|
|
|
| Do Mutual Funds Exploit Stock Market Anomalies in China？ |
| LI Bin, LEI Yinru
|
| Economics and Management School/Financial Research Center, Wuhan University |
|
|
|
Abstract Mutual funds are one of the most attractive investment options for institutional investors in China; thus, understanding the behaviors of mutual funds is crucial for making institutional investments and selecting superior mutual funds. Stock is an important underlying asset for mutual funds, especially equity mutual funds. Furthermore, stock returns are driven by a series of firm characteristics or anomaly factors, and a burgeoning number of factors worldwide provide new investment directions for mutual funds. Institutional investors, including mutual funds, have the motivation and capability to exploit market anomalies. The following questions thereby arise: Do mutual funds exploit market anomalies in Chinese stock markets？ If so, can we select mutual funds based on such exploitation behaviors？
However, the literature mainly focuses on the exploitation of a single market anomaly or single class of market anomalies, and no systematic examination of the hundreds of existing anomalies exists. Moreover, some studies provide contradictory answers to the question of whether institutional investors exploit market anomalies. In addition, most studies are based on the US stock markets, and their findings may not apply to Chinese stock markets. Unfortunately, no such research exists on Chinese capital markets. Thus, this paper seeks to answer the following four research questions: (1) Do Chinese mutual funds exploit stock market anomalies？ (2) If so, can we propose new indicators for mutual fund selection based on their holding stocks and corresponding anomaly factors？ (3) Is the exploitation of stock market anomalies a reflection of fund managers' investment management capabilities？ (4) What are the economic consequences and market impacts of exploiting these anomalies？
To answer these questions, we construct an anomaly score (or A-Score) based on 87 anomaly variables from the Chinese A-share market. To avoid look-ahead bias, we directly adopt the factors from the study by Li et al. (2019). Based on fund holding data and A-Scores of stocks, we further construct the Anomaly Investing Measure (AIM) of mutual funds, which is calculated as the weighted average of holding stocks' A-Score deciles over the whole market universe. A higher AIM indicates that the funds tend to hold stocks with long positions, aggregating the 87 anomaly variables.
Based on actively managed equity mutual funds from 2005 to 2019, we first examine whether mutual funds exploit market anomalies in Chinese stock markets. Our empirical results confirm that funds actively allocate more weight to stocks with high A-Scores as compared with the CSI 300 index and historical holdings and that trading behaviors persist for a long time. Thus, Chinese mutual funds do exploit market anomalies. We next examine the relationship between AIM and the cross-section of mutual fund returns. Results regarding both portfolio sorts and regression show that the AIM significantly predicts future returns; moreover, the AIM can be used to select superior funds. Furthermore, we decompose the fund managers' investment abilities and explore the AIM return sources. The results show that funds with a higher AIM exhibit excellent stock selection, style selection, and risk control ability. Finally, we explore the economic impacts of exploiting anomalies at the fund and market levels. Exploiting anomalies not only ensures long-term fund flows but also alleviates market mispricing.
Our study makes the following contributions to the literature. First, to the best of our knowledge, our paper is the first to systematically examine whether Chinese mutual funds exploit Chinese A-share market anomalies. Contrary to the literature that focuses on a single factor or single class of factors, our paper provides a new perspective on the investment behaviors of Chinese institutional investors by examining 87 market anomalies. Second, based on the stock market anomaly factors, we propose the AIM, which can be used to select superior mutual funds. Our findings enrich the literature on the return prediction of Chinese mutual funds and provide theoretical support for the development of fund-of-funds in China. Third, this paper explores fund managers' investments in stock anomalies from the logic of fund selection of underlying assets and confirms that anomaly exploitation reflects fund managers' superior stock selection, style selection, and risk control abilities. Thus, our study provides a reliable explanation concerning fund managers' investment skills. Fourth, we find that mutual funds' exploitation of market anomalies could reduce arbitrage returns and mitigate mispricing in the Chinese A-share market. Thus, our study enriches the literature on mispricing in the Chinese market and helps understand institutional investors' behaviors from a new perspective.
|
|
Received: 14 February 2022
Published: 12 October 2022
|
|
|
|
|
| [1] |
陈胜蓝和李璟,2021,《基金网络能够提高投资绩效吗？》,《金融研究》第6期,第170～188页。
|
| [2] |
郭春松、蔡庆丰和汤旸玚,2015,《基金家族的业绩关联与溢出效应——基于共同技能效应与共同噪声效应的实证研究》,《金融研究》第5期,第162～177页。
|
| [3] |
李斌、邵新月和李玥阳,2019,《机器学习驱动的基本面量化投资研究》,《中国工业经济》第8期,第61～79页。
|
| [4] |
刘莎莎、刘玉珍和唐涯,2013,《信息优势、风险调整与基金业绩》,《管理世界》第8期,第67～76页。
|
| [5] |
罗荣华、兰伟和杨云红,2011,《基金的主动性管理提升了业绩吗？》,《金融研究》第10期,第127～139页。
|
| [6] |
罗荣华和田正磊,2020,《基金网络、竞争阻隔与股票信息环境》,《中国工业经济》第3期,第137～154页。
|
| [7] |
罗荣华、田正磊和方红艳,2020,《“和而不群”还是“卓尔不群”？——基于基金网络信息使用的视角》,《金融研究》第8期,第188～206页。
|
| [8] |
申宇、赵静梅和何欣,2013,《基金未公开的信息:隐性交易与投资业绩》,《管理世界》第8期,第53～66页。
|
| [9] |
田澍、林树和俞乔,2012,《新兴市场环境下机构投资者投资行为——基于中国大陆资本市场的研究》,《金融研究》第8期,第139～151页。
|
| [10] |
肖峻和石劲,2011,《基金业绩与资金流量:我国基金市场存在“赎回异象”吗？》,《经济研究》第1期,第112～125页。
|
| [11] |
Akbas, F., Armstrong, W. J., Sorescu, S. and Subrahmanyam, A. 2015. “Smart Money, Dumb Money, and Capital Market Anomalies”, Journal of Financial Economics, 118(2):355～382.
|
| [12] |
Ali, A., Chen, X., Yao, T. and Yu, T. 2008. “Do Mutual Funds Profit from the Accruals Anomaly？”, Journal of Accounting Research, 46(1):1～26.
|
| [13] |
Asness, C. S., Frazzini, A. and Pedersen, L. H. 2019. “Quality minus junk”, Review of Accounting Studies, 24(1):34～112.
|
| [14] |
Avramov, D., Cheng, S. and Hameed, A. 2019. “Mutual Funds and Mispriced Stocks”, Management Science, 66(6): 2372～2395.
|
| [15] |
Calluzzo, P., Moneta, F. and Topaloglu, S. 2019. “When Anomalies Are Publicized Broadly, Do Institutions Trade Accordingly？”, Management Science, 65(10):4555～4574.
|
| [16] |
Chen, J., Hong, H. and Stein, J. C. 2002. “Breadth of Ownership and Stock Returns”, Journal of Financial Economics, 66(2):171～205.
|
| [17] |
Cohen, L., Frazzini, A. and Malloy, C. 2008. “The Small World of Investing: Board Connections and Mutual Fund Returns.”, Journal of Political Economy, 116(5):951～979.
|
| [18] |
Cremers, K. J. M. and Petajisto, A. 2009. “How Active Is Your Fund Manager？ A New Measure That Predicts Performance”, Review of Financial Studies, 22(9):3329～3365.
|
| [19] |
Daniel, K., Grinblatt, M., Titman, S. and Wermers, R. 1997. “Measuring Mutual Fund Performance with Characteristic-Based Benchmarks”, Journal of Finance, 52(3):1035～1058.
|
| [20] |
Doshi, H., Elkamhi, R. and Simutin, M. 2015. “Managerial Activeness and Mutual Fund Performance”, Review of Asset Pricing Studies, 5(2):156～184.
|
| [21] |
Edelen, R. M., Ince, O. S. and Kadlec, G. B. 2016. “Institutional Investors and Stock Return Anomalies”, Journal of Financial Economics, 119(3):472～488.
|
| [22] |
Elton, E. J., Gruber, M. J. and de Souza, A. 2018. “Fund of Funds Selection of Mutual Funds”, Critical Finance Review, 7(2):241～272.
|
| [23] |
Frazzini, A. and Lamont, O. A., 2008, “Dumb Money: Mutual Fund Flows and the Cross-Section of Stock Returns”, Journal of Financial Economics, 88:299～322.
|
| [24] |
Gompers, P. A. and Metrick, A. 2001. “Institutional Investors and Equity Prices”, Quarterly Journal of Economics, 116(1):229～259.
|
| [25] |
Green, J., Hand, J. R. M. and Zhang, X. F. 2017. “The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns”, Review of Financial Studies, 30(12):4389～4436.
|
| [26] |
Grinblatt, M., Jostova, G., Petrasek, L. and Philipov, A. 2020. “Style and Skill: Hedge Funds, Mutual Funds, and Momentum”, Management Science, 66(12): 5505～5531.
|
| [27] |
Hendricks, D., Patel, J. and Zeckhauzer, R. 1993. “Hot Hands in Mutual Funds: Short-Run Persistence of Relative Performance, 1974-1988”, Journal of Finance, 48(1):93～130.
|
| [28] |
Horowitz, J., Klemelä, J. and Mammen, E. 2006. “Optimal Estimation in Additive Regression Models”, Bernoulli, 12(2):271～298.
|
| [29] |
Huang, J., Sialm, C. and Zhang, H. 2011. “Risk Shifting and Mutual Fund Performance”, Review of Financial Studies, 24(8):2575～2616.
|
| [30] |
Jiang, H. 2010. “Institutional Investors, Intangible Information, and the Book-to-Market Effect”, Journal of Financial Economics, 96(1):98～126.
|
| [31] |
Kacperczyk, M., Sialm, C. and Zheng, L. 2008. “Unobserved Actions of Mutual Funds”, Review of Financial Studies, 21(6):2379～2416.
|
| [32] |
Kenchington, D., Wan, C. and Yüksel, H. Z. 2019. “Gross Profitability and Mutual Fund Performance”, Journal of Banking & Finance, 104:31～49.
|
| [33] |
Lee, J., Jeon, H., Kang, J. and Lee, C. 2020. “Do Actively Managed Mutual Funds Exploit Stock Market Mispricing？”, North American Journal of Economics and Finance, 53:1～18.
|
| [34] |
Lewellen, J. 2011. “Institutional Investors and the Limits of Arbitrage”, Journal of Financial Economics, 102(1):62～80.
|
| [35] |
Liu, J., Stambaugh, R. F. and Yuan, Y. 2019. “Size and Value in China”, Journal of Financial Economics, 134(1):48～69.
|
| [36] |
Lou, D. 2012. “A Flow-Based Explanation for Return Predictability”, Review of Financial Studies, 25(12):3457～3489.
|
| [37] |
McLean, R. D. and Pontiff, J. 2016. “Does Academic Research Destroy Stock Return Predictability？”, Journal of Finance, 71(1):5～32.
|
| [38] |
Pollet, J. M. and Wilson, M. 2008. “How Does Size Affect Mutual Fund Behavior？”, Journal of Finance, 63(6):2941～2969.
|
| [39] |
Schnaubelt, M., Fischer, T. G. and Krauss, C. 2020. “Separating the Signal from the Noise-Financial Machine Learning for Twitter”, Journal of Economic Dynamics and Control, 114:1～22.
|
|
|
|
