基金管理外文文献翻译

基金管理外文文献翻译
(含：英文原文及中文译文)
文献出处：
英文原文
Is Money Really “Smart”? New Evidence on the Relation Between Mutual Fund Flows, Manager Behavior, and Performance Persistence
Russ Wermers
Mutual fund returns strongly persist over multi-year periods—that is the central finding of this paper. Further, consumer and fund manager behavior both play a large role in explaining these longterm continuation patterns—consumers invest heavily in last-year’s winning funds, and managers of these winners invest these inflows in momentum stocks to continue to outperform other funds for at least two years following the ranking year. By contrast, managers of losing funds appear reluctant to sell their losing stocks to finance the purchase of new momentum stocks, perhaps due to a disposition effect. Thus, momentum continues to separate winning from losing managers for a much longer period than indicated by prior studies.
Even more surprising is that persistence in winning fund returns is not entirely explained by momentum—we find strong evidence that flow-related buying, especially among growth-oriented funds, pushes up stock prices. Specifically, stocks that winning funds purchase in response
to persistent flows have returns that beat their size, book-to-market, and momentum benchmarks by two to three percent per year over a four-year period. Cross-sectional regressions indicate that these abnormal returns are strongly related to fund inflows, but not to the past performance of the funds—thus, casting some doubt on prior findings of persistent manager talent in picking stocks. Finally, at the style-adjusted net returns level, we find no persistence, consistent with the results of prior studies. On balance, we confirm that money is smart in chasing winning managers, but that a “copycat” s trategy of mimicking winning fund stock trades to take advantage of flow-related returns appears to be the smartest strategy.
Eighty-eight million individuals now hold investments in U.S. mutual funds, with over 90 percent of the value of these investments being held in actively managed funds. Further, actively managed equity funds gain the lion’s share of consumer inflows—flows of net new money to equity funds (inflows minus outflows) totalled $309 billion in 2000, pushing the aggregate value of investments held by these funds to almost $4 trillion at year-end 2000. While the majority of individual investors apparently believe in the virtues of active management in general, many appear to hold even stronger beliefs concerning the talents of subgroups of fund managers—they appear to believe that, among the field of active managers, superior managers exist that can “beat the market” for long periods of time. In particular, Morningstar and Lipper compete vigorously
for the attention of these true believers by providing regular fund performance rankings, while popular publications such as Money Magazine routinely profile “star” mutual fund managers. In addition, investor dollars, while not very quick to abandon past losing funds, aggressively chase past winners (see, for example, Sirri and Tufano (1998)).
Are these “performance-chasers” wasting their money and time, or is money “smart”? Several past papers have attempted to tackle this issue, with somewhat differing results. For example, Grinblatt and Titman (1989a, 1993) find that some mutual fund managers are able to consistently earn positive abnormal returns before fees and expenses, while Brown and Goetzmann (1995; BG) attribute persistence to inferior funds consistently earning negative abnormal returns. Gruber (1996) and Zheng (1999) examine persistence from the viewpoint of consumer money flows to funds, and find that money is “smart”—that is, money flows disproportionately to funds exhibiting superior future returns. However, the exact source of the smart money effect remains a puzzle—does smart money capture manager talent or, perhaps, simply momentum in stock returns?1 More recently, Carhart (1997) examines the persistence in net returns of U.S. mutual funds, controlling for the continuation attributable to priced equity styles (see, for example, Fama and French (1992, 1993, 1996), Jegadeesh and Titman (1993), Daniel and
Titman (1997), and Moskowitz and Grinblatt (1999)). Carhart finds little evidence of superior funds that consistently outperform their style benchmarks—specifically, Carhart finds that funds in the highest net return decile (of the CRSP mutual fund database) during one year beat funds in the lowest decile by about 3.5 percent during the following year, almost all due to the one-year momentum effect documented by Jegadeesh and Titman (1993) and to the unexplained poor performance of funds in the lowest prior-year return decile.2 Thus, Carhart (1997) suggests that money is not very smart. Recent studies find somewhat more promising results than Carhart (1997). Chen, Jegadeesh, and Wermers (1999) find that stocks most actively purchased by funds beat those most actively sold by over two percent per year, while Bollen and Busse (2002) find evidence of persistence in quarterly fund performance. Wermers (2000) finds that, although the average style-adjusted net return of the average mutual fund is negative (consistent with Carhart’s study), high-turnover funds exhibit a net return that is significantly higher than low-turnover funds. In addition, these highturnover funds pick stocks well enough to cover their costs, even adjusting for style-based returns. This finding suggests that fund managers who trade more frequently have persistent stockpicking talents. All of these papers provide a more favorable view of the average actively managed fund than prior research, although none focus on the persistence issue with portfolio holdings data.
This study examines the mutual fund persistence issue using both portfolio holdings and net returns data, allowing a more complete analysis of the issue than past studies. With these data, we develop measures that allow us to examine the roles of consumer inflows and fund manager behavior in the persistence of fund performance. Specifically, we decompose the returns and costs of each mutual fund into that attributable to (1) manager skills in picking stocks having returns that beat their style-based benchmarks (selectivity), (2) returns that are attributable to the characteristics (or style) of stockholdings, (3) trading costs, (4) expenses, and (5) costs that are associated with the daily liquidity offered by funds to the investing public (as documented by Edelen (1999)). Further, we construct holdings-based measures of momentum-investing behavior by the fund managers. Together, these measures allow an examination of the relation between flows, manager behavior, and performance persistence.
In related work, Sirri and Tufano (1998) find that consumer flows react about as strongly to one-year lagged net returns as to any other fund characteristic. In addition, the model of Lynch and Musto (2002) predicts that performance repeats among winners (but not losers), while the model of Berk and Green (2002) predicts no persistence (or weak persistence) as consumer flows compete away any managerial talent. Consistent with Sirri and Tufano (1998), and to test the competing viewpoints of Lynch
and Musto (2002) and Berk and Green (2002), we sort funds on their one-year lagged net returns for most tests in this paper. While other ways of sorting funds are attempted.
Data
We merge two major mutual fund databases for our analysis of mutual fund performance. Details on the process of merging these databases is available in Wermers (2000). The first database contains quarterly portfolio holdings for all U.S. equity mutual funds existing at any time between January 1, 1975 and December 31, 1994; these data were purchased from Thomson/CDA of Rockville, Maryland. The CDA dataset lists the equity portion of each fund’s holdings (i.e., the shareholdings of each stock held by that fund) along with a listing of the total net assets under management and the self-declared investment objective at the beginning of each calendar quarter. CDA began collecting investment-objective information on June 30, 1980; we supplement these data with hand-collected investment objective data from January 1, 1975.
The second mutual fund database is available from the Center for Research in Security Prices (CRSP) and is used by Carhart (1997). The CRSP database contains monthly data on net returns, as well as annual data on portfolio turnover and expense ratios for all mutual funds existing at any time between January 1, 1962 and December 31, 2000. Further details on the CRSP mutual fund database are available from CRSP.
These two databases were merged to provide a complete record of the stockholdings of a given fund, along with the fund’s turnover, expense ratio, net returns, investment objective, and total net assets under management during the entire time that the fund existed during our the period of 1975 to 1994 (inclusive).5 Finally, stock prices and returns were obtained from the CRSP stock files.
Performance-Decomposition Methodology In this study, we use several measures that quantify the ability of a mutual fund manager to choose stocks, as well as to generate superior performance at the net return level. These measures, in general, decompose the return of the stocks held by a mutual fund into several components in order to both benchmark the stock portfolio and to provide a performance attribution for the fund. The measures used to decompose fund returns include:
1. the portfolio-weighted return on stocks currently held by the fund, in excess of returns (during the same time period) on matched control portfolios having the same style characteristics (selectivity)
2. the portfolio-weighted return on control portfolios having the same characteristics as stocks currently held by the fund, in excess of time-series average returns on those control portfolios (style timing)
3. the time-series average returns on control portfolios having the same characteristics as stocks currently held (style-based returns)
4. the execution costs incurred by the fund
5. the expense ratio charged by the fund
6. the net returns to investors in the fund, in excess of the returns to an appropriate benchmark portfolio.
The first three components of performance, which decompose the return on the stocks held by a given mutual fund before any trading costs or expenses are considered, are briefly described next. We estimate the execution costs of each mutual fund during each quarter by applying recent research on institutional trading costs to our stockholdings data—we also describe this procedure below. Data on expense ratios and net returns are obtained directly from the merged mutual fund database. Finally, we describe the Carhart (1997) regression-based performance measure, which we use to benchmark-adjust net returns.
The Ferson-Schadt Measure Ferson and Schadt (FS, 1996) develop a conditional performance measure at the net returns level. In essence, this measure identifies a fund manager as providing value if the manager provides excess net returns that are significantly higher than the fund’s matched factor benchmarks, both unconditional and conditional. The conditional benchmarks control for any predictability of the factor return premia that is due to evolving public information. Managers, therefore, are only labeled as superior if they possess superior private information on stock prices, and not if they change factor loadings over time in response to public information. FS also find that these conditional
benchmarks help to control for the response of consumer cashflows to mutual funds. For example, when public information indicates that the market return will be unusually high, consumers invest unusually high amounts of cash into mutual funds, which reduces the performance measure, “alpha,” from an unconditional model (such as the Carhart model). This reduction in alpha occurs because the unconditional model does not control for the negative market timing induced by the flows. Edelen (1999) provides further evidence of a negative impact of flows on measured fund performance. Using the FS model mitigates this flow-timing effect. The version of the FS model used in this paper starts with the unconditional Carhart four-factor model and adds a market factor that is conditioned on the five FS economic variables.
Decomposing the Persistence in Mutual Fund Returns
Sirri and Tufano (1998) find that consumer flows react about as strongly to one-year lagged net returns as to any other fund characteristic. In addition, the model of Lynch and Musto (2002) predicts that performance repeats among winners (but not losers), while the model of Berk and Green (2002) predicts no persistence (or weak persistence) as consumer flows compete away any managerial talent. Consistent with Sirri and Tufano (1998), and to test the competing viewpoints of Lynch and Musto (2002) and Berk and Green (2002), we sort funds on their one-year lagged net returns for the majority of tests in the remainder of
this paper. When appropriate, we provide results for other sorting approaches as well.
中文译文
资金真的是“聪明”吗？关于共同基金流动，经理行为和绩效持续性
关系的新证据
作者：Russ Wermers
此外，基金的复苏在多年期间强烈持续- 这是本文的核心发现。

此外，消费者和基金经理行为在解释这些长期延续模式方面发挥了重要作用- 消费者大量投资于去年的获胜基金，而这些获胜者的经理们将这些投资者投资于动量股票流动，以至少继续超过其他基金在定位年后的两年。

相比之下，资金不足的管理者似乎不愿意出售亏损的股票来购买新的动力股，这可能是由于处置效应。

因此，势头继续将失败的经理人的胜利与先前的研究所显示的相比要长得多。

更令人惊讶的是，获得资金回报的持久性还没有被动力所解释- 我们发现有力的证据表明与流动相关的购买，特别是在增长型基金中，推动股价上涨。

横截面回归分析表明，这些异常收益与资金流入密切相关，但并非最终，在风格调整净收益水平上，我们发现没有持续性，与先前研究的结果一致。

总的来说，我们确认资金在追逐获胜经理人方面很聪明，但模仿获利基金股票交易以利用流动相关回报的“模仿”策略似乎是Martest策略。

目前有八千八百万人持有美国共同基金的投资，其中90％以上的投资价值都在托管基金中。

此外，管理的股权获得了大部分消费者
流入- 新净流入2000年流入总流入减去流出总额达到3090亿美元，这使得这些基金的投资总值几乎达到4万亿美元尽管大多数个人投资者显然相信积极管理的普遍美德，但许多人似乎对基金经理小组的人才抱有更强烈的信念- 他们似乎认为，在积极管理者领域，高级管理人员可以长时间“打败市场”。

尤其是，晨星和理柏通过定期的基金表现排名争夺这些真正的信徒的关注，而Money Magazin等常见的出版物则经常介绍“明星”共同基金经理。

此外，投资者的美元尽管不会很快放弃过去亏损的资金，但积极追逐过去的赢家（例如，参见Sirri和Tufano（1998））。

这些“表现追逐者”浪费他们的金钱和时间，还是金钱“聪明”？过去的几篇论文试图解决这个问题，结果有些不同。

例如，Grinblatt 和Titman（1989a，1993）发现，从基金经理的角度来看，一些相互兼容的Grser（1996）和Zheng（1999）考官坚持认为，在费用和开支之前可以持续地获得不成熟的早期收入，而Brown和Goetzmann （1995）; BG）属性持续性到资金不足步骤产生负面的异常回报。

消费者资金流向资金，发现资金是“聪明的”- 也就是说，资金流入的资金不成比例地表现出良好的未来回报。

然而，智能货币效应的确切来源仍然是一个难题- 智能货币捕捉经理人才，或者简单地说就是股票回报的势头？1最近，卡尔特（Carhart，1997）研究了美国共同基金净回报率的持续性，控制（见例如Fama和French （1992,1993,1996），Jegadeesh和Titman（1993），Daniel和Titman （1997）以及Moskowitz和Grinblatt（1999））。

Carhart发现优秀基
金的证据不断上升，他们的表现一直超过他们的风格基准- 具体来说，Carhart发现在一年内最高净回报十分位数（CRSP共同基金数据库）的资金在下列期间以最低的十分位数高出3.5个基点几乎所有的原因都是由Jegadeesh和Titman（1993）记录的一年动力效应以及最低的上年回报率十分不明原因的资金表现不佳所致。

因此，Carhart （1997）认为资金不是很聪明。

有希望的结果比Carhart（1997）。

Chen，Jegadeesh和Wermers（1999）发现，基金最积极购买的股票价格超过了被动销售量的两倍以上，Bollen和Busse（2002）发现季度基金业绩持续存在的证据。

Wermers（2000）发现，尽管平均共同基金的平均风格调整净回报率为负值（与Carhart的研究一致），但高周转率基金的净回报率显着高于低周转率基金。

此外，这些高风险基金选择足够的股票来支付其成本，甚至可以调整基于风格的回报。

这一发现表明，更频繁交易的基金经理拥有持续的储备人才。

所有这些论文都比之前的研究提供了一个更为有利于平均成熟管理基金的观点，也没有一个没有关注投资组合持有数据的持久性问题。

本研究使用投资组合持有量和净收益率数据来检验共同基金持续性问题，从而比以往的研究更全面地分析问题。

数据显示，我们可以制定措施，让我们考察消费者流入和基金经理行为在基金表现Ance持续性中的作用。

具体而言，我们将每个共同基金的回报和成本分解为以下因素：（1）经理技能选择的股票的回报超过基于样式的基准（选择性）;（2）归因于特征（或风格），（3）交易成本，（4）费用，（5）与基金向投资大众提供日常流动性相关的成本（如Edelen（1999）所记录）。

此
外，我们构建Combineds，这些措施允许检查流量，经理行为和绩效持久性之间的关系。

在相关工作中，Sirri和Tufano（1998）发现，消费者流动对其他基金特征的反应强于一年滞后净收益。

此外，Lynch和Musto（2002）的模型预测，赢家（但不是输家）的表现会重复，而Berk和Green （2002）的模型预测，随着消费者流动与任何管理人才竞争，没有持续性。

与Sirri和Tufano（1998）一致，为了测试Lynch And Musto（2002）和Berk and Green（2002）的竞争观点，我们对本文中大多数测试的一年滞后净收益进行了分类。

尝试其他分类方法。

数据
我们合并了两个主要共同基金数据库来分析共同基金的表现。

Wermers（2000）提供了合并这些数据库过程的详细信息。

第一个数据库包含1975年1月1日至1994年12月31日期间任何时间存在的所有美国股票共同基金的季度投资组合;这些数据购自马里兰州罗克维尔的Thomson / CDA。

CDA数据集列出了每个基金持有的股票部分（即该基金持有的每只股票的持股量）以及每个日历季度开始时管理的净资产总额和自我声明的投资目标的列表。

CDA于1980年6月30日开始收集投资客观信息;我们从1975年1月1日起用手工收集的投资客观数据补充了这些数据。

第二个共同基金数据库可从安全价格研究中心（CRSP）获得，由Carhart（1997）使用。

CRSP数据库包含每月净回报数据以及1962年1月1日至2000年12月31日期间任何时间存在的所有共同基金
的投资组合周转率和费用率的年度数据。

有关CRSP共同基金数据库的更多详情来自CRSP。

这两个数据库合并在一起，以提供给定基金持股的完整记录，以及基金在我们期间存在的整个期间内基金的营业额，费用比率，净收益率，投资目标以及管理的总净资产1975年至1994年期间（含）.5最后，股票价格和收益率来自CRSP股票文件。

绩效分解方法在这项研究中，我们使用了一些量化基金经理选择股票的能力的量度，并在净收益水平上产生出色的表现。

一般而言，这些措施将共同基金所持股票的收益分解为若干组成部分，以便对股票投资组合进行基准比较并为基金提供业绩归因。

用于分解资金回报的措施包括：
1.基金目前持有的股票投资组合加权回报超过（在同一时期内）具有相同风格特征（选择性）的匹配控制组合的回报
2.控制权投资组合的投资组合加权回报率与基金目前持有的股票具有相同的特征，超过这些控制权投资组合的时间序列平均收益（风格时机）
3.具有与目前持有股票相同特征的控制组合的时间序列平均回报（基于风格的回报）
4.基金发生的执行成本
5.基金收取的费用比率
6.基金中投资者的净收益，超过适当基准投资组合的收益。

下面简要介绍在考虑任何交易成本或费用之前分解给定共同基
金持有的股票回报的前三部分业绩。

我们通过将最近的机构交易成本研究应用于我们的持股数据来估算每个共同基金在每个季度的执行成本- 我们也在下面描述了该程序。

费用比率和净收益数据直接从合并共同基金数据库中获得。

最后，我们描述了基于回归的Carhart （1997）绩效衡量标准，我们用这个衡量标准来调整净收益。

Ferson-Schadt措施Ferson和Schadt（FS，1996）在净收益水平上开发了一个有条件的绩效指标。

实质上，如果管理者提供的超额净收益远高于基金的匹配因子基准，无论是无条件的还是有条件的，该措施都将基金经理标识为提供价值。

有条件的基准控制因发展中的公共信息而导致的因素回报溢价的任何可预测性。

因此，如果管理者拥有优质的股票价格隐私信息，那么他们只会被贴上优秀标签，而不会因为公众信息而随时间改变因素负载。

FS还发现，这些有条件的基准有助于控制消费者现金流对共同基金的反应。

例如，当公共信息显示市场回报会异常高时，消费者会将异常高额的现金投入到共同基金中，这会降低无条件模型（如Carhart模型）的绩效指标“alpha”。

由于无条件模型无法控制由流引发的负市场时机，因此发生α的减少。

Edelen（1999）提供了流量对衡量基金业绩的负面影响的进一步证据。

使用FS模型可以减轻这种流量计时效应。

本文中使用的FS模型的版本从无条件的Carhart四因子模型开始，并增加了一个以五个FS 经济变量为条件的市场因素。

分解基金回报的持久性
Sirri和Tufano（1998）发现，与任何其他基金特征相比，消费者
流动对净收益的回报幅度都很大。

此外，Lynch和Musto（2002）的模型预测，赢家（但不是输家）的表现会重复，而Berk和Green（2002）的模型预测，随着消费者流动与任何管理人才竞争，没有持续性。

与Sirri和Tufano（1998）一致，并为了测试Lynch和Musto（2002）以及Berk and Green（2002）的竞争观点，我们将资金分为大部分测试的一年滞后净收益这张纸。

适当时，我们也提供其他分类方法的结果。