I look for annual or quarterly earnings announcements between July 1, 1993 and June 30, 1994. The other two years’ reports are matched with earnings announcements in a similar way.I take the time stamp of the first publication of the annual or quarterly earnings announcement by Business Wire, Dow Jones News Service, Dow Jones International News or PR Newswire. Announcements on trading days between 9.30 AM and 4.00 PM EST are considered daytime announcements while all other announcements are regarded as overnight announcements. The variable Overnight i is a dummy variable that is 1 for overnight and 0 for daytime announcements.53.2 Bid-ask spread and depthThe variables ∆qspread i, ∆espread i and ∆depth i represent the percentage deviation in the quoted spread, effective spread and depth, respectively. The Trades and Quotes (TAQ) database is used to calculate these percentage deviations after earnings announcements from the related medians during the non-announcement period.Following Lee et al (1993), I exclude all thinly traded stocks (on average less than ten trades a day), and stocks with an average price below $5 or above $100 during the month of January that is closest to the announcement. I focus on firms traded on the NYSE or AMEX, because the NASDAQ has a different trading system. All quotes coded differently from opening or normal trading quotes are deleted. Quotes that are set before 9.30 AM or after 4.00 PM are also removed.and it might be more difficult to detect any differences between the reaction of spreads and depths to overnight and daytime announcements.5 The accuracy of the time stamp is important. The relative precision of these time stamps is difficult to gauge. Lee et al (1993), who follow a similar approach, show that no significant increase in trading volume occurs until the half-hour interval containing daytime announcements. This finding strongly suggests announcement times are accurate to within a half hour.In accordance with Lee et al (1993) and Krinsky and Lee (1995), each trading day is divided into thirteen half-hour trading intervals. Next, the period after earnings announcements is defined. For firms with a daytime announcement, the half-hour trading interval containing the earnings announcement is determined according to the time stamp of the news wire. The period after the earnings release consists of the half-hour trading interval containing the announcement plus the six trading hours thereafter (See FIGURE 1).6 For firms with an overnight announcement, the period after the announcement consists of 6.5 trading hours after the release (full trading day).Insert FIGURE 1For earnings announcements between July 1, 1993 and June 30, 1994, the non-announcement period consists of all trading days between these dates except two trading days before, the day of and two trading days after earnings announcements as well as days with management earnings forecasts or dividend announcements.7 The non-announcement periods for announcements between July 1, 1994 and June 30, 1995, and announcements between July 1, 1995 and June 30, 1996 are defined in the same way.For each half-hour interval, the time weighted quoted bid-ask spread and depth, and the average effective bid-ask spread are calculated for each firm, separately. In these computations, the effective spread is defined as two times the absolute value of the difference between the trade price and the quote midpoint (average of the bid and ask price).8 The depth equals the number of shares the specialist is willing to buy plus the shares (s)he is willing to sell, simultaneously.6 Different from other announcements, the period after an announcement made between 9.30 AM and 10.00 AM does not include the opening of the market after a non-trading period after the announcement. The results are, however, robust to excluding these observations.7 The Dow Jones Interactive database is used to investigate the Business Wire, Dow Jones News Service, Dow Jones International News and PR Newswire for management earnings forecasts and dividend announcements.8 Following Lee and Ready (1991), I delay quotes five seconds relative to transactions to reduce time stamping errors in the data.The percentage deviation in the quoted spread after an earnings announcement from the median spread during the non-announcement period is calculated as follows. First, I average the time weighted quoted spreads during the period after the earnings announcement (Spread after in FIGURE 2). Next, I divide the non-announcement period into similar periods of 13 half-hour trading intervals each (A, B, C, D etcetera). Thus, when the period after the earnings announcement ranges from 10.00 AM the day of the announcement to 10.00 AM the day after the announcement, the non-announcement period is divided in periods from 10.00 AM day 1 to 10.00 AM day 2, 10.00 AM day 2 to 10.00 AM day 3 etcetera. For each period, I average the time weighted quoted spreads during the 13 half-hour trading intervals during that period (Spread A, B, C, D). For each announcement this approach generates one average quoted spread after the announcement and several average quoted spreads during the non-announcement period. I determine for each announcement the median of the average quoted spreads during the non-announcement period (Median spread non-announcement period). Finally, I divide the difference between the quoted spread after the announcement and the median quoted spread during the non-announcement period by this median quoted spread and multiply it by 100. The median is used instead of the mean of the average quoted spreads during the non-announcement period because the distribution of these quoted spreads is skewed. The percentage deviations in the effective spread and depth after earnings announcements are calculated in a comparable way.Insert FIGURE 2In addition to an analysis of the spread and depth during a full trading day after the earnings announcement, I also investigate these variables for 13 half-hour trading intervals after the announcement, separately. To compute the percentage deviation in the quoted spread for a certain half-hour interval, I determine the difference between the spread during the half-hour interval after the announcement and the median spread during the same half-hour interval during the non-announcement period. For example, if the half-hour interval after theannouncement ranges from 10.30 AM to 11.00 AM, I compute the median of the time weighted quoted spreads during the 10.30 AM - 11.00 AM intervals during the non-announcement period. Because I compare spreads during the same half-hour interval, this approach controls for the intraday pattern of spreads and depths. Next, I divide the difference between the spread after the announcement and the median spread during the non-announcement period by the median daily spread during the non-announcement period. The daily spread is used instead of the half-hour spread to prevent that the intraday pattern affects the denominator and makes the comparison between announcements at different times of the day impossible.9 The percentage deviation in the depth is calculated in a similar way.3.3 Content of the earnings announcementsIt is important to control for the information content of the earnings announcements. The content could be different for daytime and overnight announcements, because managers decide on the timing of the announcement and they may condition their decision on the earnings news. Patell and Wolfson (1982) and Francis et al (1992), for instance, argue that overnight releases contain more bad news and bigger surprises than daytime announcements. They present empirical evidence that is in line with these conjectures. Thus, managers disseminate more informative releases when individual investors cannot trade. If overnight announcements contain larger surprises, the uncertainty in the capital market will be larger after these announcements than after daytime announcements. Therefore, the difference in the9 Lee et al (1993) show that during trading days without earnings announcements the quoted spread has a U-shaped pattern with relatively wide spreads during the half-hour after opening and the half-hour before closing of the market. The depth shows an opposite pattern. For overnight announcements the spread during the first trading half-hour after the announcement (9.30 AM-10.00 AM) has, therefore, relatively wide spreads during the non-announcement period. However, if a daytime announcement is, for example, made at 1.10 PM the first trading half-hour (1.00 PM-1.30 PM) has relatively small spreads during the non-announcement period. If the difference between the spread after the announcement and spread during the non-announcement period is divided by the spread during the half-hour period during the non-announcement period, the intraday pattern would influence the results. Dividing by the daily spread, however, controls for cross-sectional differences in the level of the spread, while the result is not affected by the intraday pattern.information content will, ceteris paribus, result in a larger (smaller) deviation in the spread (depth) after overnight announcements compared to daytime announcements.I include the percentage deviation in volume (∆volume i), the absolute price change (|∆price|i) and a good news dummy (Good news i) to control for the information content of the earnings news. I expect a positive (negative) relation between the absolute price change and the deviation in the quoted and effective spread (depth). A larger price change indicates a more informative earnings announcement and this will likely increase the probability of informed trading. For the same reason, high trading volume could relate to wide spreads and low depths. Alternatively, an opposite relation between the deviation in trading volume and the deviation in the spread and depth can be argued. For example, Demsetz (1968), Tinic (1972), and Tinic and West (1972) document a negative correlation between the level of the spread and trading volume during a non-announcement period. Their argument is that the order processing costs and inventory holding costs per share decrease when trading volume increases. Therefore, an increase in trading volume could relate to a decrease in spreads. Depths may be positively correlated with trading volume, because it requires many trades to trade a large amount of shares if the depth is low. The specialist may, therefore, increase the depth to limit transaction costs if trading volume increases.For overnight announcements, the absolute price change equals the absolute value of the quote midpoint at the closing of the market after the release minus the quote midpoint at the closing of the market before the release divided by the quote midpoint before the release. For daytime announcements, I calculate the absolute value of the relative change between the quote midpoint at the beginning of the half-hour containing the announcement and the quote midpoint at the same time one trading day later. The good news dummy is 1 if the price change after the earnings announcement is larger than zero and 0 otherwise. The percentage deviation in volume is computed in a similar way as the percentage deviation in the quoted。