CMP_slurry_partilces_Market

合集下载

国际金融市场

• Momentum Trading
6
外匯市場(三)：避險與交易策略
三、波動度策略 (Volatility Strategies)
• Dynamic Hedging
1. Synthetic Put 2. Portfolio Insurance 3. VaR-based Hedging 4. ES-based Hedging
外匯（三）：匯率避險與交易--價值型策略、趨勢型策略
**（溫書假）外匯（三）：匯率避險與交易--波動度策略外匯（四）：外匯衍生性商品之發展與運作債券（一）：全球債券市場概況債券（二）：投資工具訂價分析債券（三）：殖利率曲線與投資策略債券（四）：資產配置理論、績效評估總複習期末考
3
外匯(一)：全球外匯市場概況
99學年度第二學期
台大國企系所
國際金融市場
中央銀行外匯局賀蘭芝研究員
課程大綱
科目名稱系所 / 年級學分數 / 授課時間教師 / 聯絡方式課程目標國際金融市場台大國企系所 / 大二~研二 2學分 / 星期三 18:30-21:20 (每三週上兩次) 賀蘭芝 / LCHO@.tw 了解國際金融市場與金融工具之運作實務
• Yield Curve Building and Fitting Bootstrapping Spline Fitting Parsimonious Fitting • Yield Curve Trading Strategies Slope Trading Curvature Trading Relative Trading Riding on the Curve
5
外匯市場(三)：避險與交易策略
一、價值型策略 (Value Strategies)

Consumer Perceptions of Price, Quality, and Value

Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of EvidenceAuthor(s): Valarie A. ZeithamlSource: The Journal of Marketing, Vol. 52, No. 3 (Jul., 1988), pp. 2-22Published by: American Marketing AssociationStable URL: /stable/1251446Accessed: 21/05/2010 10:20Your 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/action/showPublisher?publisherCode=ama.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@.American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access to TheJournal of Marketing.。

市场结构与管理决策1

比较 R(q) and C(q)
• 产出水平
– q0 - q*: R(q)> C(q) – MR > MC
• 表明在更高的产量有较高的利润
Cost, Revenue,
Profit $ (per year)
–
(q) increasing
0 q0
C(q)
A
R(q)
B
q*
(q)
Output (units per year)
A
R(q)
B
0 q0
q*
(q)
Output (units per year)
利润最大化
因而，我们可以说:
利润最大化条件： MC = MR.
Cost, Revenue,
Profit $ (per year)
C(q)
A
R(q)
B
0 q0
q*
(q)
Output (units per year)
利润最大化
比较 R(q) and C(q) • 产出水平
– q*: R(q) C(q) – MR = MC
– (q) maximized
Cost, Revenue,
Profit $ (per year)
C(q)
A
R(q)
B
0 q0
q*
(q)
Output (units per year)
AC
Pmin
AVC
短期停工点
0
Q
短期产出的选择
生产决策小结
1. is maximized when MC MR 2. If P ATC the firm is making 's.

一次鸟枪换炮的革命：用机器横扫传统,从量化的角度深度剖析外汇交易

一次鸟枪换炮的革命：用机器横扫传统，从量化的角度深度剖析外汇交易文| 用Python的交易员@知乎来源| 汇眼网，ID：Forexeye编辑| 扑克投资家，转载请注明出处导读：量化交易的概念在近两年越来越热，量化与外汇的结合也是最近外汇交易者和爱好者们探索的话题，这次有幸请到了知乎上的量化交易专家“用Python的交易员”为大家解惑如何用量化的方式做外汇交易。

“用Python的交易员”是金融工程学硕士，毕业于伦敦卡斯商学院，拥有多年私募基金和量化投资从业经验，日常工作中每天负责期权波动率套利、CTA、价差套利等量化策略的实盘交易和研发，目前参与管理的产品规模在数亿级别。

开发维护了一款针对国内市场的开源量化交易平台开发框架vn.py，目前是国内用户最多的量化金融开源项目之一（Github Star 1341），业内目前有几十家私募公司在实盘使用。

感谢大家参与今天的见识会，今天的主要内容是从量化的角度做外汇交易，我本人是从外汇交易起步入门才会选择了走金融这条路，本来大学的志愿是化学，但是因为交易引发了兴趣才转成了金融，硕士选择了金融工程方向并且在英国工作了一段时间，可以说我的金融之路是从外汇和MT4开始的。

确实在过去的十年中，外汇行业迎来了翻天覆地的发展，但是也注意到现在国内外汇的交易员还是主要在采用主观、手工的交易方式，或者相对比较简单的MT4上的EA交易，我今天首先给大家介绍在国际市场上比较主流的量化交易方法的分类。

在这张PPT里面，我把外汇领域的策略大体分为三类，分别是量化宏观、CTA、高频交易三类，这也是是对策略的具体持仓周期的区分，量化宏观对应的是超长期或者长期、CTA 对应的中期或者短期，高频交易对应的短期或者超短期。

第一种类型就是量化宏观，如果大家对对冲基金行业有所了解呢会知道基金业有一种大类的策略叫做全球宏观（Global Macro），这种策略主要是通过对全球经济宏观面的预测，从上至下的方式来筛选并进行外汇、期货、股票等标的的交易实现该策略的交易目标。

基于cmp估计的边际效应的stata命令

基于cmp估计的边际效应的stata命令在Stata中，我们可以使用cmp命令来估计基于cmp估计的边际效应。

cmp命令是一个用于横截面混合数据（cross-sectional mixed data）模型的命令，它可以用于估计二分类或多分类模型中的边际效应。

在这个命令中，我们可以使用两个子命令：estat effects和margins。

我们需要使用cmp命令来拟合我们的模型。

假设我们想估计一个二分类模型，我们可以使用以下命令：cmp（dependent_variable explanatory_variables1/explanatory_variables 2）, model(binary)在这个命令中，dependent_variable是我们的因变量，explanatory_variables 1是第一个二分类变量的自变量，explanatory_variables 2是第二个二分类变量的自变量。

一旦我们估计了模型，我们可以使用estat effects子命令来计算边际效应。

这个子命令可以为每个二分类变量计算两个效应：平均效应和个体效应。

平均效应是指将所有个体的自变量设置为其平均值时的效应，而个体效应是指将个体的自变量设置为其观测值时的效应。

以下是一个使用estat effects子命令的例子：estat effects, margins(atmeans) atexposure(exp=1) expression（expression_list）在这个命令中，margins(atmeans)表示计算平均效应，atexposure(exp=1)表示将自变量设置为指定的值，expression （expression_list）是需要计算的效应表达式。

通过estat effects子命令计算出来的效应是以对数概率的形式呈现的。

如果我们想将其转化为概率形式，我们可以使用margins子命令。

以下是一个使用margins子命令的例子：margins, dydx(explanatory_variable 1)at(explanatory_variable 1=(0 1))在这个命令中，dydx(explanatory_variable 1)表示计算explanatory_variable 1的边际效应，at(explanatory_variable1=(0 1))表示计算在explanatory_variable 1等于0和1时的边际效应。

Insider Trading and the Bid-Ask Spread_ A Critical Evaluation of Adverse Selection in Market Making

INSIDER TRADING AND THE BID-ASK SPREAD: A CRITICAL EVALUATION OF ADVERSE SELECTION INMARKET MAKINGS TANISLAV D OLGOPOLOV*In economic, finance, and legal literature, there is a widespread acceptance of the notion that market makers increase the bid-ask spread in response to insider trading, as they consistently lose money by transacting with better-informed insiders. The development of this adverse selection model of market making was treated as proof that insider trading imposes a real cost on securities markets by decreasing liquidity and increasing the corporate cost of capital and was used as a justification for regulation. This Article is a critical review of the adverse selection literature. It discusses the model’s theoretical development, its use in the regulation debates, a summary of the case law on the harm from insider trading to market makers, and empirical research on the link between insider trading and transaction costs. The adverse selection argument is criticized from both theoretical and empirical standpoints: there are limitations to the model due to required assumptions about the role and behavior of market makers’ inventories; different causal links among insider trading, firm size, quality of disclosure, stock price volatility, and the bid-ask spread are possible; the existing empirical studies may confuse various components of the spread; and information asymmetry may actually benefit market makers.I.I NTRODUCTIONA. Insider Trading ControversyThe issue of insider trading1 has never disappeared from academic and public policy debates during the past four decades,2 and this practice has _______________________________________________________ Copyright © 2004, Stanislav Dolgopolov.*Empire Education Corporation (Latham, NY) and the John M. Olin Center for Law and Economics at the University of Michigan Law School (Ann Arbor, MI). The author thanks Henry G. Manne for suggesting the topic and for his guidance and Faith A. Takes for her encouragement. The author also gratefully acknowledges the valuable comments and help of Omri Ben-Shahar, Laura N. Beny, Laurence D. Connor, Vladislav Dolgopolov, Jon Garfinkel, Zohar Goshen, David R. Henderson,David Humphreville, Kjell Henry Knivsflå, Leonard P. Liggio, Edith Livermore, John Moore, John Papadopoulos, Paula Payton, David S. Ruder, Daniel F. Spulber, Michael Trebilcock, and Martin Young, as well as the Atlas Economic Research Foundation, the Earhart Foundation, and the John M. Olin Center for Law and Economics at the University of Michigan Law School.1“Insider trading” refers to transactions in company’s securities by corporate insiders (such as executives, directors, large shareholders, and outside persons with privileged access to corporate affairs) or their associates based on information originating(continued)84 CAPITAL UNIVERSITY LAW REVIEW [33:83 attracted a great deal of publicity and near-universal condemnation.3 Recently, and in the wake of the stock market decline and numerous corporate scandals, insider trading, treated as one of the chief symptoms of the business world’s corruption, once again captured public attention.4within the firm that would, once publicly disclosed, affect the prices of such securities. The definition of “informed trading” is broader than “insider trading” because the former also includes transactions on the basis of “market” or “outside” information, such as the knowledge of forthcoming market-wide or industry developments, competitors’ strategies and products, or upcoming takeovers by a third party. There are arguments for regulating the use of external information as well: “The traditional fairness and market integrity bases for regulating insider trading are still important to uphold when market information is involved.” Committee on Federal Regulation of Securities, Report of the Task Force on Regulation of Insider Trading, Part I: Regulation Under the Antifraud Provisions of the Securities Exchange Act of 1934, 41 B US.L AW. 223, 229 (1985). Indeed, the use of such information is, in some instances, covered by federal securities regulations. See John F. Barry III, The Economics of Outside Information and Rule 10b-5, 129 U.P A.L.R EV. 1307, 1308-09 (1981).2See Paula J. Dalley, From Horse Trading to Insider Trading: The Historical Antecedents of the Insider Trading Debate, 39 W M.&M ARY L.R EV. 1289 (1998) (discussing earlier controversies pertaining to the duty to disclose in transactions between asymmetrically informed parties). One of the earliest, and unsuccessful, attempts to regulate insider trading on the federal level occurred after the 1912-13 congressional hearings before the Pujo Committee, which concluded that “[t]he scandalous practices of officers and directors in speculating upon inside and advance information as to the action of their corporations may be curtailed if not stopped.” H.R. R EP.N O.62-1593,at 115 (1913).3Insider trading is quite different from market manipulation, false disclosure, or direct expropriation of the company’s wealth by corporate insiders, and trading on asymmetric information is common in many other markets. Nevertheless, insider trading seems objectionable for many reasons. First, corporate employees as “agents” owe fiduciary duties to shareholders as their “principals.” Second, “unfairness” results from trading on information obtained as a byproduct of employment or privileged access to corporate affairs. Third, insider trading is objectionable because of the extent of managerial control over the production, disclosure, and access to inside information, which may give rise to arbitrary, costless, and non-transparent wealth transfers from outside investors to managers. Fourth, insider trading may lead to possible conflicts between maximizing insiders’ trading profits and maximizing the firm’s value. These concerns are very much unique to securities markets. See generally Victor Brudney, Insiders, Outsiders, and Informational Advantages Under the Federal Securities Laws, 93 H ARV.L.R EV. 322 (1979).4In fact, one empirical study posits that selling by corporate insiders after the expiration of lockup provisions was one of the most important immediate factors that led to the New Economy market burst. Eli Ofek & Matthew Richardson, DotCom Mania: The Rise and Fall of Internet Stock Prices, 58 J. F IN. 1113, 1131 (2003). While this study does(continued)2004] INSIDER TRADING AND THE BID-ASK SPREAD 85 Academic analysis has considered insider trading from the perspectives of such diverse disciplines as economics,5 ethics,6 feminist studies,7 and psychology.8 It has been hailed as a mechanism of enhancing stock price accuracy and an efficient compensation scheme for entrepreneurial services,9 a stimulus of producing information at a low cost,10 compensation for undiversified risk for controlling shareholders,11 a reward to blockholders for their monitoring activities,12 a device mitigating agency costs,13 and a mechanism of credible signaling to the market.14 not suggest that insider selling by itself led to the market crash, the implication is that, in many instances, the outside investors, not the insiders, largely absorbed the loss. See also Mark Gimein, You Bought. They Sold., F ORTUNE, Sept. 2, 2002, at 64 (documenting massive insider selling in such companies as Enron, Global Crossing, Tyco, and others before the sharp drop in their shares’ prices).5See generally H ENRY G.M ANNE,I NSIDER T RADING AND THE S TOCK M ARKET (1966); Javier Estrada, Insider Trading: Regulation, Securities Markets, and Welfare Under Risk Aversion, 35 Q.R EV.E CON.&F IN. 421 (1995); Norman S. Douglas, Insider Trading: The Case Against the “Victimless Crime” Hypothesis, F IN.R EV., May 1988, at 127.6See generally Gary Lawson, The Ethics of Insider Trading, 11H ARV.J.L.&P UB.P OL’Y 727 (1988); Ian B. Lee, Fairness and Insider Trading, 2002 C OLUM.B US.L.R EV. 119; Kim Lane Scheppele, “It’s Just Not Right”: The Ethics of Insider Trading, 56 L AW &C ONTEMP.P ROBS. 123 (1993).7See generally Theresa A. Gabaldon, Assumptions About Relationships Reflected in the Federal Securities Laws, 17 W IS.W OMEN’S L.J. 215 (2002); Judith G. Greenberg, Insider Trading and Family Values, 4 W M.&M ARY J.W OMEN &L. 303 (1998).8See generally John Dunkelberg & Debra Ragin Jessup, So Then Why Did You Do It?, 29 J. B US.E THICS 51 (2001); David E. Terpstra et al., The Influence of Personality and Demographic Variables on Ethical Decisions Related to Insider Trading, 127 J. P SYCHOL. 375 (1993).9See M ANNE, supra note 5, at 81-90, 131-58 (discussing the “smoothing” effect of insider trading on the stock price and arguing that insider trading constitutes efficient compensation for entrepreneurial services rendered to the corporation).10See David D. Haddock & Jonathan R. Macey, Regulation on Demand: A Private Interest Model, with an Application to Insider Trading Regulation, 30 J.L.&E CON. 311, 318 (1987) (arguing that “insiders are the low-cost suppliers of most of the [firm-specific] information that is useful to securities markets”).11See Harold Demsetz, Corporate Control, Insider Trading, and Rates of Return, 76 A M.E CON.R EV. (P APERS &P ROC.)313, 315 (1986).12See Stephen Thurber, The Insider Trading Compensation Contract as an Inducement to Monitoring by the Institutional Investor, 1 G EO.M ASON L.R EV. (n.s.) 119, 119 (1994).13See Dennis W. Carlton & Daniel R. Fischel, The Regulation of Insider Trading, 35 S TAN.L.R EV.857,870-71(1983)(discussing how insider trading may align the interests of shareholders and managers).86 CAPITAL UNIVERSITY LAW REVIEW [33:83 Insider trading has also been condemned on the grounds that it may reduce investor confidence in securities markets,15 create perverse incentives for management,16 constitute a misappropriation of information and wealth,17 interfere with timely disclosure and the flow of information inside firms,18 adversely affect the process of gathering and disseminating information by14See id. at 868 (discussing how insider trading “gives the firm an additional method of communicating and controlling information”).15See Lawrence M. Ausubel, Insider Trading in a Rational Expectations Economy, 80 A M.E CON.R EV. 1022, 1022-23 (1990) (asserting that insider trading deters potential investors from securities markets, as outsiders want to avoid dilution of their investment returns); Louis Loss, The Fiduciary Concept as Applied to Trading by Corporate “Insiders” in the United States, 33 M OD.L.R EV. 34, 36 (1970) (arguing that insider trading constitutes a “grievous insult to the market in the sense that the very preservation of any capital market depends on liquidity, which rests in turn on the investor’s confidence that current quotations accurately reflect the objective value of his investment”).16See Frank H. Easterbrook, Insider Trading, Secret Agents, Evidentiary Privileges, and the Production of Information, 1981 S UP.C T.R EV. 309, 332-33; David Ferber, The Case Against Insider Trading: A Response to Professor Manne, 23 V AND.L.R EV. 621, 623 (1970).17See A DOLF A.B ERLE J R.&G ARDINER C.M EANS,T HE M ODERN C ORPORATION AND P RIVATE P ROPERTY 326 (1932) (arguing that inside information “accordingly belongs in equity to the body of shareholders as a whole”); R OBERT C HARLES C LARK,C ORPORATE L AW 273-74 (1986) (arguing that “the amount of the value of new developments unilaterally appropriated by the insiders from the outsiders could be an enormous portion of the total”); James D. Cox, Insider Trading and Contracting: A Critical Response to the “Chicago School,” 1986 D UKE L.J. 628, 651 (pointing out that “a firm wishing to consider alternative dispositions of inside information [for profit] could rightly see that such uses must foreclose trading by its managers”).18See O LIVER E.W ILLIAMSON,C ORPORATE C ONTROL AND B USINESS B EHAVIOR:A N I NQUIRY INTO THE E FFECTS OF O RGANIZATION F ORM ON E NTERPRISE B EHAVIOR 95 (1970) (arguing that insider trading may lead to “information hoarding”); Robert J. Haft, The Effect of Insider Trading Rules on the Internal Efficiency of the Large Corporation, 80 M ICH.L. R EV. 1051, 1052 (1982).2004] INSIDER TRADING AND THE BID-ASK SPREAD 87 outsiders,19 provoke conflicts among groups of shareholders,20 and increase the corporate cost of capital.21B. New Argument for Regulating Insider TradingThe proponents of deregulating insider trading succeeded in attracting the attention of academia and government agencies to their economics-based methodology. As a result, the emphasis of the pro-regulators has shifted from the issue of fairness to the search for economic costs of insider trading.22_______________________________________________________ 19See Michael J. Fishman & Kathleen M. Hagerty, Insider Trading and the Efficiency of Stock Prices, 23 R AND J.E CON. 106, 107 (1992); Naveen Khanna, Why Both Insider Trading and Non-Mandatory Disclosures Should Be Prohibited, 18 M ANAGERIAL & D ECISION E CON. 667, 668 (1997).20See Oliver Kim, Disagreements Among Shareholders over a Firm’s Disclosure Policy, 48 J. F IN. 747, 748 (1993); Ernst Maug, Insider Trading Legislation and Corporate Governance, 46 E UR.E CON.R EV. 1569, 1570 (2002).21See David Easley et al., Is Information Risk a Determinant of Asset Returns?, 57 J. F IN. 2185, 2219 (2002); Morris Mendelson, The Economics of Insider Trading Reconsidered, 117 U. P A.L.R EV. 470, 477-78 (1969) (reviewing M ANNE,supra note 5).22Many works concentrate on managerial incentives and consider whether insider trading, on one extreme, constitutes non-transparent rents detrimental to the corporation or, on the other hand, an efficient form of compensation taken into account in determining the total reward package. See Carlton & Fischel, supra note 13,at 870-71; Ronald A. Dye, Inside Trading and Incentives, 57 J. B US. 295 (1984); Neelam Jain & Leonard J. Mirman, Real and Financial Effects of Insider Trading with Correlated Signals, 16 E CON.T HEORY 333, 340 (2000); Ranga Narayanan, Information Production, Insider Trading, and the Role of Managerial Compensation, F IN.R EV., Nov. 1999, at 119. The “pro-insider trading” literature posits that allowing managers to trade on inside information is likely to create beneficial incentives for them. See, e.g., M ANNE, supra note 5, at 138-39, 150 (inducing managers to pursue innovation and repeatedly generate “good news”); Carlton & Fischel, supra note 13, at 870-72 (encouraging managers to discover and develop valuable information, economize on compensation renegotiation costs, and signal their willingness to pursue risky projects favorable to diversified shareholders); Guochang Zhang, Regulated Managerial Insider Trading as a Mechanism to Facilitate Shareholder Control, 28 J. B US.F IN.&A CCT. 35, 36 (2001) (inducing managers to provide shareholders with accurate information). Their opponents contend that insider trading may create perverse incentives for managers. See, e.g., S TEPHEN M.B AINBRIDGE,C ORPORATION L AW AND E CONOMICS 593 (2002) (encouraging managers to publicize information prematurely); C LARK, supra note 17, at 273-74 (unilaterally altering managers’ compensation package agreements); Cox, supra note 17, at 651-52 (increasing managers’ tolerance of bad performance); Boyd Kimball Dyer, Economic Analysis, Insider Trading, and Game Markets, 1992 U TAH L.R EV. 1, 21-22 (encouraging managers to spread rumors and devote too much effort to gaining access to information for trading purposes); Easterbrook, supra note 16, at 332(continued)88 CAPITAL UNIVERSITY LAW REVIEW [33:83One such cost was pointed out by economists—and utilized by legal academics and regulatory agencies to justify the existence of regulation—when some works in market microstructure23 proposed that insider trading harms market liquidity due to its adverse effect on market makers—specialists or dealers that provide liquidity on an organized exchange or an over-the-counter (OTC) market.24 This was an attempt to satisfy the criterion advanced by Henry G. Manne: “Ultimately the complaint must be that some individuals are being harmed by allowing insider trading. It is not enough simply to say that insider trading is unfair. If it is unfair, it must be unfair to somebody.”25The argument is that insider trading increases the bid-ask spread—the difference between the market maker’s “sell” and “buy” prices26—thereby (encouraging managers to engage in excessively risky projects and increase stock price volatility); Haft, supra note 18, at 1054-55 (discouraging internal information-sharing); Roy A. Schotland, Unsafe at Any Price: A Reply to Manne, Insider Trading and the Stock Market, 53 V A.L.R EV. 1425, 1448-50 (1967) (encouraging managers to delay disclosure and engage in market manipulation). See also Darren T. Roulstone, The Relation Between Insider-Trading Restrictions and Executive Compensation, 41 J. A CCT.R ES. 525, 548-49 (2001) (offering empirical evidence suggesting that higher potential profits from legal insider trading are associated with lower explicit executive compensation).23Market microstructure, as a field of financial economics, studies trading rules and mechanisms, price discovery, and transaction costs. See generally M AUREEN O’H ARA, M ARKET M ICROSTRUCTURE T HEORY (1995);L ARRY H ARRIS,T RADING AND E XCHANGES: M ARKET M ICROSTRUCTURE FOR P RACTITIONERS (2003); D ANIEL F.S PULBER,M ARKET M ICROSTRUCTURE:I NTERMEDIARIES AND THE T HEORY OF THE F IRM (1999); Ananth Madhavan, Market Microstructure: A Survey, 3 J. F IN.M ARKETS 205 (2000); Hans R. Stoll, Market Microstructure, in 1A H ANDBOOK OF THE E CONOMICS OF F INANCE 553 (George Constantinides et al. eds., 2003).24See H ARRIS,supra note 23, at 286-91.25M ANNE, supra note 5, at 93.26The bid-ask spread as such does not constitute a “regrettable” friction for securities markets. Rather, it is a compensation for a very important economic service. One of the earliest works on market making noted that “the jobber’s turn [the spread] represents the price paid by the community for the invaluable privilege of close prices and a continuously free market for securities.” F.E. Steele, The ‘Middleman’ in Finance, 5 E CON. J. 424, 431 (1895). There are three basic measures of the bid-ask spread. See Roger D. Huang & Hans R. Stoll, Dealer Versus Auction Markets: A Paired Comparison of Execution Costs on NASDAQ and the NYSE, 41 J. F IN.E CON. 313, 322-28 (1996). See also Mitchell A. Peterson & David Fialkowski, Posted Versus Effective Spreads: Good Prices or Bad Quotes?, 35 J. F IN.E CON. 269 (1994) (discussing the magnitude of the difference between various spread measures). First, the quoted spread is the difference between the bid and ask prices quoted simultaneously. See Huang & Stoll, supra, at 322. Second, the effective spread is the difference between the actual bid and ask prices executed at the same(continued)2004] INSIDER TRADING AND THE BID-ASK SPREAD 89 increasing the costs of transacting. The importance of the spread is that it represents the “price for immediacy”27 and the “cost of trading and the illiquidity of a market.”28The adverse selection model analyzes interaction of a market maker with informed and uninformed traders.29 Because providers of liquidity, unable to distinguish among types of traders, are always “losing” on trades with better-informed counterparties,30 they must charge everyone a higher bid-ask spread to compensate for their losses31 and still enter into time, as many transactions occur inside the quoted spread. See id. at 324. Finally, the realized spread is the difference between the actual bid and ask prices for trades separated by a specified period of time, which represents a profit or loss of a liquidity provider in the course of transacting at the initial and subsequent prices. See id. at 326-27. In the presence of multiple market makers, the “best” bid-ask spread and quotes are also known as “inside” or “market.”27 HaroldThe Cost of Transacting, 82 Q.J.E CON.33,35-36. “Predictable Demsetz,immediacy . . . requires that costs be borne by persons who specialize in standing ready and waiting to trade with the incoming orders of those who demand immediate servicing of their orders. The bid-ask spread is the markup that is paid for [that] predictable immediacy.” Id. The role of market makers as providers of immediacy was recognized much earlier because without such intermediaries, “buyers desirous of buying at once, and sellers anxious for immediate realization, would have to make considerable sacrifices in the matter of price [and f]luctuations in prices would thus occur with greater frequency and greater violence, and the element of pure speculation and uncertainty . . . would be still further increased.” Steele, supra note 26, at 431. See also George J. Stigler, Public Regulation of the Securities Markets, 37 J. B US. 117, 129 n.16 (1964) (finding that the bid-ask spread represents the price paid for “(1) immediate availability of a buyer or seller; (2) the elimination of short run fluctuations in price”). The London “stock jobbers” in the late 18th – early 19th centuries were one of the first historical examples of specialized intermediaries continuously buying and selling securities to profit from the price differential. See S.R. Cope, The Stock Exchange Revisited: A New Look at the Market in Securities in London in the Eighteenth Century, 45 E CONOMICA 1, 5-8 (1978).28 Stoll,supra note 23, at 562.29Informed traders possess material nonpublic “inside” or “outside” information or enjoy superior abilities in data gathering and processing. In contrast, uninformed traders transact to consume or save, to readjust their portfolios, to act on “noise” and diverging expectations, and to make speculative bets. See M ANNE, supra note 5, at 84-86; Fischer Black, Noise, 41 J. F IN. 529 (1986).30See O’H ARA,supra note 23, at 54.31See id. A variant of the adverse selection model states that market makers also reduce market liquidity by decreasing the market depth—the amounts of shares offered by a market maker at his bid and ask prices—in order to limit their exposure to the risk of incurring losses while trading with better-informed persons. See infra notes 474-79 and accompanying text.90 CAPITAL UNIVERSITY LAW REVIEW [33:83 some “adverse” transactions.32 Furthermore, insider trading is said to impose a social loss: securities prices are discounted due to higher transaction costs,33 and some potential investors refrain from participating in such markets.34Thus, the case for regulating insider trading was alleged: “[Informed] trades can damage the dealer, perhaps fatally. That’s a valid reason for discouraging trading on so-called ‘inside’ information, quite apart from whether such trading entails misappropriation of corporate property or wire fraud.”35 Similarly, a leading legal academic has remarked that “the more that the law successfully prohibits the use of non-public information, the more that the market maker can (and will be forced by competitive pressure to) narrow the bid-ask spread.”36The adverse selection argument is not concerned with the “unfairness” of trading on inside information or with wealth transfers from uninformed to informed traders.37 Rather, it points out an economic cost of insider _______________________________________________________ (continued )32 See O’H ARA , supra note 23, at 54. In the context of market microstructure, an alternative meaning of “adverse selection” refers to the notion that limit orders tend to be executed at times when the market moves against them, leading to transactions that are unfavorable in light of the new market conditions. See David K. Whitcomb, Applied Market Microstructure , J. A PPLIED F IN ., Fall–Winter 2003, at 77, 78.33 See infra note 99 and accompanying text.34 See infra note 91 and accompanying text.35 Jack L. Treynor, Securities Law and Public Policy , F IN . A NALYSTS J., May–June 1994, at 10, 10.36 John C. Coffee, Jr., Is Selective Disclosure Now Lawful?, N.Y. L.J., July 31, 1997, at 5.37 The wealth redistribution argument states that trading on asymmetric information is a zero-sum game. But the fact of insider trading does not induce most individual transactions of outsiders with insiders. See Henry G. Manne, Insider Trading and the Law Professors , 23 V AND . L. R EV . 547, 551-53 (1970); Jack M. Whitney II, Section 10b-5: From Cady, Roberts to Texas Gulf: Matters of Disclosure , 21 B US . L AW . 193, 201-04 (1965). The U.S. Supreme Court reached a similar situation in Dirks v. SEC , 463 U.S. 646 (1983), which held that “in many cases there may be no clear causal connection between inside trading and outsiders’ losses. In one sense, as market values fluctuate and investors act on inevitably incomplete or incorrect information, there always are winners and losers.” Id . at 667 n.27. Even the U.S. Securities and Exchange Commission (SEC) officials admitted that “[w]ith respect to equities trading, it may well be true that public shareholders’ transactions would have taken place whether or not an insider was unlawfully in the market.” Thomas C. Newkirk & Melissa A. Robertson, Remarks at the Sixteenth International Symposium on Economic Crime (Sept. 19, 1998), available at /news/speech/speecharchive/1998/spch221.htm (last visited Jan. 11, 2005). However, even though an uninformed trader transacting directly with an insider in an impersonal market is unlikely to suffer a loss, compared to a hypothetical with no insider2004] INSIDER TRADING AND THE BID-ASK SPREAD 91 trading: a higher bid-ask spread and a corresponding decrease in market liquidity. A wealth of empirical evidence is cited in support of this theory. Yet the model does not attempt to describe a general equilibrium in securities markets. Indeed, the argument is quite elegant and simplified, as one would justly expect from an economic model.C. Article’s Scope of AnalysisThis Article reviews the adverse selection literature, discussing the development of the model and its utilization by the legal academics and the regulators; the analysis of assumptions concerning market makers’ inventories; comparative analysis of the specialist and dealer systems; detection of informed trading by market makers; the correlation and possible theoretical links among the spread, quality of disclosure, insider trading, rate of return, stock price volatility, trading volume, and firm size; the overall effect of information asymmetry on providers of liquidity; informed trading and market making in derivatives; the relevance of the adverse selection argument to the practices of “cream-skimming” and trading in otherwise identical circumstances, the fact of insider trading induces or preempts some other marginal transaction and thus causes a loss or deprives of a potential gain. See W ILLIAM K.S.W ANG &M ARK S TEINBERG,I NSIDER T RADING 62-105 (1996) (discussing the “Law of Conservation of Securities”); Henry G. Manne, In Defense of Insider Trading, H ARV.B US.R EV., Nov.–Dec. 1966, at 113, 114-15 (arguing that insider trading induces unfavorable transactions of short-term traders—not necessarily those who trade directly with insiders). It should be noted that long-term shareholders are rarely adversely affected by insider trading, as there is a lower chance that trading on private information would affect their trading pattern. See M ANNE, supra note 5, at 102, 107. But the same argument is still revived, maintaining that uninformed traders are always disadvantaged: “In bad times, this disadvantage can result in the uninformed trader’s portfolio holding too much of the stock; in good times, the trader’s portfolio has too little . . . . Holding many stocks cannot remove this effect because the uninformed do not know the proper weights of each asset to hold.” Easley et al., supra note 21, at 2218-19. However, the described harm comes not from insider trading, but from the lack of instantaneous disclosure of all material information, which is likely to be harmful to corporate operations. Insider trading is also likely to redistribute wealth among outsiders, benefiting some of them due to its effect on the market price and trading patterns. See M ANNE, supra note 5, at 93-110; W ANG & S TEINBERG, supra, at 64. Some argue that even abstaining from trading on inside information would yield abnormal profits. See Jesse M. Fried, Insider Abstention,113 Y ALE L.J.455, 463 n.29, 465 & nn.35-37 (2003) (citing various scholars supporting this point of view). However, others question the validity of this proposition and the magnitude of such profits. See id. at 466-67 (arguing that “the insider’s ability to abstain on nonpublic information indicating that a planned trade would be unfavorable merely compensates the insider for her inability to proceed with a trade after learning nonpublic information indicating that the planned trade would be favorable”).。

CMP_slurry_partilces_Market

KHolland@
Tungsten
IC CMP Slurry Suppliers
High growth, but less volume than Cu polish Cu Barrier Customers’ priorities similar 1 major >40% New slurries for each 4 others >10% new generation due to low k dielectrics?
AVS CMP-UG 07/2009 6
Volume % Colloidal
KHolland@Techceຫໍສະໝຸດ
Abrasive and Slurry Suppliers
Numerous Suppliers of Slurry (>21)
Abrasives only (>15) Slurries only (>15) Both Slurry and Abrasives (>6)
Oxide
2 major suppliers, >30% each
Cu Step 1
3 suppliers split 75% 2 others >9%
Tungsten
1 strong supplier, >75%
KHolland@
IC CMP Slurry Suppliers
KHolland@ AVS CMP-UG 07/2009 4
IC CMP Slurry Growth ?
1,200 1,000 800 600 400 200 0 2006 2007 2008 2009
Cu Barrier Cu Step 1

易盛程序化交易系统大数据函数

易盛程序化交易系统数据函数BarStatus当前Bar的状态CurrentBar当前Bar的索引值Date当前Bar的日期,简写DTime当前Bar的时间,简写T HistoryDataExist历史数据是否存在Close收盘价,简写CHigh最高价,简写HLow最低价,简写LOpen开盘价,简写OOpenInt持仓量Vol成交量,简写VHisData获得各种历史数据行情函数Q_AskPrice最新卖价Q_AskVol最新申卖量Q_AvgPrice实时均价Q_BidPrice最新买盘价格Q_BidVol最新申买量Q_Close最新价或收盘价Q_High当日最高价Q_HisHigh历史最高价Q_HisLow历史最低价Q_Low当日最低价Q_LowerLimit当日跌停板价Q_Open当日开盘价Q_OpenInt持仓量Q_PreOpenInt昨日持仓量Q_PreSettlePrice昨日结算价Q_PriceChgRatio当日涨跌幅Q_PriceChg当日涨跌Q_TotalVol当日成交量Q_TurnOver成交金额Q_UpperLimit当日涨停板价属性函数BarType当前图表的周期类型BarInterval当前图表的周期值BigPointValue返回一个点的价值SymboStatus交易状态SeatMargin返回合约的保证金率ContractUnit每张合约的单位数量ExchangeName商品的交易所名称ExpiredDate最后交易日StartTrdDate开始交易日EndDelvDate最后交割日MinMove商品价格最小变动量PriceScale计数单位，报价精确度Symbol当前图表商品的代码SymbolName当前图表商品的名称ExecPrice执行价格Volatile波动率SymbolKindOptionsType买权卖权标志DesCommodity期权标的合约编码期货交易函数Buy多头建仓Sell多头平仓SellShort空头建仓BuyToCover空头平仓CancelAllOrders批量撤单函数MarketPosition当前公式应用总持仓方向A_AccountID返回交易帐户IDA_DeleteOrder发送撤单指令A_SendOrder针对当前公式应用发送委托单A_FirstOrderNo返回当前公式应用第一个订单号A_LastOrderNo返回当前公式应用最近发送的订单号A_FirstQueueOrderNo返回当前公式第一个排队单子A_NextQueueOrderNo返回当前公式下一个排队单子A_NextOrderNo返回当前公式应用下一个订单号A_FirstPositionSymbol返回当前公式应用第一个有持仓的合约A_NextPositionSymbol返回当前公式应用下一个有持仓的合约A_OrderFlag获取用户自定义标识A_OrderStatus查询订单状态A_OrderContractNo订单合约号A_OrderContractNo2第二腿订单合约号A_OrderExchangeName交易所名称A_OrderBuyOrSell订单多空类型A_OrderEntryOrExit订单开平类型A_OrderPrice订单委托价格A_OpenOrderLot订单委托数量A_OrderFilledLot订单成交量A_OrderFilledPrice订单成交价格A_OpenOrderTime订单下单时间A_TodayBuyPosition当前公式应用当日多仓数量A_TodaySellPosition当前公式应用当日空仓数量A_BuyPosition当前公式应用多仓总量A_SellPosition当前公式应用空仓总量A_BuyAvgPrice当前公式应用多仓均价A_SellAvgPrice当前公式应用空仓均价A_TotalPosition当前公式应用总持仓量A_TotalAvgPrice当前公式应用总持仓均价LastExitTime当前公式应用当天最近一次平仓时间LastExitPrice当前公式应用当天最近一次平仓价格LastEntryTime当前公式应用当天最近一次开仓时间LastEntryPrice当前公式应用当天最近一次开仓价格BarsSinceEntry获得公式第一次调用建仓函数到当前位置的Bar计数BarsSinceExit获得公式第一个调用平仓函数到当前位置的Bar计数BarsSinceLastEntry获得公式最后一个调用建仓函数到当前位置的Bar计数BarsSinceLastExit获得公式最后一个调用平仓函数到当前位置的Bar 计数A_IsConected判断交易服务器是否登录SetOrderFlag设置当前公式所下定单的自定义标识证券交易函数S_IsConnected判断交易是否连接S_AccountID获取用户资金帐号S_Buy买入,返回订单号S_Sell卖出,返回订单号S_DeleteOrder撤单S_FirstOrderNo返回第一个订单号S_NextOrderNo返回下一个订单号S_FirstPositionSymbol返回第一个有持仓的股票代码S_NextPositionSymbol返回下一个有持仓的股票代码S_CurrentAmount返回当前数量S_CostPrice返回成本价S_StockCode证券代码S_ExchangeType交易所S_OrderBuyOrSell返回买卖方向S_EntrustAmount委托数量S_EntrustPrice委托价格S_BusinessAmount成交数量S_BusinessPrice成交价格S_ReportTime申报时间S_OrderStatus返回订单状态枚举值证券资金函数MS_Current获取当前余额MS_Enable获取可用金额MS_Fetch获取可取金额MS_Interest获取待入账利息MS_Asset获取资产总值(不含基金市值)MS_cash获取可取现金MS_Fund获取资金(=资产总值-证券市值)MS_Market获取证券市值MS_OpFund获取基金市值MS_Preinterest获取预计利息高级期货交易函数G_SendOrder针对当前帐户发送委托单G_FirstOrderNo返回当前帐户第一个订单号G_NextOrderNo返回当前帐户下一个订单的订单号G_FirstQueueOrderNo返回当前账户第一个排队单子G_NextQueueOrderNo返回当前账户下一个排队单子G_FirstPositionSymbol返回当前帐户第一个有持仓的合约名称G_NextPositionSymbol返回当前帐户下一个有持仓的合约名称G_TodayBuyPosition当前帐户合约当日买入持仓数量G_TodaySellPosition当前帐户合约当日卖出持仓数量G_BuyPosition当前帐户合约多仓数量G_SellPosition当前帐户合约空仓数量G_BuyAvgPrice当前帐户合约持仓买均价G_SellAvgPrice当前帐户合约持仓卖均价G_TotalPosition当前帐户合约总持仓量G_TotalAvgPrice当前帐户合约总持仓均价G_MarketPosition当前帐户合约持仓方向资金函数M_TodayDeposit当日入金M_TodayDrawing当日出金M_Fee手续费M_Freeze冻结保证金M_Margin持仓保证金M_LiqProfit平仓总盈亏M_NumericProfit浮动总盈亏M_DLiqProfit平仓本日盈亏M_DNumericProfit浮动本日盈亏M_DayProfit结算盈亏M_YBalance上日结存M_YRightBalance上日权益M_DBalance本日结存M_DRightBalance本日权益M_YFreeMargin上日可用资金M_FreeMargin本日可用资金M_RiskRate风险率M_HoldFund持仓金额M_MCashAvalib交易所可用资金M_MFreeze交易所冻结保证金M_MMargin交易所持仓保证金M_MRiskRate交易所风险率M_Premium权利金金融及统计函数AdaptiveMovAvg求卡夫曼自适应移动平均Average求平均AverageFC快速计算平均值AvgPrice求平均价格AverageD求N天以来的价格平均值AvgDeviation求平均背离AvgTrueRange求平均真实范围BarsSinceToday当天的第一个数据到当前的Bar数CoefficientR求皮尔森相关系数CountIf获取最近N周期条件满足的计数Correlation求相关系数Covar求协方差CrossOver求是否上穿CrossUnder求是否下破Cum求累计值CloseD求N天前的收盘价DEMA求双指数移动平均Detrend求趋势平滑Extremes求极值Filter条件过滤Highest求最高HighestFC快速计算最高HighestBar求最高值出现的BarHighD求N天前的最高价LinearReg求线性回归Lowest求最低LowestFC快速计算最低LowestBar求最低只出现的BarLowD求N天前的最低价NthCon第N个满足条件的Bar距当前的Bar数目NthExtremes求N极值NthHigher求第N高NthHigherBar求第N高出现的BarNthLower求第N低NthLowerBar求第N低出现的BarOpenD求N天前的开盘价OpenIntD求N天前的持仓量ParabolicSAR求抛物线转向PercentChange求涨跌幅PercentR求威廉指标Pivot求转折RateOfChange求变动率SMA求移动平均StandardDev求标准差Summation求和SummationFC快速求和SwingHigh求波峰点SwingLow求波谷点TrueHigh求真实高点TrueLow求真实低点TrueRange求真实范围VariancePS求估计方差VolD求N天前的成交量WAverage求权重平均XAverage求指数平均字符串函数Exact判断两字符串是否完全相等，区分大小写Left返回前lCount长度的字符串Len返回字符串的长度Lower转化为小写Mid返回从lFirst开始共lCount个字符组成的字符串Right返回字符串右端指定长度的字符串Text将数字转化为字符串Trim去除字符串两端的空格Upper转为大写Value将字符串转化为数字其他函数Alert弹出警告窗口或声音Commentary向调试窗口输出一行字符串Print向调试窗口输出一个字符串或数字InvalidString返回无效字符串的值""InvalidNumeric返回无效数字的值IsTradeAllowed返回是否允许自动交易FileAppend向文件追加一行文字FileDelete删除文件IIF根据逻辑真假值返回不同的数值IIFString根据逻辑真假值返回不同的字符串SetGlobalVar设置一个全局变量GetGlobalVar读取一个全局变量的值GetELProfileString读取公式信息文件指定块中的键名对应的字符串GetELProfileString2File读取公式信息文件指定块中的键名对应的字符串SetELProfileString把给定的键名及其值写入到公式信息文件的相应块中SetELProfileString2File把给定的键名及其值写入到公式信息文件的相应块中时间函数DateAdd返回增加指定天数后的日期DateDiff返回两个日期之间的日期间隔DateTimeToString将日期时间值转化为字符串类型DateToString将日期值转化为字符串类型Day获得当前Bar的日信息DayFromDateTime获取输入日期时间的日信息Friday获得星期五的值Hour获得当前Bar的小时信息HourFromDateTime获取输入日期时间的小时信息MakeDate将参数生成日期值MakeDateTime将参数生成日期时间值MakeTime将参数生成时间值Minute获得当前Bar的分钟信息MinuteFromDateTime获取输入日期时间的分钟信息Monday获得星期一的值Month获得当前Bar的月信息MonthFromDateTime获取输入日期时间的月信息Saturday获得星期六的值Second获得当前Bar的秒信息SecondFromDateTime获取输入日期时间的秒信息StringToDate将字符串YYYY-MM-DD转化为日期StringToDateTime将字符串YYYY-MM-DDHH:MM:SS转化为日期时间StringToTime将字符串HH:MM:SS转化为时间Sunday获得星期日的值SystemDateTime获取交易平台的当前日期时间Thursday获取星期四的值TimeDiff返回两个日期之间的间隔秒数TimeAdd返回增加指定秒数后的日期TimeToString将时间值转化为字符串类型Tuesday获取星期二的值Wednesday获取星期三的值Weekday获得当前Bar的周信息WeekdayFromDateTime获取输入日期时间的周信息Year获得当前Bar的年信息YearFromDateTime获取输入日期时间的年信息数学函数Abs返回参数的绝对值Acos返回数字的反余弦值Asin返回参数的反正弦值Atan返回参数的反正切值Atan2返回给定的X及Y坐标值的反正切值Combin计算从给定数目的对象集合中提取若干对象的组合数Cos返回给定角度的余弦值Cosh返回参数的双曲余弦值Ctan返回给定角度的余切值Ceil返回不小于num的最小整数Exp返回e的n次幂Fact返回数的阶乘Floor返回不大于num的最大整数FracPart返回一个数的小数部分IntPart返回一个数的整数部分Ln返回一个数的自然对数Log按所指定的底数，返回一个数的对数Max返回两数中较大的一个Min返回两数中较小的一个Mod返回两数相除的余数Neg返回参数的负绝对值Pi返回数字3.1415926535898Power返回给定数字的乘幂Rand返回位于两个指定数之间的一个随机整数Round返回某个数字按指定位数舍入后的数字Sign返回数字的符号 1:正数,-1:负数,0:零Sin返回给定角度的正切值Sinh返回某一数字的双曲正弦值Sqr返回参数的平方Sqrt返回正平方根Tan返回给定角度的正切值Tanh返回某一数字的双曲正切值数组函数ArrAdd添加数据到数组末端ArrInsert在数组指定位置插入数据ArrSub返回数组的子集ArrLength获得数组的长度ArrClear清空数组ToArray将序列变量复制到数组ArrRevers将数组反转ArrSetSize设置数组的大小，以value填充iMA求平均iHHV求最高iLLV求最低颜色函数DefaultColor默认颜色(红色)Rgb自定义颜色Black黑色Blue蓝色Cyan青色DarkBrown茶色DarkCyan深青色DarkGray深灰色DarkGreen深绿色DarkMagenta深褐色DarkRed深红色Green绿色LightGray浅灰色Magenta褐色Red红色White白色Yellow黄色枚举函数Enum_Data_Close返回收盘价枚举值Enum_Data_Open返回开盘价枚举值Enum_Data_High返回最高价枚举值Enum_Data_Low返回最低价枚举值Enum_Data_Median返回中间价枚举值 (高+低)/2 Enum_Data_Typical返回标准价枚举值 (高+低+收)/3Enum_Data_Weighted返回加权收盘价枚举值 (高+低+开+收)/4 Enum_Data_Vol返回成交量枚举值Enum_Data_Opi返回持仓量枚举值Enum_Period_Default周期类型_当前图表周期Enum_Period_Tick周期类型_分笔Enum_Period_Second周期类型_秒线Enum_Period_SecondX周期类型_多秒Enum_Period_Min1周期类型_1分钟Enum_Period_Min3周期类型_3分钟Enum_Period_Min5周期类型_5分钟Enum_Period_Min15周期类型_15分钟Enum_Period_Min30周期类型_30分钟Enum_Period_Min60周期类型_60分钟Enum_Period_Min120周期类型_102分钟Enum_Period_Min240周期类型_240分钟Enum_Period_MinX周期类型_多分钟Enum_Period_Day周期类型_日线Enum_Period_Week周期类型_周线Enum_Period_Month周期类型_月线Enum_Period_Year周期类型_年线Enum_Period_DayX周期类型_多日线Enum_Buy多空类型_多头Enum_Sell多空类型_空头Enum_Entry开平类型_开仓Enum_Exit开平类型_平仓Enum_ExitToday开平类型_平今Enum_Invalid无效订单Enum_Queue排队中Enum_PartDeal部分成交Enum_AllDeal完全成交Enum_Canceling待撤Enum_Canceled已撤单绘图函数PlotNumeric绘制指标线PlotVertLine绘制一条竖线PlotText绘制一个字符串PlotIcon绘制一个系统图标PlotDot绘制一个点PlotBar设置K线颜色UnPlot取消PlotNumeirc所绘制的线UnPlotText取消PlotText绘制的字符串UnPlotIcon取消绘制的图标实用文档UnPlotDot取消绘制的点UnPlotVertLine取消绘制的竖线文案大全。

利他偏好下双渠道供应链定价决策研究

亿，电子商务市值突破２万亿人民币［１］。网络购道的供应链决策模型，从而对供应链各方的定价
物的发展促使许多制造商（ＨＰ、Ａｐｐｌｅ、Ｌｅｎｏｖｏ决策和利润进行了分析。
等）开始在传统零售渠道的基础上开辟电子渠
以上关于双渠道供应链定价决策的研究都是
道。建立网络直销渠道与传统零售渠道共存的双建立在 “理性经济人” 的基本假定之上，即认为
实现传统零售渠道和网络直销渠道的双赢。Ｈｕａ等［５］在双渠道供应链中，通过集中决策和分散决
模式。根据波士顿咨询公司２０１５年发布的相关报告，中国消费者在互联网的消费数量已增至３�� ２９
策分析了交货时间对制造商和零售商定价决策的影响。申成然等［６］在网络比价行为下建立了双渠
工业大学管理学院硕士研究生。研究方向：物流与供应链管理。
— ９１ —
第２期（总第２９２期）
工业技术经济
Ｎｏ�� ２（Ｇｅｎｅｒａｌ，Ｎｏ�� ２９２）
２０１８年２月
ＪｏｕｒｎａｌｏｆＩｎｄｕｓｔｒｉａｌＴｅｃｈｎｏｌｏｇ
��

交易开拓者函数一览表文华对照

交易开拓者函数一览表文华对照交易开拓者文华数学函数绝对值Abs ABSX 反余弦值Acos ACOSX 反双曲余弦值Acosh反正弦值Asin ASINX 反双曲正弦值Asinh反正切值Atan ATANX 给定的X及Y坐标值的反正切值Atan2反双曲正切值Atanh沿绝对值增大方向按基数舍入Ceiling从给定数目的对象集合中提取若干对Combin象的组合数余弦值Cos COSX 双曲余弦值Cosh余切值Ctan沿绝对值增大方向取整后最接近的偶Even数e的N次幂Exp EXPX 数的阶乘Fact沿绝对值减少的方向去尾舍入Floor实数舍入后的小数值FracPart实数舍入后的整数值IntPart自然对数Ln LNX对数Log LOGX余数Mod MODA,B 负绝对值Neq指定数值舍入后的奇数Odd返回PI Pi给定数字的乘幂Power POWA,B 随机数Rand按指定位数舍入Round靠近零值,舍入数字RoundDown远离零值,舍入数字RoundUp数字的符号Sign SGNX正弦值Sin双曲正弦值Sinh SINX平方Sqr SQUAREX 正平方根Sqrt SQRTX正切值Tan TANX双曲正切值Tanh取整Trunc INTPARTX 字符串函数测试是否相同Exact返回字符串中的字符数Len大写转小写Lower数字转化为字符串Text取出文本两边的空格Trim小写转大写Upper文字转化为数字Value颜色函数黑色Black COLORBLACK 蓝色Blue COLORBLUE 青色Cyan COLORCYAN 茶色DarkBrown深青色DarkCyan深灰色DarkGray深绿色DarkGreen深褐色DarkMagenta深红色DarkRed默认颜色DefaultColor绿色Green COLORGREEN浅灰色LightGray COLORLIGHTGREY 紫红色Magenta COLORMAGENTA 红色Red COLORRED自定义颜色Rgb Rgb白色White COLORWHITE黄色Yellow COLORYELLOW时间函数当前日期CurrentDate当前时间CurrentTime日期时间值转化为字符串类型DateTimeToString日期值转化为字符串类型DateToString获得当前bar的日信息Day DAY 获得星期一值Monday获得星期二值Tuesday获得星期三值Wednesday获得星期四值Thursday获得星期五值Friday获得星期六值Saturday获得星期日值Sunday获得当前bar的小时信息Hour HOUR 将参数生成日期值MakeDate将参数生成日期时间值MakeDateTime将参数生成时间值MakeTime获得当前bar的分钟信息Minute MINUTE 获得当前bar的月信息Month MONTH 获得当前bar的秒信息Second将字符串转化为日期StringToDate将字符串转化为日期时间StringToDateTime将字符串转化为时间StringToTime获得交易开拓者平台的当前日期时间SystemDateTime将时间值转化为字符串类型TimeToString获得当前bar的周信息Weekday WEEKDAY 获得当前bar的年信息Year YEAR数据函数当前商品数据的bar总数BarCount当前商品当前bar的状态值BarStatus当前bar收盘价C当前bar收盘价Close CLOSE 当前商品当前bar的索引值CurrentBar BARPOS 当前bar日期D当前bar日期Date当前bar的最高价H当前bar的最高价High HIGH 当前历史数据是否有效HistoryDataExist当前bar的最低价L当前bar的最低价Low LOW下一个bar的收盘价未来函数NextClose下一个bar的最高价未来函数NextHigh下一个bar的最低价未来函数NextLow下一个bar的开盘价未来函数NextOpen下一个bar的持仓量未来函数NextOpenInt下一个bar的成交量未来函数NextVol当前bar的开盘价O当前bar的开盘价Open OPEN 当前bar的持仓量OpenInt OPI 当前bar的时间T当前bar的时间Time当前bar的成交量V当前bar的成交量Vol VOL 属性函数当前商品的时间周期数值BarInterval当前商品的时间周期类型BarType当前商品数据的买卖盘个数BidAskSize当前商品的一个整数点价值BigPointValue是否支持市价委托CanMarketOrder是否支持做空CanShortTrade是否支持Stop委托CanStopOrder是否可以交易CanTrade当前商品合约大小ContractSize每张合约包含基本单位ContractUnit当前商品交易的货币名称CurrencyName当前商品交易的货币符号CurrencySymbol当前商品的交易所名称ExchangeName当前商品的初始保证金InitialMargin当前商品的维持保证金MaintenanceMargin 当前商品的默认保证金MarginRatio当前商品单笔交易限量MaxSingleTradeSize 当前商品最小变动量MinMove当前商品的计数单位PriceScale当前商品的点差Spread当前商品的代码Symbol当前商品的名称SymbolName当前商品的类型SymbolType行情函数交易开拓者行情函数只对最后一个bar有效最新卖盘价格Q_AskPrice最新卖盘量Q_AskVol实时均价Q_AvgPrice AVPRICE 卖盘价格变化标志Q_AskPriceFlag最新买盘价格Q_BidPrice买盘价格变化标志Q_BidPriceFlag最新买盘量Q_BidVol当日收盘价Q_Close CLOSE 当日最高价Q_High HIGH历史最高价Q_HisHigh历史最低价Q_HisLow内盘Q_InsideVol最新价Q_Last最新价变化标志Q_LastFlag最新成交时间Q_LastTime商品的现手Q_LastVol当日最低价Q_Low LOW 当日跌停板价Q_LowerLimit当日开盘价Q_Open OPEN 当日持仓量Q_OpenInt OPI 持仓量变化标志Q_OpenIntFlag当前商品的振幅Q_Oscillation当前商品的外盘Q_OutsideVol当前商品的昨日持仓量Q_PreOpenInt当前商品的昨日结算价Q_PreSettlePrice SETTLE 当日涨跌Q_PriceChg当日涨跌幅Q_PriceChgRatio当前商品的最新笔升跌Q_TickChg当日开仓量Q_TodayEntryVol当日平仓量Q_TodayExitVol当日成交量Q_TodayVol VOL成交金额Q_TurnOver当日涨停板价Q_UpperLimit行情数据是否有效QuoteDataExist账户函数交易开拓者账户函数只对最后一个bar有效交易账户ID A_AccountID对应交易商ID A_BrokerID当前账户下当前商品买入持仓均价A_BuyAvgPrice当前账户的买入冻结A_BuyFreeze当前账户的买入保证金A_BuyMargin当前账户的买入持仓A_BuyPosition当前账户的买入持仓盈亏A_BuyProfitLoss当前账户的动态权益A_CurrentEquity撤单指令A_DeleteOrder当前账户的可用资金A_FreeMarginA_GetLastOpenOrderIndex 返回当前商品最后一个未成交单的索引返回当前商品的最后一个当日委托单A_GetLastOrderIndex索引返回当前商品的未成交委托单数量A_GetOpenOrderCount返回当前商品的当日委托单数量A_GetOrderCount返回当前商品的未成交委托单买卖类A_OpenOrderBuyOrSell 型返回当前账户当前商品的某个委托单A_OpenOrderContractNo 合同号当前账户当前商品某个未成交委托单A_OpenOrderEntryOrExit 的开平仓状态当前账户当前商品的某个未成交委托A_OpenOrderFilledPrice 单的成交价格当前账户当前商品的某个未成交委托A_OpenOrderLot单的委托数量当前账户当前商品的某个未成交委托A_OpenOrderPrice 单的委托价格当前账户当前商品的某个未成交委托A_OpenOrderStatus 单状态当前账户当前商品的某个未成交委托A_OpenOrderTime单的委托时间当前账户当前商品的某个交委托单的A_OrderBuyOrSell 买卖类型当前账户当前商品的某个交委托单的A_OrderContractNo 合同号当前账户当前商品的某个交委托单的A_OrderCanceledLot 撤单数量返回当前公式应用的帐户下当前商品A_OrderEntryOrExit 的某个委托单的开平仓状态;返回当前公式应用的帐户下当前商品A_OrderFilledLot 的某个委托单的成交数量;返回当前公式应用的帐户下当前商品A_OrderFilledPrice 的某个委托单的成交价格;返回当前公式应用的帐户下当前商品A_OrderLot的某个委托单的委托数量;返回当前公式应用的帐户下当前商品A_OrderPrice的某个委托单的委托价格;返回当前公式应用的帐户下当前商品A_OrderStatus的某个委托单的状态;返回当前公式应用的帐户下当前商品A_OrderTime的某个委托单的委托时间;返回当前公式应用的帐户下当前商品A_PositionProfitLoss 的持仓盈亏返回当前交易帐户的昨日结存;A_PreviousEquity返回当前交易帐户的浮动盈亏;A_ProfitLoss针对当前帐户、商品发送委托单A_SendOrder返回当前帐户下当前商品的卖出持仓A_SellAvgPrice均价返回当前交易帐户的卖出冻结A_SellFreeze返回当前交易帐户的卖出保证金A_SellMargin返回当前帐户下当前商品的卖出持仓A_SellPosition返回当前帐户下当前商品的卖出持仓A_SellProfitLoss盈亏返回当前帐户下当前商品的当日买入A_TodayBuyPosition 持仓返回当前公式应用的交易帐户的当日A_TodayDeposit入金返回当前公式应用的交易帐户的当日A_TodayDrawing出金返回当前帐户下当前商品的当日卖出A_TodaySellPosition 持仓返回当前帐户下当前商品的持仓均价A_TotalAvgPrice 返回当前帐户下当前商品的总持仓A_TotalPosition 当前公式应用商品的帐户数据是否有AccountDataExist 效枚举函数返回买卖状态的买入枚举值Enum_Buy返回委托状态的已撤单枚举值Enum_Canceled返回委托状态的正在撤单枚举值Enum_Canceling 返回委托状态的正在申报枚举值Enum_Declare返回委托状态的已申报枚举值Enum_Declared返回委托状态的已废除枚举值Enum_Deleted返回开平仓状态的开仓枚举值Enum_Entry返回开平仓状态的平仓枚举值Enum_Exit返回开平仓状态的平今仓枚举值Enum_ExitToday 返回委托状态的全部成交枚举值Enum_Filled返回委托状态的部分成交枚举值Enum_FillPart返回委托状态的部分成交枚举值Enum_Sell交易函数获得保本交易的平均持仓Bar数AvgBarsEvenTrade 获得亏损交易的平均持仓Bar数AvgBarsLosTrade 获得盈利交易的平均持仓Bar数AvgBarsWinTrade 获得当前持仓的平均建仓价格AvgEntryPriceBarsSinceEntry 获得当前持仓的第一个建仓位置到当前位置的Bar计数获得最近平仓位置到当前位置的BarBarsSinceExit计数产生一个多头建仓操作Buy产生一个空头平仓操作BuyToCover获得当前持仓位置的每手浮动盈亏ContractProfit 获得当前的可用资金CurrentCapital 获得当前持仓的持仓合约数CurrentContracts 获得当前持仓的建仓次数CurrentEntries 获得当前持仓的第一个建仓位置的日EntryDate期获得当前持仓的第一个建仓价格EntryPriceEntryTime获得当前持仓的第一个建仓位置的时间获得最近平仓位置Bar日期ExitDate获得最近平仓位置的平仓价格ExitPrice获得最近平仓位置Bar时间ExitTime获得累计的总亏损GrossLoss获得累计的总利润GrossProfit获得最大单次交易亏损数LargestLosTrade 获得最大单次交易盈利数LargestWinTrade 获得当前持仓状态MarketPosition 获得最大连续亏损交易次数MaxConsecLosers 获得最大连续盈利交易次数MaxConsecWinners 获得当前持仓的最大持仓合约数MaxContracts获得最大的持仓合约数MaxContractsHeld 获得最大的建仓次数MaxEntries获得最大的资产缩水值MaxIDDrawDown获得当前持仓的最大浮动亏损数MaxPositionLoss 获得当前持仓的最大浮动盈利数MaxPositionProfit 获得累计的净利润NetProfit获得保本交易的总次数NumEvenTrades获得亏损交易的总次数NumLosTrades获得盈利交易的总次数NumWinTrades获得盈利的成功率PercentProfit获得当前持仓位置的浮动盈亏PositionProfit产生一个多头平仓操作Sell产生一个空头建仓操作SellShort根据参数进行保本平仓操作SetBreakEven根据参数进行价值回落平仓操作SetDollarTrailing 当日收盘全部平仓SetExitOnClose根据参数进行盘整平仓操作SetInactivate根据参数进行百分比回落平仓操作SetPercentTrailing 根据参数进行区间回落平仓操作SetPeriodTrailing 根据参数进行获利平仓操作SetProfitTarget根据参数进行止损平仓操作SetStopLoss获得保本交易的总持仓Bar数TotalBarsEvenTrades 获得亏损交易的总持仓Bar数TotalBarsLosTrades 获得盈利交易的总持仓Bar数TotalBarsWinTrades获得交易的总次数TotalTrades其他函数产生一个报警动作Alert返回当前公式应用的报警设置AlertEnabled输出用户字段的一个布尔值FieldBool输出用户字段的一个数值FieldNumeric输出用户字段的一个字符串FieldString在指定文件中追加一行字符串FileAppend删除指定文件FileDelete获得当前执行的公式名称FormulaName获取某个索引的全局变量值GetGlobalVarI_AvgEntryPrice 在技术分析中输出交易指令组合在当前Bar的平均建仓成本在技术分析中输出交易指令组合在当I_CloseEquity前Bar的盈亏在技术分析中输出交易指令组合在当I_CurrentContracts前Bar的持仓手数在技术分析中输出交易指令组合在当I_MarketPosition前Bar的持仓状况在技术分析中输出交易指令组合在当I_OpenEquity前Bar的浮动盈亏执行真假值判断,根据逻辑测试的真假IIF IFC,A,B 值返回不同的数值执行真假值判断,根据逻辑测试的真假IIFString值返回不同的字符串返回整型的无效值InvalidInteger返回数值型的无效值InvalidNumeric字符串的无效值InvalidString在当前Bar输出一个布尔值PlotBool在当前Bar输出一个数值PlotNumeric在当前Bar输出一个字符串PlotString在当前Bar输出两个值,用于在图表中PlotBar当前Bar上画出连接两个值的线条设置某个索引的全局变量值SetGlobalVar删除曾经输出的值Unplot金融、数理统计内建用户函数求卡夫曼自适应移动平均AdaptiveMovAvg求平均Average MAX,N快速计算平均值AverageFC求平均背离AvgDeviation求平均价格AvgPrice求平均真实范围AvgTrueRange求皮尔森相关系数CoefficientR求相关系数Correlation求协方差Covar求是否上穿CrossOver CROSSX,Y 求是否下破CrossUnder求累计值Cum求双指数移动平均DEMA求趋势平滑Detrend求偏差均方和DevSqrd求极值Extremes求Fisher变换Fisher求反Fisher变换FisherInv求调和平均数HarmonicMean求最高Highest HHVX,N求最高值出现的Bar HighestBar HHVBARSX,N类似求峰度系数Kurtosis求线性回归LinearReg求线性回归角度LinearRegAngle求线性回归斜率LinearRegSlope SLOPEX,N求线性回归值LinearRegValue FORCASTX,N求最低Lowest LLVX,N求最低值出现的Bar LowestBar LLVBARSX,N 求最大值Max MAXA,B求中位数Median求中点MidPoint求最小值Min MINA,B求众数Mode求动量Momentum求N极值NthExtremes求第N高NthHigher求第N高出现的Bar NthHigherBar求第N低NthLower求第N低出现的Bar NthLowerBar求抛物线转向ParabolicSAR SARN, Step, Max 求涨跌幅PercentChange求威廉指标PercentR求排列Permutation求转折Pivot求振荡PriceOscillator求变动率RateOfChange求平滑平均SAverage求偏度系数Skewness求标准差StandardDev STDX,N,STDPX,N 求和Summation SUMX,N快速求和SummationFC求波峰点SwingHigh求波峰点出现的Bar SwingHighBar求波谷点SwingLow求波谷点出现的Bar SwingLowBar求真实高点TrueHigh求真实低点TrueLow求真实范围TrueRange求估计方差VariancePS VARX,N,VARPX,N 求权重平均WAverage SMAX,N,M求指数平均XAverage文华独有函数交易开拓者没有直接对应的函数若X非0,则将当前位置到N周期前的无对应函数BACKSETX,N数值设为1;求上一次条件成立到当前的周期数;无对应函数BARSLASTX统计在N周期内满足X条件的周期数;无对应函数COUNTX,N返回X的动态移动平均,其中A必须介无对应函数DMAX,A于0及1之间;求X在N周期内的平滑移动平均;指数无对应函数EMAX,N加权求X在N周期内的加权平均;线性加权无对应函数EMA2X,N ZIGZAG之字转向未来函数ZigZag技术指标ZIGZAGX,P,C取得ZIGZAG前M个波峰的值未来函数无对应函数PEAKX,P,M,C取得ZIGZAG前M个波峰到当前周期的无对应函数PEAKBARSX,P,M,C 周期数;未来函数取得ZIGZAG前M个波谷的值;未来函数无对应函数TROUGHX,P,M,C取得ZIGZAG前M个波谷到当前周期的无对应函数TROUGHBARSX,P,M,C 周期数未来函数得到X向前累加直到大于A时的周期无对应函数SUMBARSX,A数;求X在N周期内的三角移动平均;无对应函数TRMAX,N求X在N周期内的时间序列移动平均;无对应函数TSMAX,N求X在N周期内的平均绝对偏差;无对应函数AVEDEVX,N数据偏差平方和;无对应函数DEVSQX,N判断A是否位于B及C之间无对应函数BETWEENA,B,C判断过去N个周期内是否有满足条件无对应函数EXISTCOND,N COND判断过去N个周期内是否一直满足条无对应函数EVERYCOND,N件COND无对应函数LASTCOND,N1,N2判断过去N1到N2周期内是否一直满足条件COND如果A在前N个周期内都小于B,本周无对应函数LONGCROSSA,B,N期上穿B,则返回1;否则返回0;信号过滤函数无对应函数NOFILTER如果该周期收阴则返回1,否则返回0;无对应函数ISDOWN如果该周期平盘则返回1,否则返回0;无对应函数ISEQUAL如果该周期收阳则返回1,否则返回0;无对应函数ISUP取得当前周期是否为最后一根K线;无对应函数ISLASTBAR当条件COND满足时,取当时的DATA的无对应函数VALUEWHENCOND,DATA 值,否则取得VALUEWHEN的前一个值;向上舍入;返回沿X数值增大方向最接无对应函数CEILINGX近的整数;向下舍入;返回沿X数值减小方向最接无对应函数FLOORX近的整数;当X为0时返回1,否则返回0;无对应函数NOTX取反;无对应函数REVERSEX。

r语言的信息份额模型

r语言的信息份额模型在R语言中，信息份额模型（Information Share Model）通常用于评估不同市场之间的价格发现作用。

这个模型由Hasbrouck（1995）提出，并广泛用于金融领域。

在R语言中实现信息份额模型，首先需要安装和加载相关的包。

通常，您需要安装并加载“urca”和“vars”等包，这些包提供了进行信息份额分析所需的函数和工具。

安装这些包的命令如下：```rinstall.packages("urca")install.packages("vars")```安装完成后，您可以通过加载这些包来使用它们的功能：```rlibrary(urca)library(vars)```接下来，您需要准备数据。

信息份额模型要求您提供对数价格数据，因此您需要将原始价格数据转换为对数形式。

您可以使用“log”函数来完成这一步：```rlog_price_data <- log(price_data)```准备好数据后，您可以使用“vecm”函数来拟合向量误差修正模型（VECM），这是信息份额模型的基础。

您可以将对数价格数据作为输入传递给“vecm”函数：```rvecm_model <- vecm(log_price_data)```拟合模型后，您可以计算信息份额指标（Information Share，IS）和贡献程度指标（Contribution to Price Discovery，CS）。

这些指标可以帮助您评估不同市场之间的价格发现作用。

在R语言中，您可以使用“is.脉冲响应函数”（is.irf）和“cs.脉冲响应函数”（cs.irf）来计算这些指标：```ris_irf <- is.irf(vecm_model)cs_irf <- cs.irf(vecm_model)```最后，您可以使用这些指标进行进一步的分析和可视化，以更好地理解不同市场之间的价格发现过程。

“药价虚高”现象分析及药品定价策略研究

“药价虚高”现象分析及药品定价策略研究张英男;徐文【摘要】目的进一步分析药价虚高的原因,对规范药品定价提出定价策略建议.方法收集电视报道、医院进销价格数据,分析药价虚高的现状及原因.借鉴国外定价策略,结合我国国情,提出药品定价策略的建议.结果与结论药价虚高的原因有药品费用增长因素、药品特殊性、医患双方信息不对称性、流通渠道环节过多等.应加强政府层面的制度研究,借览国外合理的定价方法与经验,利用药物经济学原理,制订科学、合理、规范的药品定价策略,妥善解决“药价虚高”现象.【期刊名称】《中国药业》【年(卷),期】2014(023)006【总页数】4页(P3-6)【关键词】药价虚高;药物经济学;信息不对称;药品定价【作者】张英男;徐文【作者单位】山东中医药大学,山东济南 250355;山东中医药大学,山东济南250355【正文语种】中文【中图分类】R197.1;F714.1;F763药品是关系全国人民生命健康的特殊商品，人民群众反映“药价虚高”问题已有10余年。

笔者通过收集具体数据总结药价虚高的现状并分析其原因，通过利用经济学原理，借鉴国外先进定价策略经验针对我国国情，提出了药品定价策略建议。

1 药价虚高的现状与原因分析1.1 现状如今，看病难、看病贵一直是民生问题中老百姓最关心的问题。

群众普遍反映的看病贵、药价高、不合理的药费增长和虚高定价，已成为影响卫生服务利用和可及性的重要原因。

2010年5月16日，中央电视台日曝光了湖南公立医院药价虚高1300%的“天价芦笋片”事件。

某厂生产的芦笋片出厂价15.5元/瓶，中标价185.22元/瓶，零售价213元/瓶；医院所获15%加成为185.22×15%＝27.8元/瓶；医生所收回扣为80元/瓶，占中标价的43.12%，占零售价的37.56%，医生所收回扣是医院所获加成的2.88倍。

2011年11月13日，中央电视台又曝光了北京公立医院药价虚高2000%的“克林霉素磷酸酯注射液”事件。

Excel中oddlprice函数的奇异债券净价计算

Excel中oddlprice函数的奇异债券净价计算Excel中的ODDLPRICE函数是一种用于计算奇异债券的净价的函数。

通过使用这个函数，我们可以轻松地计算出奇异债券的净现值，为我们的投资决策提供参考。

在Excel中，ODDLPRICE函数是基于一组特定的参数来计算奇异债券的净价的。

下面我们将详细介绍这些参数，并给出实际计算的例子。

首先，让我们了解一下ODDLPRICE函数的语法。

它的语法如下：ODDLPRICE(settlement; maturity; issue; first_coupon; rate; yield; redemption; frequency; basis)参数说明如下：- settlement: 结算日期，即购买债券的日期。

- maturity: 到期日，即债券的到期日期。

- issue: 发行日期，即债券的发行日期。

- first_coupon: 首次付息日，即债券的首次付息日期。

- rate: 年息票利率，即债券每年支付的利息。

- yield: 债券到期时的收益率。

- redemption: 债券到期时的赎回价值。

- frequency: 付息频率，即债券每年支付利息的次数。

- basis: 日计算基准，即计算利息的天数。

下面是一个实际的例子，用来说明如何使用ODDLPRICE函数来计算奇异债券的净价。

假设我们购买了一张面值1000元的奇异债券，购买日期为2022年1月1日，到期日期为2027年12月31日，债券发行日期为2021年1月1日，首次付息日期为2022年1月1日，年息票利率为4%，债券到期时的收益率为5%，债券到期时的赎回价值为1000元，付息频率为每年支付一次利息，日计算基准为实际天数。

我们可以使用如下的Excel公式来计算奇异债券的净价：=ODDLPRICE("2022/1/1"; "2027/12/31"; "2021/1/1"; "2022/1/1"; 0.04;0.05; 1000; 1; 1)在这个例子中，我们将ODDLPRICE函数的各个参数都进行了相应的填写。

考虑消费者预期后悔的产品换代策略研究

考虑消费者预期后悔的产品换代策略研究刘维奇;张晋菁【摘要】由于信息的易得性,面对企业的持续产品创新运营计划,消费者决策时不仅会将未来可选项纳入考虑,而且会在事前预期可能出现的后悔.本文分析了两种产品创新换代策略下零售企业与消费者的两阶段动态博弈,发现预期后悔对消费者自身购买决策具有显著影响,进一步得到不同的消费者预期后悔相对强度下企业取得均衡的条件.最后,将两种产品换代策略对比研究,结果表明:共生换代策略在提高企业产品市场占有率方面更有优势,而单品换代策略能实现更高的企业利润.%Becauseofinformation availability, consumers will consciously take future purchase options into consideration and anticipate the possible regrets before decision making with frequent product introductions. This study explores and analyzes the two-stage dynamic game model of consumers and retail enterprises under two primary rollover strategies and find that the anticipated regret has a significant impact on the purchase options of the consumers; Furthermore, we derive the conditions for the different equilibria at the different relative strengths of anticipated regret. At last,the paper makes a comparative analysis. The result show that the dual rollover strategy has more advantages in improving the market share of enterprise products,yet the single rollover strategy holds better profit.【期刊名称】《中国软科学》【年(卷),期】2017(000)011【总页数】10页(P147-156)【关键词】策略型消费者;预期后悔;产品换代;动态定价【作者】刘维奇;张晋菁【作者单位】山西大学管理与决策研究所,山西太原 030006;山西财经大学财政金融学院,山西太原 030006;山西大学经济与管理学院,山西太原 030006【正文语种】中文【中图分类】F270一、引言企业为了获得可持续的发展需要不断地推陈出新，因此产品的更新换代已然成为企业常态。

术语和记号_量化金融R语言高级教程_[共2页]

第5章FX衍生品FX衍生品（或者外汇衍生品）是金融衍生产品，它的回报是两种（或更多种）货币的汇率函数。

和一般的衍生品一样，FX衍生品可以分成3个主要类型：期货、互换和期权，本章仅仅处理期权型衍生品。

从基本Black-Scholes模型的一个简明广义形式开始，同时展示如何对一个简单的欧式看涨期权或者看跌货币期权定价。

然后讨论汇率期权和交叉货币期权（quanto options，以下简记为quanto）的定价。

学习本章需要你具备衍生品定价的基本知识，特别是Black-Scholes模型和风险中性定价。

本章偶尔会涉及某些数量金融中常用的数学关系［如伊藤引理（Itô's lemma）或吉尔萨诺夫定理（Girsanov theorem）］，不过并不需要对这些定理的深入理解。

但是，对其纯粹数学背景有兴趣的读者可以参考Medvegyev（2007）。

5.1 术语和记号因为需要处理汇率，所以必须澄清一些相关术语。

我们通常用S表示即期汇率，它衡量了一种货币（称为基础货币）以另一种货币（称为变量或报价货币）为单位的价格。

换句话说，一单位的基础货币等于S单位的变量货币。

理解如何阅读外汇市场报价也很重要。

一个货币对的汇率报价由两种货币的缩写表示：一个三字母代码表示基础货币，接下来另一个三字母代码表示变量货币。

例如，EURUSD=1.25意味着1欧元价值1.25美元。

这相当于报价USDEUR=0.8，意味着1美元价值0.8欧元。

通常在给定外汇对中，根据市场历史惯例决定选择哪种货币作为基础货币。

回顾在第4章“大数据—高级分析”中，我们已经看到如何从互联网下载货币汇率数据，因此我们可以用来在真实数据上检验所学的知识。

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

CMP Technical Associates
Michael Fury, Ph.D. Robert Rhoades, Ph.D.
© 2009 Techcet Group, LLC
NCCAVS CMPUG July 14, 2009 19
The Techcet Group, LLC
2009 CMP Report Orders
S‐STI Cu Barrier Oxide
2 major suppliers >30% each Commodity business Customer pricing pressure Low passes per wafer start in newer nodes Opportunities in ≤ 45 nm
1 strong supplier, >75%
KHolland@
IC CMP Slurry Suppliers
S‐STI Cu Barrier
1 strong supplier, >70%
Oxide
Mostly ≤ 65 nm Single process per wafer start Cerium is costly
Lower tech applications abound … re-use of spent CMP slurries?
KHolland@
AVS CMP-UG 07/2009 2
Major Slurry Trends
For IC manufacturing, silica dominates the slurry market in amount used For STI, ceria is preferred for more advanced technologies, but is much more expensive per gram. For Wire Cutting of the Si Ingot (IC and Solar) SiC particles are used as cutting fluid For Si wafer manufacturing polish it’s silica
Cu Step 1 Tungsten
KHolland@
Transition to Copper Interconnects
100% 74% 80% Percent Wafer Starts w/ Cu 71% 56% 60% 37% 40% 15% 20% 2% 0% RAM MPU 180 nm
Tungsten Oxide S-STI
2010
2011
2012
KHolland@
AVS CMP-UG 07/2009 5
ILD Abrasive Migration
140 25%
20% Revenues ($M) 100 15%
60
10%
Colloidal Fumed 0 2002 2003 2004 2005 2006 2007 2008 2009
KHolland@ AVS CMP-UG 07/2009 4
IC CMP Slurry Growth ?
1,200 1,000 800 600 400 200 0 2006 2007 2008 2009
Cu Barrier Cu Step 1
☺ ?
Annual Revenue (M$USD)
Oxide
2 major suppliers, >30% each
Cu Step 1
3 suppliers split 75% 2 others >9%
Tungsten
1 strong supplier, >75%
KHolland@
IC CMP Slurry Suppliers
Road filler or ?? Does it pay to be green?
KHolland@
AVS CMP-UG 07/2009 18
The Techcet Group, LLC
Techcet Partners
John Housley Lita Shon-Roy Steven Holland, Ph.D. Karey Holland, Ph.D.
Abrasives and Applications
Oxide/ILD ≤65nm use less aggressive particles: fumed to colloidal or milled fumed Copper “Step 1” predominately uses Colloidal Copper Barrier (and Dielectric Cap) uses Colloidal or Sol Tungsten fumed and colloidal
Selective STI
Lower Defects and Lower Cost (Ceria Expense)
Tungsten
Lower Defects and Lower Cost (Competition)
Copper and Barrier
Each Cu Node Expects a New Formulation Costly for Supplier, Pricy for End User
Unlike CMP Pads, the Slurry and Abrasive Supplier List is Stable
KHolland@
AVS CMP-UG 07/2009
CGMG 07/2008 7
IC CMP Slurry Market Size by Application Revenue
KHolland@
Tungsten
IC CMP Slurry Suppliers
High growth, but less volume than Cu polish Cu Barrier Customers’ priorities similar 1 major >40% New slurries for each 4 others >10% new generation due to low k dielectrics?
KHolland@
AVS CMP-UG 07/2009 17
Slurry Disposal
Lower tech applications abound Reclaim and re-use of spent CMP slurries
Concentrate used slurry Reduce heavy metal contamination
Cu Barrier 19% S‐STI 8% Oxide 16%
Cu Step 1 28% Tungsten 29%
KHolland@
IC CMP Slurry Suppliers
S‐STI Cu Barrier
1 major >40% 4 others >10% 1 strong supplier, >70%
FEOL Polish
Polish of ILD thru poly gate prior to poly etchback and metal gates
KHolland@ AVS CMP-UG 07/2009 16
Si Wafer and Slurry
SiC for Si ingot slicing not a large market Solar use is >5x the volume of IC wafers SiC revenues will approach $40M in 2009
2009 Market Slurries and Particles in CMP & a Bit Beyond
NCCAVS CMPUG Semicon W Meeting
Karey Holland, Ph.D.
July 14, 2009
Techcet Group, LLC.
KHolland@
KHolland@
AVS CMP-UG 07/2009 3
Silica for CMP
Silica Comes in 3 Types
“Fumed”: flame vapor-phase hydrolysis of SiCl4 or the like … 3 major suppliers “Colloidal”: precipitated from sodium silicate & H2SO4 ... >8 suppliers “Sol”: Stöber process condensation of silicon alkoxide (TEOS, TMOS) w/ ammonia & water, at least 2 major suppliers
Slurries & Particles in High Tech
IC CMP is by far the largest in Revenues Si Wafer production
Cutting the Si Ingot into wafers uses SiC cutting fluids Si Wafer polishing and final polish use high purity & excellent surface quality CMP with silicas
Cu Step 1
Tungsten
KHolland@
IC CMP Slurry Suppliers
S‐STI Cu Barrier Oxide
Cu Step 1 Tungsten