在上世纪70年代第一支指数基金诞生之前，主动管理曾一度为投资者寻求外部财富管理首选（Bogle 1997）。此后，其价值一直为学术界所热议，基于以下三个原因，公募基金成为这个领域研究的“香饽饽”。第一，指数投资和ETF为代表的被动投资的风行；第二，统计方法使得研究者可以快速而稳健地衡量各种金融产品表现；第三，公募基金的结构、关注度、以及数据的可获得性。

自Jenson（1968）以来，基于数量和规模之众，美国权益公募基金研究成为焦点。一般认为，“价值寻优”的努力值得所偿（Sharpe，1966），然而早期的文献发现，扣费之后，公募基金无法战胜市场。对传统观点最为凝练的总结莫过于Mark Chrhart 1997的研究：“没有证据表明主动基金经理有能力获得超额收益”。

本文回顾了大量的、自1997年以来讨论公募基金价值这一领域的文献。我们首先讨论了传统观点的支持者；其次更多的，我们考虑了近些年的研究（大多是挑战者）。在类型上，我们首要聚焦于美国权益基金，此外，我们也关注债券型和混合型基金。最后，我们讨论了近些年来的文献的局限性、重要的开放性问题，以及主动权益公募基金的研究结论的普适性程度。

整体上，文献回顾显示：对于主动管理的负面评价，传统观点言过其实。整体上，过去20年间的文献认为，主动基金具备管理能力，可做出增值决策；也即是，在扣费后，主动基金依然有超额收益。所以，我们认为传统观点有失偏颇。

自1997年以来，公募基金行业变化显著，在这一章，我们讨论了这个行业的发展、被动投资的市占率的提升、和投资管理费用的下降。

美国公募基金行业在1998年-2017年间变化剧烈，注册投资公司的净资产几乎翻了两番（或者说增长了三倍，从5.8万亿到22.5万亿美元），但是传统的开放式基金只增长了两倍，这一增速的差别归咎于ETF的兴起。

ETF和传统的指数基金在市占率增速上保持一致。在1997年，美股权益基金资产中，指数基金的规模占比从1997年的7.5%上升到2017年的29.6%，在2017年，大多数资金为被动投资策略所吸纳。在1997年到2017年间，美国权益指数基金的净流入为6390亿美元，而主动基金净流出9750亿美元。这一现象在债券和混合型基金里并不明显——在总计5.6万亿的规模里，指数基金仅占11.2%。

与此同时，主动管理的费用率在下降，资产规模加权费用率从2000年的1.06%下降到了2017年的0.78%，债券基金的平均费用率也从0.78%下降到了0.55%。这个下降趋势部分归咎于资金对于低费率基金的倾斜。

主动基金的间接费用、交易频率双降（Cremers and Pareek 2016）。尤其是在2016年，主动基金的平均换手率只有34%。此外，Hasbrouck（2009）和Bessembinder，Maxwell 和Venkataraman(2006)的结论显示，权益和债券市场的交易成本均有下降。

由此，当前的主动基金，和传统文献所处的那个年代相比，运营环境迥异——当前主动管理者面临同类型、被动基金的双重竞争，以及费率压缩。在此趋势下，不禁要问，Jenson(1968) - Carhart (1997)年间诞生的传统观点还适用吗？下一章的文献回顾中，我们讨论传统观点支持者；在第四章，我们聚焦于主动管理能力（近些年的文献讨论更多）。

传统观点认为：平均来看，主动管理者几乎不创造价值，体现在：1）扣除费率后跑输市场；2）冠军基金的业绩不能持续；3）基金经理的能力不值所偿。围绕以上三点，本章逐一总结。

第一点：主动基金跑输同类型的被动基金。理论上讲，在超额层面，主动投资者在进行一场“零和博弈”，因此，剔除掉费率之后，整体主动基金和同行们必然进行着“负和博弈”。Sharpe（1991，2013）将此现象称为“基金管理的算数问题”（arithmetic of fund management），Bogle将其称为“朴素算术视角下的丛林法则”（relentless rule of humble arithmetic）。基于此，French(2008)得出结论：“寻求交易收入，投资没有出路”（In aggregate, the search for trading gains is doomed）。Buffet（2016）也说过：“投资收入中，流入‘理财助手’腰包的占比，又一次刷新了记录；而这，本应全为投资者所得。”（a record portion of the earnings that would go in their entirety to owners is now going to a swelling army of Helpers）。

下面几篇文章认为：美国主动权益基金扣费后跑输市场。Jenson(1968)以Gruber(1996)使用了1945-1994年间非重叠的样本，发现：扣费后公募基金平均超额收益为负。类似的研究还有Davis(2001)。Wermers（2000）也指出，主动管理者在扣费之前也进行着“零和博弈”，他发现平均来看，主动管理者持有的股票每年能够战胜市场1.3个百分点；而扣费之后，则跑输1个百分点。

第二点，冠军基金业绩不能持续——“运气还是实力”？可能是运气。诚然，早些的文献发现了业绩持续性的证据，但Carhart（1997）发现，基金的“热手效应”（原文是：hot hand effect，即业绩持续好）可被其持有股票收益的共同因子所解释，尤其是动量因子（Jegadeesh and Titman，1993）。Carhart发现只有“输家”基金的业绩可以持续，其结论的说服力可体现在，其使用的数据包含已经停止运营的基金，规避了幸存者偏差。正如Elton，Gruber and Blake（1996）指出，因为“幸存者”往往实现了超额收益，所以若只研究现存基金，容易得出主动基金为“能者”的结论。此外，Brown，Goetzmann，lbbotson and Ross（1992）的发现也类似。

同时期的其他研究大多支持Carhart的结论。比如Malkiel（1995），以及Phelps and Detzel(1997)——发现当风险衡量恰当或考虑更近期的样本时，主动管理者的业绩持续性就消失了。

第三点，主动管理有“能者”，但“要价偏高”。这个层面的讨论并未否定主动管理者的能力，正如Fama and Frech（2010）指出：“很难看到超额收益可覆盖费用的基金”。Barras, Scaillet and Wermers（2010）给出了定量结论：只有0.6%的基金为“能者”，且值所偿；有75.4%的基金为能者，但难值所偿。此外，他们发现，“能者”占比从1990年初的14.4%下降到了2006年末的0.6%。

直切“业绩持续性下滑”要害，传统观点深入人心。

第一种解释：Bernstrin（1998）认为业绩持续下滑，是因为权益市场变得更有效。Chordia, Roll，and Subrahmanyam（2008，2011）以及Conrad, Wahal, and Xiang (2015证明了，近些年来，权益市场有效性确有提高。

第二种解释：“群雄逐鹿”（原文：投资机会的减少叠加主动管理者的竞争加剧）。Pastor，Stambaugh，and Taylor（2015）将主动基金的业绩削弱归为：虽能者渐多，但竞争渐烈。Dyck，Lins，and Pomorski（2013）也支持该结论：在非美市场，竞争愈小，主动管理价值愈大，这一现象在新兴市场尤为突出。类似的研究还有Hoberg，Kumar and Prabhala(2018)：在美国市场上，当主动管理者面临的竞争环境改善，其业绩也随之改善。

也有文献提出警告：论文发行过程偏好统计意义显著的、提供新的洞见的文章，不青睐单只论证支持（或无法证伪）传统观点的文章。Rosenthal（1979）发文指出：期刊青睐统计显著性，使得大约有5%的文章存在第一类错误（false positive）；统计不显著，则无人问津。Galiani，Gertler, and Romero（2017）进一步指出，“结果复制只是例外，绝非主流”，最后，在一些有着“广泛的……检验关系”以及“在设计、定义、结果和和分析模型上有很大的灵活性”的研究成果很难为真（Loannidis 2005）。在金融领域，相关性随处可见，那么，主动管理研究在检验识别关系设计方面的灵活性很大。这个境况使得：1）我们无法遍历传统观点的支持者；2）传统观点的反对者可能被“拒绝真”了。（译者注：“拒绝真”原文是false positive，原指拒绝了正确的假设，这里译者的理解是：反对传统观点的文献被期刊拒绝了，而他们的结论可能是正确的）

传统观点在近些年不乏支持者，但挑战者更多。近些年来的研究，发现：许多主动管理者的能力显而易见，创造真价值，具有持续性。他们往往用了新的衡量方式（比如，Berk and van Binsbergen （2015）衡量的“增值”）或是采用了创新方法（比如，Barras et al., 2010使用的“错误发现”（false discoveries）方法），再者（比如Daniel，Grinblatt，Titman and Wermers, 1997）使用了新的数据（如个人基金持有）。本章总结了近些年来，美国权益公募基金的研究成果；第五章，我们讨论其他资产类别。

用超额收益来衡量主动管理人的能力的文章随处可见。大多数情况下，基准要么是基准指数，要么是多因子模型（比如Carhart 1997），又或着是基金的持有组合（Daniel et al. 1997）。基准模型的选择和数据的质量，很大程度上影响超额收益的衡量，继而影响到讨论主动管理者是否具备能力的结论。

下面的文献考虑了基准模型选择问题。Kothari and Warner（2001）以及Glode（2011）强调，当前的模型在衡量主动管理者能力的方面有局限性，而且往往低估。Glode提到，若模型无法适当衡量经济状态（衰退或着扩张），那么有能力的管理者“将（错误的）被认为是扣费后跑输市场的”。此外，Huij and Verbeek（2009）和Cremers，Petajisto and Zitzewits（2013）认为：多因子模型为文献所常用，但存在系统性偏差，所以其用来衡量公募基金业绩的基准不适合。

数据层面，Linnainmaa（2013）的研究表明：诚然, 若数据集存在幸存者偏差，那么主动管理者能力会被夸大；但由于“反向幸存者偏差”，没有幸存者偏差的数据集则会低估主动管理者的能力。这是因为，一些基金因为“运气差”而非“能力差”导致业绩不佳而关停，若未被关停，他们有理由在未来表现更好。所以，平均超额收益这个指标低估了基金经理的能力。Linnainmaa发现，若考虑了反向幸存者偏差，真实的年化超额收益可提升43 bps。

因具有私有信息，内部从业人员的行为特征表明，一些基金管理人为“能者”。Berk，van Binsbergen，and Liu (2017) 的研究表明：资产管理公司似乎具有有价值的私有信息，因其可以在管理人之间有效地分配资金，这种资金再配置贡献“占了行业总增值部分的30%”。Fang，Kempf，and Trapp（2014）发现，基金可以有效的安排管理人达到“能者多偿”。Porter and Trifts（2014）发现，基金经理表现不佳，更易失业。Gallo and Lockwood（1999）发现，在管理安排上发生变动之后，平均来看，基金的风险调整后收益每年提高2%。Khorana(2001)显示，在管理者被重新安排之后，基金表现显著提升。

基金集团（fund families）试图保留基金经理中的能者。Deuskar，pollet，Wang， and Zhang（2011）发现，在面对来自对冲基金的竞争时，基金公司倾向于保留顶尖的管理人；一个方法是，请公募基金管理人同时兼顾管理私募基金。Nobel, Wang and Zhang(2011)发现，这种协同管理方式使管理人可以战胜同行，和“优秀管理人获得更多的资源倾斜”的观点保持一致。Cici, Gibson and Moussawi (2010) 和Del Guercio, Genc andTran（2018）发现这类管理模式的公募基金反而跑输。

并非所有的研究都支持上述结论。比如Goyal and Wahal（2008）发现更换基金经理并不能改善业绩；Kotovetsky and Warner(2015)发现更换外部管理人也不能改善业绩；Phillips, Pukthuanthong and Rau（2014）发现，孰为最强对手？管理人无法精确判断。

前一小节，我们讨论了内部人士识别能者的问题，但大量的研究还是聚焦于，透过公开信息，投资者能否识别“能者”。Kosowski, Timmermann, Wermers, and White(2006)发现：“这部分‘为能者、值所偿’的管理人数量可观”；并且，他也找到一些 “赢家基金”筛选指标——主要基于净值，持仓或二者结合的一些衍生信息。

还有些研究发现对基金未来业绩的预测，历史业绩可借鉴。比如Bollen and Busse（2005）从日度收益数据中发现了业绩的持续性，虽然“仅当基金每年被多次评估，才会被观测到”；以及Kosowski et al.(2006) 基于Carhart（1997）方法流程，在评估统计显著性方面，使用了更加稳健的方法，来展示这种业绩的持续性。

除历史表现外，许多研究也使用了基于收益数据的其他衍生信息来预测未来表现。Amihud and Goyenko(2013) 考察了收益能够被共同因子解释（比如市值因子）的基金的占比，发现：平均来看，历史业绩优秀的公司和对共同因子暴露低的基金，每年战胜市场3.8%。Massa and Yadav(2015)，用历史收益来衡量基金的“情绪beta”——基金对高情绪股票的暴露，发现情绪beta低组在之后可以战胜高组。

其他的研究采用了复杂的统计方法来评估收益。Barras et al.（2010）借鉴了医学研究的方法——“假发现”（false discoveries）来把业绩好的基金中，将能力成分从运气成分中分离出来。Busse and Irvine（2006）；Avramov and Wermers（2006）；以及Huij and Verbeek（2007）使用了贝叶斯概率方法成功地预测了未来表现。Mamaysky，Spiegel and Zhang（2007）通过剔除常见的收益分析中误差，发现了年化超额收益超过4%的组合。Baks，Metrick，and Wachter（2001）以及Pastor and Stambaugh（2002）展示了主动管理基金应该是投资者最优组合里的一部分，即使他们对其表现心存疑虑。

对基金未来业绩的预测，基金持有信息可借鉴。Cremers and Petajisto（2009）展示了具有更高的主动持有占比（意味着其对基准偏离很大）的基金往往可以战胜市场，而低主动持有占比基金则跑输。Cremers and Pareek（2016）展示了，在高主动持有基金中，这种业绩差异分化度（可以解读为“耐性”）更明显。Cremers，Fulkerson and Riley（2018）发现，如果跟踪基准（基金合同里有描述）准精确地衡量了基金的风险的话，那么基金未来业绩更优。Doshi，Elkamhi and Simutin（2015）将基金的实际组合和其持有的证券价值加权组合进行对比，发现若二者的区别越大，那么基金表现越佳。Sherrill，Shirley and Stark（2017）发现持有更多的ETF的基金未来表现往往更差。

对于基金的主动持有，争论的焦点围绕：1）过去是否预测了以及2）未来能否持续预测基金的超额收益。Schlanger，Philips and LaBarge（2012）以及Cohen，Leite，Nielson and Browder（2014）均对主动持有的预测能力表示质疑。Frazzini，Friedman and Pomorski（2016）发现在实证上，主动持有这个衡量方式并不稳健。相反， Petajisto(2016)以及Cremers（2017）争论说，Frazzini et al.的分析有明显的短板（比如，衡量基金表现的模型和对结果的解读）。不论主动持有是否有效，Brown and Davies（2017）的研究表明，如果投资者有意基于主动持有做决策，那么管理人就有动机来做手脚。

基金持有集中度也有预测力。Kacperczyk，Sialm，and Zheng（2005）发现在某些行业持有集中度高的基金会跑赢持有集中度低的基金。Goldman，Sun，and Zhou（2016）发现如果基金持有集中度体现在，分行业看前一大持有或着前二大持有的话，那么该基金表现更佳。通常来看，持有集中度高的业绩一般更好（Huij and Derwall 2011；Hiraki，Liu，and Wang 2015；Choi，Fedenia，Skiba and Sokolyk 2017；Fulkerson and Riley 2019）；虽然也有不同的声音（Sapp and Yan 2008）。

一些研究兼顾历史收益和持有。比如，Kacperczyk，Sialm and Zheng（2008）展示了交易产生的收益（基金的实际收益和假定基金维持披露的持有证券不变的收益差）对业绩有预测力；类似的，Groenborg，Lunde，Timmermann，and Wermers（2018）同时采用持有和收益来预测未来的表现。

基金经理的履历对业绩也有预测力。Chuprinin and Sosyura（2018）发现贫寒出身的基金经理倾向于战胜富家子弟的同行。早期的研究中，Shukla and Singh（1994）；Golec（1996）；以及Chevalier and Ellison（1999）发现管理人的教育的诸多层面对业绩有预测力。然而，Gottesman and Morey（2006）显示，在众多的教育衡量指标中，只有管理人的MBA项目才和未来的收益相关。

也有证据表明管理者持有（原文为：ownership stake）对于基金表现有预测力。Khorana, Servaes, and Wedge(2007)展示了“管理者持有每提高1 bp，那么基金表现将提高3bps”；类似的，Evans（2008）也发现了管理者持有和未来业绩的正相关关系；Cremers, Driessen, Maenhout, and Weinbaum(2009)展示了管理者低持有的基金往往跑输。

虽然谈到管理人的能力，文献通常所指产生超额收益的能力，但是“能力”一词就价值创造而言，也有多种诠释。

首先，选股能力关注度最高。Daniel et al.（1997）以及Wermers（2000）从基金持仓中提取选股能力对业绩的贡献；具体做法是，分成分股个股来看，找到与其特征类似的基准（市值，BP，前期涨幅）进行比较，发现平均来看，基金的选股可以战胜市场。然而，Fulkerson（2013）显示，基金经理的这种择股能力逐年递减。Jones and shaken（2005）发现，即使平均来看“不值所偿”，但是管理人的确具备某些选股能力。相关的研究还有：Chen Jegadeesh，and Wermers（2000）发现基金买入的个股显著跑赢卖出的个股；Chen，Desai and Krishnamurthy（2013）发现基金有很强的卖空能力。

相关的研究也探索了选股能力的细微差别。Duan, Hu, and Mclean (2009)发现，对于高特质波动率的股票，管理人择股愈强；Fulkerson（2013）发现大部分的选股能力来自于行业内的选股能力。

其他的研究聚焦于识别选股能力的来源。比如Baker，Litov，Wachter，and Wulger（2010）以及Cai and Lau（2015）认为管理人可以预测股票的盈利，Nain and Yao（2013）发现管理人可以预测公司合并后的股价表现。此外，Chen，Gao，Zhang and Zhu（2018）发现行业分析师的经历有助于放大基金的选股能力。Cohen，Frazzini，and Malloy（2008）结果表明管理人会动用其校友资源去捕捉获利机会。

地理位置亦为择股的重要考量。Coval and Moskowits（1999）发现，或因信息优势，管理人对本地公司有偏好。Giannetti and Laeven（2016）发现，在市场波动率高企时，管理人倾向于卖出远离本地的公司。与本地公司的社交互动也很重要，Hong，Kubik，and Stein（2005）发现管理人买入一支股票可能是因为同城的同行也在买这支股票；这种行为也是理性的，Pool，Stoffman，and Yonker（2015）提供了一个视角：同城同行的买入-卖出组合经风险调整后，有正收益。

如果不考虑交易原因的“类型”，那么管理人的选股能力可能被低估。Cici，and Gibson（2007）展示了：基于估值因素的买入可以显著战胜市场，而基于“处理组合中多余的现金”的买入行为则不会。类似的，Rohleder，Schulte，Syryca and Wilkens（2018）展示了：基于估值因素买入的基金显著跑赢，而基于估值因素卖出的基金则跑输。

虽然文献一般分基金而论，探究选股能力；但是，基金的综合选股能力也有研究。Yao and Zhao（2012）开发了一种方法：如果基金的合并持仓后的个股头寸增加，那么基金将跑赢市场。Gupta-Mukherjee（2013）展示了，基于一群具有相同投资信念、投资类似资产的投资人的组合可以跑赢被动指数。Jiang，Verbeek，and Wang（2014）发现，经风险调整后，主动管理者重仓股可以平均每年跑赢轻仓股7%；作为整体的一组，基金经理似乎可以精确地预计到未来的个股收益。

对于选股能力，发现易，度量难。一方面，Puckett and Yan（2011）发现，因为季度披露的持仓数据无法包含每个季度间的交易信息，所以基于此的选股能力会被系统性低估；另一方面，Nicolosi (2009)论证说，季度持仓数据会高估选股能力。Chakrabarty, Moulton and Trzcinka (2017) 发现，如果按照基金季度持仓信息，不考虑基金在之间的交易情况得话，那么看下来，表现不会太好。

另一个增值能力是择时能力，学术上定义为：能够正确预测未来整体市场或细分市场的方向的能力。Jiang, Yao and Yu(2007)发现，主动管理人有择时能力；Kaplan and Sensoy（2005）发现主动管理人在市场上涨前会主动增加对市场的暴露；Mamaysky, Spiegel and Zhang（2008）显示：“一部分基金（可能高达20%）”有择时能力；类似的，Busse(1999)发现基金可以择时市场波动水平。

很遗憾，由于数据受限，择时能力的检验尤其困难。学者常使用季度持仓数据，但Eltion, Gruber and Blake(2012)使用了月度持仓数据（一般难以获得）之后发现了更多管理人具有择时能力的证据。Bollen and Busse(2001)发现“公募基金的择时能力在日度层面的检验相比月度层面更为显著”。类似的研究还有，Goetzmann, Ingersoll, and Ivkovic (2000)发现，如果管理人的操作是日度择时，那么在月度收益率数据上很难观测到其择时能力。

除了择时和选股之外，文献也发现主动管理人有其他增值的方式。一些管理人对信息处理十分有效，Kacperczyk and Seru(2007)展示了有能力的管理人对于新的公开信息的依赖度较低，比如盈利公告。类似的研究还有，Cullen，Gasbarro，and Monroe(2010)发现，在获取了私有信息的情况下，如果对公开信息采取逆向投资，基金往往表现超群。公开信息的种类也重要：Chuprinin, Gaspar, and Massa(2019)发现，通常来看，基于定量信息进行交易的基金往往跑赢，而基于定性的则不然。

管理人也可通过参与公司治理来创造价值。Iliev and Lowry（2015）发现相比于依赖机构持有者推荐服务的管理人，直接参与公司治理事务能够战胜市场。然而Duan and Jiao（2016）发现，基金往往“用脚投票”而不是“用手投票”。

实践的角度，一些管理人通过税收管理来为客户创造价值。Sialm and Starks（2012）展示了如果基金持有需要交税，那么管理人在策略选择上，往往让客户少交税；Gibson，Safieddine，and Ttiman（2000）发现，相比税前，税后收益对于基金净流入的解释度更高。

最后，基金的价值也体现在保持有纪律的投资方法。Singal and Xu（2011）发现“约30%的基金体现出处置效应行为，这些基金平均每年跑输不具有处置效应的基金4-6%”，因为投资者倾向于避开这类基金，所以无法避免这种偏差的基金关停的概率更高。Cici（2012）发现学习效应减少了处置效应，显示出学术研究在影响行业实践。Del Guercio and Reuter（2014）发现监督程度低的基金显著跑输，所以，投资者对于基金行为的监督很重要。

此前小节也讨论过，主动管理人通过一系列的技能创造价值，这些技能对于投资者的价值会被诸多因素影响，包括市场状况、竞争环境、和基金结构。在下面这个小节，我们关注导致主动管理者价值增加或减少的因素。

管理人技能的价值，受投资机会的变异性影响。Pastor，Stambaugh，and Taylor（2017）发现当基金增加交易频率后，表现会更好，行业内整体交易状况对于未来表现有预测力；他们提出假说：管理人基于市场获利机会的数量，来改变他们的交易。类似的研究还有，Von Reibnitz（2017）发现市场分化度高的时候，基金往往表现更好。Dong，Feng and Sadka（2019）发现：“基金经理创造价值的能力取决于市场流动性环境”。

选股和择时的能力的价值往往因时而异。Kacperczyk，van Nieuwerburgh and Veldkamp（2014）显示能者在熊市擅择时，牛市擅择股。研究显示如果不区分市场状况的话，管理人的能力会被低估。Kosowski(2011)发现，经风险调整后，基金在经济扩张时期跑输市场，在衰退时期则跑赢；由此，他总结出：很多传统的模型低估了主动管理者在衰退时期的价值。

竞争程度也会影响基金经理的能力。Dyck et al.（2013）发现，在竞争较小的资产种类市场，主动管理人更能发挥技能。Hoberg et al.（2018）发现投资于美国的主动基金，当其暴露的风格的拥挤程度较低时，表现会更好；然而，在所有维度上的低激烈竞争并不会带来更好的表现。Cremers，Ferreira, Matos，and Starks（2016）考察了多个不同的国际市场，发现在面临更高程度的低费率ETF的竞争时，主动基金会更加主动积极、费率更低、表现更好。

基金管理团队的结构也会影响主动管理者的价值创造。Dass， Nanda，and Wang（2013）发现单打独斗的管理人往往擅长择时，团队作战的管理人善于择股。Goldman et al.（2016）发现，单打独斗的基金经理“持仓集中度更高，往往表现更好，要价高于多人管理的基金”。然而，Patel and Sarkissian（2017）展示了：在众多的衡量表现的指标中，团队作战的基金表现好于单打独斗的基金。综上，单干还是群策，孰优孰劣尚无定论。

最后，管理人的激励结构也很重要。Eltion，Gruber and Blake（2003）发现若绩效激励和选股挂钩，激励也可能导致风险；Mass and Patgiri（2009）发现更强的激励合同会同时带来更高的风险、更高的经风险调整收益；相反，Golec and Starks（2004）并没有发现承担高风险的证据；Drago，Lazzari and Navone(2010)也显示：强激励合同对收益和风险均没有显著影响；Ma，Tang，and Gomez（2019）阐释了，管理人的总补偿合同差别巨大，但“几乎没有证据表明，未来业绩的差异和某种特定补充合同安排有关”；除却风险和收益的讨论，Dass，Mass and Patgiri（2008）发现：“激励安排诱使管理人投资趋同”。

主动管理人的“零和博弈”理论在细节上有所欠缺。Pedersen（2018）认为，这种基于算数的论点“是基于市场组合一层不变的显性假设，现实中并不存在；因为存在新股发行、回购、指数重构，所以即使是被动投资这也要时常做交易”。他表示一旦这个假设放松之后，“总的来说，主动管理者收管理费很合理。”

若指数重构，被动管理人自然也要随之切换，这使得主动管理人可以从中获利。Chen，Noronha, and Singal（2006）显示：围绕着标普500指数和罗素2000指数的套利活动，使得被动管理基金每年给主动基金贡献10-20亿美元的财富转移。

Berk and Van Binsbergen（2015）提出，主动管理者的能力应该用基金的毛超额收益乘以其管理规模。也就是，主动管理者的从资本市场“提钱”的数额。这个视角下，他们发现，平均每年每一支主动基金（包括非权益在内的所有基金类型）为投资者创造320万美元的收入；此外，他们发现，平均来看，每年主动管理者跑赢类似的风格指数36 bps。

Berk and van Binsbergen(2015)认为：“阿尔法取决于竞争驱使的均衡而非管理者的能力”。他们在Berk and Green（2004）提出的模型之上建立了公募基金行业的理论模型；模型假设规模经济递减（业绩随着规模增加而下降），投资者竞相在基金之间分配资金；在模型均衡的水平下，有能力的管理人不断的接纳更多的资金，其获取超额收益的能力也随之下降；所以，在均衡水平下，无论是否具备能力，所有的管理人的预期超额收益相同。在这个视角下，Berk（2005）提出两点迷思：1）主动管理者给投资者创造收益的数额反应了能力；2）因为平均来看，主动管理者无法战胜市场，所以平均水平的管理人不创造价值。

Berk and Green（2004）模型若要成精确刻画公募基金行业状况，那么必须满足两点：1）资金充分分配以及2）规模经济递减要成立。对于第1点，以Chevalier and Ellison（1997）以及Sirri and Tufano（1998）为起点和代表的诸多研究表明资本确实完全分配；虽然资金流动并非高效，但是赢家基金往往吸纳资金，而输家资金往往经历资金净流出。Wahal and Wang（2011）总结道：“公募基金行业体现出竞争市场的典型特征。”

并非所有的资金分配渠道是同等竞争性的，基金可以直接卖给投资者也可以通过中介机构。Del Guercio and Reuter（2014）展示了，中介机构销售基金的方式会减少资金分配的效率。Christoffersen，Evans，and Musto（2013）发现，中介机构的动机（比如，买卖规模）对于中介机构如何引流资金起着很重要的作用。Bergstresser，Chalmer，and Tufano（2009）研究发现，这些由中介机构卖出的基金“即使在扣除中介费用之前”跑输市场，表明完全资金分配的假设没有实证支持。相比于管理者能力的比较，投资者更加倾向于基于信任来分配资金（Gennaioli Shleifer and Vishny 2015）或着基于管理者的老练程度（Chalmers，Kaul，and Phillips 2013）。Garleanu and Pedersen（2018）总结道：市场分割会削弱Berk and Green（2004）模型的第1个假设。

对于第二个假设：学术界也尚无定论。Chen, Hong, Huang and Kubik (2004); Yan(2008)；Pator et al.(2015); Harvey and Liu (2017); Zhu (2018); 以及Mclemore(即将发表)找到了公募基金行业的规模经济递减的证据。但是Reuter and Zitzewits (2015); Phillips, Pukthuanthong, and Rau(2018); 以及Adams, Hayunga, and Mansi(2018)发现基金规模和表现没有关系。Ferreira, Keswani, Miguel, and Ramos(2013)发现了对于特定的基金种类来说，存在规模经济递减；尤其是，他们发现投资海外的基金不会受到规模的负向影响。因此，在规模经济递减尚无定论以及竞争性资本分配并不完整的境况下，基于Berk and Green（2004）模型的研究在解读上要尤其谨慎。

前一个章我们考虑了在1997年以后发表的，聚焦于美国公募基金的文献。那么我们自然思考对于其他资产类别，这些文献的结论是否适用；然而，很少有文献研究传统观点是否适用于其他大类资产或投资方法；此外，过去20年间美国权益基金的研究的先进之处也很少应用到其他基金类别上。在以下各个小节，我们简要的考虑了债券基金、混合基金、社会责任基金、目标日基金、房地产信托基金（REIT）、行业基金、国际基金的研究。

诸多证据表明，主动债券基金管理人具备能力。通过研究债券基金的买入和卖出的行为，Moneta（2015）发现债券基金在扣费后有超额收益；Gutierrez，Maxwell，and Xu（2009）找到了业绩持续性的证据；和权益基金研究类似的发现是，Amihud and Goyenko(2013)发现如果共同因子对基金收益的解释度低的话，那么该基金后期表现可能强势；Chen, Ferson and Peters（2010）以及Cici and Gibson（2012）发现债券基金管理人具备择时能力；而Boney, Comer and Kelly(2009)展示了管理人难以对择收益率曲线择时。整体来说，债券基金能够为投资人做出有价值的决策，与美国权益基金的研究也保持一致。

然而当考虑净收益的时候，债券基金平均来看跑输。Moneta(2015)发现，债券基金扣费前跑赢，扣费后跑输，这一结论也适用于美国国债基金（Ferson，Henry，and Kisgen 2006）、美国企业债基金（Cici and Gibson 2012）、加拿大基金（Ayadi and Kryzanowski 2011）和全球债券基金（Detzler 1999）。Jones and Wermers (2011)发现大部分的债券基金在2008-2009年衰退期跑输基准。Choi and Kronlund(2018)发现在低利率环境下“收益饥渴”的企业债基金经风险调整后收益为负。

对于主动债券基金负收益研究，有些值得警惕的点：即使货币经济学和金融学对利率和固定收益投资收益有较多的研究，但是对于控制债券组合风险的模型还没有达成共识；于是乎，文献中使用的模型很广泛。图表1总结了过去的研究中使用过的模型；此外，Bai，Bali，and Wen（2019）开发了一个新的模型聚焦于企业债的下行风险、信用风险、流动性风险。这个模型如果用在债券基金上，可能会带来新的洞见。但是，就哪个模型能够最好地帮助我们理解债券基金表现的这个问题，学术界还存在鸿沟。

混合型基金即混合权益和固收资产的基金。在这些有限的研究混合型基金的文献中，大部分聚焦于在衡量其表现的时候会出现的问题：比如，对于混合型基金来说，如果采用传统的股票因子模型来定价，会发现其超额收益为正；但是Comer et al.（2009）发现如果纳入债券定价因子之后，起码存在子样本表明其超额收益为负。Herrmann and Scholz（2013）聚焦于风格基准来控制风险，虽然他们没有发现混合基金跑赢市场，但发现了其短期的收益持续性的存在。

因为混合基金可以对于分配股债配置的权重上较大自由度，所以这些基金的管理人体现出超群的择时能力。具体来看，他们基于股债的预期回报来调整各自的权重。Comer（2006）发现了混合基金子样本里管理人的择时能力；Comer，Larrymore and Rodriguez（2008）发现，在熊市里混合型基金可以跑赢基准。

社会责任基金，也称为环境、社会、和公司治理(ESG)，近些年来增长迅猛。包括新ESG ETF在内的这类基金的资产规模在2013年至2015年翻倍（Davidson 2015）。投资者对这类基金的青睐，是因为他们想鼓励或规避某种公司治理行为。Riedl and Smeets（2017）发现一些“投资者可以不顾业绩上的表现的考虑来达到其社会责任偏好的目的”。对于ESG的研究很多，但是对于ESG公募基金的研究却很少。

现存的研究ESG基金的文献聚焦于，基于社会责任策略来限制组合投向带来的不利影响。在现代组合理论视角下，受限的投资组合会被不受限的所主导，所以如果我们基于ESG的标准有意剔除股票会导向一个次优组合。而ESG筛选器对于基金的影响则另当别论。

一方面，Grinold（1989）；Adler and Kritzman（2008）；以及El Ghoul and Karoui（2017）发现这种限制会给投资者带来成本。另一方面，Hamilton, Jo and Statman（1993）；Statman（2000）；Bauer, Koedijk, and Otten（2005）；Renneboog，Ter Horst，and Zhang（2008a）；Meziani（2004）；以及Dolvin，Fulkerson，and Krukover(2019)的研究表明：在经风险调整的角度，ESG基金其实并不优于也不劣于传统基金。Renneboog，Ter Horst，and Zhang（2008a）对当时的ESG文献进行了详尽的回顾，得出结论：“几乎没有证据表明，美国和英国的ESG基金和传统的基金在风险调整后收益层面有什么区别”，但是“欧洲大陆和亚太地区的ESG基金跑输基准组合。”

虽然社会责任投资有种种限制，但一些研究人员也发现了其在在表现上的积极的一面：Henke（2016）审视了债券公募基金，发现遵循社会责任策略的基金每年跑赢传统基金大约0.5%；Nofsinger and Varma（2014）发现ESG在市场危机期间可以跑赢可比的传统基金。

目标日基金在资管行业的占比日益提升。但是目标日基金是否为创造价值尚无定论。Bodie and Treussard（2007）以及Gomes，Kotlikoff and Viceira（2008）显示目标日期基金可为创造价值客户；然而，Eltion， Gruber，de Souza，and Blake (2015)阐释到，目标日基金经理既没有创造超额收益也没有选股能力；Elton，Gruber，and de Souza（2018）发现目标日基金在选择管理人的问题上不如普通大众。

在基金切换资产组合时，对目标日基金的表现的评估尤为困难。管理人设计“滑翔通道”来随着日期临近来改变资产组合，但何为最合适“滑翔通道”并不明确。Balduzzi and Reuter（2019）发现，拥有类似的目标日的这类基金具有着迥异的风险记录和历史收益；Spitzer and Singh（2008）发现目标日基金在2007年左右的资产组合向固定收益倾斜，目标日基金开始跑输股债等权组合；Tang and Lin（2015）的研究表明，这个趋势源于一些管理人对风险的降低；而Trammel（2009）发现，这个趋势来源于基金去针对特定工种雇员的需要。

基于资产配置随时变的特性，目标日基金难以找到合适的基准。Surz and Israelsen（2008）发现单个基准无法持续适用；但Branch and Qiu（2011）发现滑翔通道和随时变的配置并不一定比固定配置策略好。

REIT基金首要投资于场内REITs的权益发行。虽然REIT基金仅代表公募基金的一小部分，但是基于其高分红、和市场低相关性的特征，许多投资者将其纳入其投资组合。管理人可通过选择跑赢市场的REITs或着对于不同种类的RIETs进行择时来为客户创造价值。然而，对于其表现的研究尚无共识——有的认为其无超额收益，有的认为其表现超群。

主动管理的研究通常排除掉行业基金，这使我们对其了解甚少。行业基金往往使得管理人为客户创造价值的灵活度不大，低分散性特征使得行业基金必然导致高异质风险。Dellva， Demaskey，and Smith（2001）发现了行业基金的选股能力的证据；但是Kaushik, Pnennathur and Barnhart（2010）持不同观点：在评估这些基金的一个重要考虑是，评估结果对基准选择十分敏感；但是Khorana and Nelling(1997)发现市场表现依然解释了行业基金的绝大部分回报。

相比于国内基金，国际公募基金面临的机会更多。不同国家的证券市场差异显著。Foerster and Karolyi（1999）发现由于信息透明度、流动性、监管和投资者保护的水平的不同，国内股票回报的相关性高于国际股票的回报。而Fama and French（2012，2017）发现，在大多数市场，股票回报呈现共同特征。Eun，Huang，and Lai（2008）发现市场间的相关性越来越高，美国的国际公募基金在在充满复杂性的国际市场驰骋中为客户创造价值。

Karolyi(2016)发现，只有16%的顶尖学术期刊的文章研究了非美市场，所以美国的国际基金少有学者问津也不足为奇。Kao, Chen，and Chan（1998）发现在80-90年代间，国际基金有超额收益。Turtle and Zhang（2012）展示了在全球牛市期间新兴市场基金有超额收益。Hiraki et al.（2015）发现，聚焦于特定国家和行业的基金跑赢更分散的基金。Fan and Addams（2012）以及Breloer，Scholz，and Wilkens（2014）发现国际基金领跑者的业绩并不能持续。但是Droms and Walker（2001）却发现了业绩持续性的证据。Eun et al.（2008）强调了小市值国际基金的分散化价值。而Didier，Rigobon，and Schmukler（2013）发现国际基金的分散化水平还有提升空间。

对于主动管理的国际基金价值创造的评估，许多独特的因素使其变得困难。用日度历史净值作为评估参考便会造成扭曲（因为国际基金投资标的分散于不同时区，日度标准难以统一）。Comer and Rodriguez（2012）强调了国际基金的基准选择的问题——尤其是通常采用的MSCI 世界指数。Dyck et al.（2013）发现主动管理人的价值取决于投资地的市场有效性。Cremers et al. (2016) 强调了管理人间的竞争水平——包括来自指数基金和ETF的竞争——在国际基金市场显著分化。最后国际投资的复杂性也为管理人带来额外的约束：Dubofsky（2010）发现国际基金的组合向流动性倾斜。

在探究主动管理人价值，研究人员面临了何种挑战？我们沿着这个主线来进行总结。基于种种挑战，有一些公开问题有待回答，这对于未来的研究也是机遇。在这个章节，沿着未来的探索方向，我们讨论了6个话题——或反驳或承认主动管理的传统观点。

衡量主动管理的价值的方法，必须考虑基金的风险和因子暴露，主动管理者的价值创造体现在，相比具备类似风险特征的被动基准收益更高。基于正确的基准选择的主管管理的研究结论才有意义——过去的文献使用了因子模型，市场指数模型，基于持仓的模型；但是这些模型均存在限制，我们下面一一展开。

对于公募基金研究，Carhart（1997）的多因子模型常常被使用。该模型试图将收益归结为市场、规模、价值和动量因子，而不是管理人的技能。然而，对于这个模型的重要的批判相继出现。Huij and Verbeek(2009)；Moreno and Rodriguez（2009）；以及Kadan and Liu（2014）强调了这种因子表达的短板。Cremer et al. (2013) 展示了，使用Carhart模型的方法来构建市值和价值因子，意味着公募基金需要和小市值价值股主导的基准来比较，全然不顾这类股票只是投资域中的一小部分；这种存疑的比较会导致一个“有偏的阿尔法”。

此外，Carhart（1997）模型使用的因子的设定可能不合适。Harvey，Liu，and Zhu（2016）以及Hou, Xue，and Zhang（2017）发现了上百个可用的潜在定价因子，因子的选择对基金表现的评估结果有着显著的影响。Jordan and Riley（2015）发现包含进新的Fama and French（2015）因子之后，可以解释低波动的美国权益公募基金的显著超额业绩。虽然Carhart的模型存在短板，但是文献尚未落地一个被广泛接受的模型来取代之。

除了使用因子模型，研究人员还可以将主动基金的收益和被动的市场指数进行比较。如果主动基金的风险和因子暴露与指数一致，那么一旦基金收益超过指数收益便可断定管理人创造了价值，这类基准在实践中广泛存在。在美国，证监会（SEC）要求所有的公募基金在其合同里披露一支基准指数以便投资者来评估基金的表现；一些文献往往使用基准调整收益，这在行业研究报告里也常常出现。

比如来自于标普道琼斯指数的SPIVA U.S. 打分卡（Soe and Poirier 2016），晨光之星的主动/被动晴雨表（Bryan, Boccellari，Johnson, and Rawson 2015），以及先锋领航白皮书（Rowley，Walker，and Ning 2018）

遗憾的是，自我声称的基准通常不能代表基金的实际投资风格。Sensoy（2009）以及Cremers et al.（2018）发现25%-30%的基金宣布的基准和自身不符；Cremers et al.（2018）尤其发现基金披露的基准常常风险更低。这导致基金的表现平均来看被夸大了。并且，披露的基准基本不会发生变化，而基金管理人的风格却是随时变的。此外，Cremers and Petajisto(2009)提出“最小主动份额”基准，可以用来改善评估表现的方法。

最后一个调整风险的可行办法是，聚焦于基金持仓和头寸来评估基金表现。Daniel et al. （1997）and Wermers（2000）提供了基于首次持仓的基金表现的分析，发现公募基金倾向于选出跑赢市场的股票。Busee, Jiang，and Tang（2019）结合了基于持仓的方法和因子模型来“双重调整”基金的表现。

虽然基于持仓的方法去计算主动管理人决策的价值是可行的，但是基于持有证券的表现来评估基金却存在明显的缺陷。一个原因是：由于存在交易费用和管理费，持仓所隐含的表现可能无法精确的代表基金投资人的实际收益；另外一个原因是该方法无法捕捉报告期之间的交易活动。

前面的小节聚焦于从学术界的角度衡量基金表现，一个挑战是当投资者在做买入和卖出决策的时候，他们关注什么基金表现指标？我们怎么衡量？一长串的文献在这方面有所记载。最早见Chevalier and Ellison（1997）以及Sirri and Tufano（1998），揭示了投资者很看重历史业绩，尤其愿意投业绩冠军。驱动投资者决策的基金业绩指标的精确衡量虽然尚无定论，但是以上结论可见一斑。

Barber et al.（2016）以及Berk and van Binsbergen (2016)展示了Sharpe（1964）以及Linter(1965)的资本资产定价模型（CAPM）在解释公募基金投资者行为上最佳。具体的，在假设投资者仅调整市场风险暴露来评判基金的表现时，他们的买入和卖出行为是最好预测的。这与模型中通常使用的复杂的因子模型形成对比，比如使用了除市场风险外的多个因子的Carhart（1997）模型。

Berk and van Binsbergen(2016)声称，他们的结果显示，CAPM是现有的度量基金的风险程度和估计基金表现的最佳模型。但是Barber et al.（2016）持反对意见，认为考虑“所有的与收益相关的因子”很重要；再上升一个层次，Barber et al. (2016) 陈述到：“不相信基于CAPM模型的论文的结论。”

除因子模型外，Sensoy（2009）以及Cremers et al.（2018）发现，在做决策的时候，投资者也会考虑相对被动基准指数比的表现。证监会要求披露的基准也很重要，虽然该基准与基金投资风格不符，但是投资者也会基于该基准做出买入和卖出的决策。虽然这一观察也与公募基金投资者并非完全理性的发现保持一致（比如Cooper et al. 2005），但是其也可能是由于投资者面临的壁垒导致的：Sirri and Tufano（1998）以及Hortascu and Syverson（2004）把这些壁垒描述为“搜索成本”——寻找投资机会的耗费的时间和金钱——得出结论：这些成本会迫使投资者使用简单的准则，而非使用难以获取或解读的数据。

在管理公募基金组合的过程中的透明度不足，也是评估主动管理人价值的另外一个因素。大多数研究使用汤森路透关于基金持仓的数据，但是该数据库有三大限制：第一，其仅包含季度持仓；第二，其仅包含权益资产持仓；第三，正如Schwarz and Potter（2016）所展示的，缺失一些证监会要求披露的信息，一些未被要求披露数据却被包含进来。综上，在研究主动管理时，这些约束使得基于已有持仓的数据的结论受限。而最受限的层面是数据披露的频率——季度，Elton，Gruber，and Ozelge（2010）展示了季度持有时点信息缺失了很多的季度内的交易信息。

除了数据问题外，持仓数据本身可能也没法代表基金的组合。因为基金经理知道他们的头寸将公之于众，那么他们可以在临近财报期时对其组合进行“窗口粉饰”。Danthine and Moresi（1998）以及Wermers（2001）解释到，“窗口粉饰”是为了防止其别人“抢跑”，以及投资思路被人“搭便车”。Morey and O’Neal（2006）以及Agarwal，Gay，and Ling（2014）的研究显示，组合窗口粉饰是为了让其显得对投资者负责。

即使基金持有反映了其真实策略，Nicolosi（2009）以及Fulkerson（2013）发现，关于交易发生的时间，学术上对此常用的假设会使得基金的表现的度量出现偏差。

虽然公募基金是个投资门槛低、监管严的行业，但是学术研究人员想要获取有关基金经理和基金运作的信息并非易事。对于基金管理人，Massa，Reuter，and Zitzwits（2010）讨论了学术界广泛使用的数据源——CRSP数据库在基金管理人信息上数据有限。Patel and Sarkissian（2017）显示，Morningstar Direct数据更好，但是这两个数据库都难以跟踪在不同基金切换的管理人，也不提供管理人背景和非公募基金活动的可直接使用的信息（比如，Gottesman and Morey 2006；Evans and Fahlenbrach 2012；Kempf, Manconi, and Spalt 2017；and Chen，Chen，Johnson，and Sardarli 2017）。

诚然，管理人的机会发现和头寸管理的投资过程信息难以获取；但竟没有文献对这个过程进行总结。具体原因是：这个领域的研究需要一些关键输入——比如分析师雇佣人数，外部研究使用情况，和数据源——这些都无法获得。在学术界，因为数据限制，主动管理的价值创造过程几乎文人问津。所以，主动管理的很多问题还有待挖掘。

公募基金主动管理在本文笔墨较重，但多资产组合主动管理也不容忽视。除了在单一资产内业绩最大化，多资产组合管理人决定资产种类的资金分配。当前关于多资产组合主动管理的价值认识还局限在相对少量的对养老金的研究上。

Brinson，Hood，and Beebower（1986）发现养老金管理人的决策仅解释了基金总收益的一小部分；Dyck et al.（2013）展示了，养老金每年跑输美国市场0.35%但跑赢新兴市场1.80%；Andonov，Bauer and Cremers（2012）发现美国养老金通过资金分配、择时、择证券的方式，来使得业绩每年提升0.89%；这种改善很大程度上可以被动量——具有显著的规模经济递减效应——所解释。

虽然这些研究为主动管理人创造价值的论点提供了支持，但是养老金管理人面临着独特的约束。Novy-Marx and Rauh（2011）展示了，公众养老金更倾向于持有风险更高的资产，因为监管允许其持有负债贴现率更高的高风险资产（译者注：负债贴现率指的是投资者的要求回报率）。高贴现率使得公众养老金可以给官方汇报更高的融资比例；考虑到这个监管动机，Andonov，Bauer, and Cremers（2017）发现融资不足的养老金以降低经风险调整后收益为代价来增大风险。鉴于这些特殊的环境，基于养老金的研究成果难以适用到所有的做资产组合上。

评估主动管理价值的最后一个挑战是主动管理人受到的约束的影响。理论模型通常假设主动管理人是不受约束的，以至于有能力最优分配资产来实现最大化风险调整后收益。实践来看，基金的运营面临诸多的监管和制度约束，这些约束可能影响他们的决策以及为投资者创造价值的能力。Clarke，de Silva，and Thorley（2002）记录到，任何的约束都会“使管理人将有价值的信息纳入到组合头寸上的能力受限”；至于其他约束，Almazan, Brown, Carlson, and Chapman (2004) 发现，除了证监会规定以外，公募基金常常在“融资买入、融券卖空上、持有个股期权、股指期货交易、和受限证券”上受限；类似的，Evans, Ferreira, and Prado(2017) 展示了基金集团对于常常给旗下基金投资集设限。如果这些约束很重要，那么公募基金主动管理的研究的结论在其他主动管理工具上可能就不再适用。公募基金主动管理的价值可能随着约束的变化而变化。基于当前数据的主动管理的结论在未来或不再适用。

一些对于公募基金受限于约束的影响的研究也在进行，这些文献聚焦于：1）日度流动性要求；2）合规要求。公募基金通过在每日临近收市之际以净值买入和售出份额来为投资者提供流动性，这种流动性的提供迫使管理人必须使得组合可以应对投资者在任何一天要赎回时的现金要求，这种必要性已被证明会增加单支公募基金的成本，甚至影响公募基金整体和整个市场。管理人为了更好的处理投资者的买入和卖出，投资者要么保留更多的现金或投资流动性更好的资产，但是这种做法的后果是偏离最优资产配置。

除了提供流动性的要求，公募基金也面临其他可能影响主动管理价值的监管要求。比如此前讨论的持仓披露，以及基金的投资约束，比如杠杆限制。除了遵守这些约束的直接成本外，这些监管要求也会带来实质性间接成本：比如Parida and Teo（2018）展示了，公募基金的持仓披露愈频繁，其业绩愈会受拖累，因为其他投资人可以搭便车“抢跑”；Dyakov and Verbeek(2013)展示了，公募基金的资金流向和持有信息公开化会使其暴露在“狙击交易”的枪口之下。

估计这些约束对主动管理的价值的总影响是个艰巨的任务，一个方法是，相比于公募基金，估计受限程度更低的主动管理组合的价值。比如对冲基金和专户，虽然他们并非完全不受监管，但这些投资工具相比公募基金面临的约束更少。Kosowski, Naik, and Teo (2007)以及lbbotson, Chen, and Zhu (2011)发现，对冲基金每年有3%-5%的超额收益，这显示了主动管理在这个行业的显著价值。然而Aragon（2007）；Fung, Hsieh, Naik, and Ramadorai (2008); Dichev and Yu (2011); 以及Joenv??r?, Kauppila, Kosowski, and Tolonen (即将出版)，发现对冲基金的超额收益近乎为0，专户相比对冲基金受到的关注度更低。但是Busse, Goyal, and Wahal (2010) 以及Eltion, Gruber, and Blake (2014)发现，平均来看，专户跑赢公募基金。

遗憾的是，做这种比较来估计约束成本，或来决定公募基金研究结论的适用性是危险的。把公募基金和对冲基金对比尤为困难：Agarwal，Boyson and Naik（2009）；Clifford, Jordan, and Riley (2013); Huang and Wang (2013); Klement (2015); 以及Hartley (2017) 均发现试图复制对冲基金风格投资策略（绝对收益，另类投资，市场中性）的公募基金难以复制其实际收益——表明：公募基金经理，因为种种限制，相比于私募基金，在通过主动管理为客户创造价值的能力受限。

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