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Mutual Funds’ Conditional Performance Free of Data Snooping Bias

Published online by Cambridge University Press:  15 February 2024

Po-Hsuan Hsu Open the ORCID record for Po-Hsuan Hsu [Opens in a new window] ,
Ioannis Kyriakou Open the ORCID record for Ioannis Kyriakou [Opens in a new window] ,
Tren Ma  and
Georgios Sermpinis
Po-Hsuan Hsu*
Affiliation:
National Tsing Hua University, College of Technology Management
Ioannis Kyriakou
Affiliation:
City, University of London, Bayes Business School [email protected]
Tren Ma
Affiliation:
University of Nottingham, Nottingham University Business School [email protected]
Georgios Sermpinis
Affiliation:
University of Glasgow, Adam Smith Business School [email protected]
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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We introduce a test to assess mutual funds’ “conditional” performance that is based on updated information and corrects data snooping bias. Our method, named the functional false discovery rate “plus” ($ {\mathrm{fFDR}}^{+} $), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the $ {\mathrm{fFDR}}^{+} $ controls well the ratio of false discoveries and gains considerable power over prior methods that do not account for extra information. Portfolios of funds selected by the $ {\mathrm{fFDR}}^{+} $ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 28
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We would like to thank Hendrik Bessembinder (the editor), Keith Cuthbertson, Jens Perch Nielsen, Olivier Scaillet (the reviewer), Zheng Sun, Qifei Zhu, and the audience of the American Finance Association (AFA) student poster session and the China International Conference in Finance (CICF) for their comments. We appreciate the replies and discussions of Gery Geenens, Kuangyu Wen, and Ximing Wu on their works on density estimations, as well as Laurent Barras and Russ Wermers on the mutual fund data. Hsu acknowledges the Ministry of Education and the Ministry of Science and Technology in Taiwan for financial support (Yushan Fellow Program and MOST109-2628-H-007-001-MY4). All errors are our own.

References

Amihud, Y., and Goyenko, R.. “Mutual Fund’s $ {R}^2 $ as Predictor of Performance.” Review of Financial Studies, 26 (2013), 667–694.CrossRefGoogle Scholar
Andrikogiannopoulou, A., and Papakonstantinou, F.. “Estimating mutual fund skill: A new approach.” Swiss Finance Institute Research Paper No. 14-42 (2016).Google Scholar
Andrikogiannopoulou, A., and Papakonstantinou, F.. “Reassessing False Discoveries in Mutual Fund Performance: Skill, Luck, or Lack of Power?” Journal of Finance, 74 (2019), 2667–2688.CrossRefGoogle Scholar
Bajgrowicz, P., and Scaillet, O.. “Technical Trading Revisited: False Discoveries, Persistence Tests, and Transaction Costs.” Journal of Financial Economics, 106 (2012), 473–491.CrossRefGoogle Scholar
Barras, L.; Gagliardini, P.; and Scaillet, O.. “Skill, Scale, and Value Creation in the Mutual Fund Industry.” Journal of Finance, 77 (2022), 601–638.CrossRefGoogle Scholar
Barras, L.; Scaillet, O.; and Wermers, R.. “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas.” Journal of Finance, 65 (2010), 179–216.CrossRefGoogle Scholar
Barras, L.; Scaillet, O.; and Wermers, R.. “Reassessing False Discoveries in Mutual Fund Performance: Skill, Luck, or Lack of Power? A Reply.” Swiss Finance Institute Research Paper No. 19-61 (2019).Google Scholar
Benjamini, Y., and Hochberg, Y.. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological), 57 (1995), 289–300.CrossRefGoogle Scholar
Berk, J. B., and van Binsbergen, J. H.. “Measuring Skill in the Mutual Fund Industry.” Journal of Financial Economics, 118 (2015), 1–20.CrossRefGoogle Scholar
Bessembinder, H.; Cooper, M. J.; and Zhang, F.. “Mutual Fund Performance at Long Horizons.” Journal of Financial Economics, 147 (2023), 132–158.CrossRefGoogle Scholar
Carhart, M. M. “On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 57–82.CrossRefGoogle Scholar
Chen, X.; Robinson, D. G.; and Storey, J. D.. “The Functional False Discovery Rate with Applications to Genomics.” Biostatistics, 22 (2021), 68–81.CrossRefGoogle ScholarPubMed
Chordia, T.; Goyal, A.; and Saretto, A.. “Anomalies and False Rejections.” Review of Financial Studies, 33 (2020), 2134–2179.CrossRefGoogle Scholar
Clifford, C. P.; Fulkerson, J. A.; Jame, R.; and Jordan, B. D.. “Salience and Mutual Fund Investor Demand for Idiosyncratic Volatility.” Management Science, 67 (2021), 5234–5254.CrossRefGoogle Scholar
Doshi, H.; Elkamhi, R.; and Simutin, M.. “Managerial Activeness and Mutual Fund Performance.” Review of Asset Pricing Studies, 5 (2015), 156–184.CrossRefGoogle Scholar
Fama, E. F., and French, K. R.. “Luck Versus Skill in the Cross-Section of Mutual Fund Returns.” Journal of Finance, 65 (2010), 1915–1947.CrossRefGoogle Scholar
Geenens, G. “Probit Transformation for Nonparametric Kernel Estimation on the Unit Interval.” Journal of the American Statistical Association, 109 (2014), 346–358.CrossRefGoogle Scholar
Grønborg, N. S.; Lunde, A.; Timmermann, A.; and Wermers, R.. “Picking Funds with Confidence.” Journal of Financial Economics, 139 (2021), 1–28.CrossRefGoogle Scholar
Hansen, P. R. “A Test for Superior Predictive Ability.” Journal of Business & Economic Statistics, 23 (2005), 365–380.CrossRefGoogle Scholar
Harvey, C. R., and Liu, Y.. “Decreasing Returns to Scale, Fund Flows, and Performance.” Working Paper, Duke University, NBER, and Purdue University (2017).CrossRefGoogle Scholar
Harvey, C. R., and Liu, Y.. “Detecting Repeatable Performance.” Review of Financial Studies, 31 (2018), 2499–2552.CrossRefGoogle Scholar
Harvey, C. R., and Liu, Y.. “False (and Missed) Discoveries in Financial Economics.” Journal of Finance, 75 (2020), 2503–2553.CrossRefGoogle Scholar
Hsu, P.-H.; Hsu, Y.-C.; and Kuan, C.-M.. “Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test Without Data Snooping Bias.” Journal of Empirical Finance, 17 (2010), 471–484.CrossRefGoogle Scholar
Hsu, P.-H.; Taylor, M. P.; and Wang, Z.. “Technical Trading: Is It Still Beating the Foreign Exchange Market?” Journal of International Economics, 102 (2016), 188–208.CrossRefGoogle Scholar
Ignatiadis, N., and Huber, W.. “Covariate Powered Cross-Weighted Multiple Testing.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83 (2021), 720–751.CrossRefGoogle Scholar
Jensen, M. C. “The Performance of Mutual Funds in the Period 1945–1964.” Journal of Finance, 23 (1968), 389–416.Google Scholar
Jones, C. S., and Mo, H.. “Out-of-Sample Performance of Mutual Fund Predictors.” Review of Financial Studies, 34 (2021), 149–193.CrossRefGoogle Scholar
Jones, C. S., and Shanken, J.. “Mutual Fund Performance with Learning Across Funds.” Journal of Financial Economics, 78 (2005), 507–552.CrossRefGoogle Scholar
Kacperczyk, M.; Sialm, C.; and Zheng, L.. “Unobserved Actions of Mutual Funds.” Review of Financial Studies, 21 (2008), 2379–2416.CrossRefGoogle Scholar
Kosowski, R.; Timmermann, A.; Wermers, R.; and White, H.. “Can Mutual Fund “Stars” Really Pick Stocks? New Evidence from a Bootstrap Analysis.” Journal of Finance, 61 (2006), 2551–2595.CrossRefGoogle Scholar
Newey, W. K., and West, K. D.. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55 (1987), 703–708.CrossRefGoogle Scholar
Newton, M. A.; Noueiry, A.; Sarkar, D.; and Ahlquist, P.. “Detecting Differential Gene Expression with a Semiparametric Hierarchical Mixture Method.” Biostatistics, 5 (2004), 155–176.CrossRefGoogle ScholarPubMed
Romano, J. P., and Wolf, M.. “Stepwise Multiple Testing as Formalized Data Snooping.” Econometrica, 73 (2005), 1237–1282.CrossRefGoogle Scholar
Storey, J. D. “A Direct Approach to False Discovery Rates.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 64 (2002), 479–498.CrossRefGoogle Scholar
Storey, J. D. “The Positive False Discovery Rate: A Bayesian Interpretation and the $ q $ -Value.” Annals of Statistics, 31 (2003), 2013–2035.CrossRefGoogle Scholar
Storey, J. D.; Akey, J. M.; and Kruglyak, L.. “Multiple Locus Linkage Analysis of Genomewide Expression in Yeast.” PLOS Biology, 3 (2005), 1380–1390.CrossRefGoogle ScholarPubMed
Storey, J. D.; Taylor, J. E.; and Siegmund, D.. “Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 66 (2004), 187–205.CrossRefGoogle Scholar
Storey, J. D., and Tibshirani, R.. “Statistical Significance for Genomewide Studies.” Proceedings of the National Academy of Sciences, 100 (2003), 9440–9445.CrossRefGoogle ScholarPubMed
Sullivan, R.; Timmermann, A.; and White, H.. “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” Journal of Finance, 54 (1999), 1647–1691.CrossRefGoogle Scholar
Sullivan, R.; Timmermann, A.; and White, H.. “Dangers of Data Mining: The Case of Calendar Effects in Stock Returns.” Journal of Econometrics, 105 (2001), 249–286.CrossRefGoogle Scholar
Tibshirani, R. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological), 58 (1996), 267–288.CrossRefGoogle Scholar
Wen, K., and Wu, X.. “An Improved Transformation-Based Kernel Estimator of Densities on the Unit Interval.” Journal of the American Statistical Association, 110 (2015), 773–783.CrossRefGoogle Scholar
Wermers, R. “Mutual Fund Herding and the Impact on Stock Prices.” Journal of Finance, 54 (1999), 581–622.CrossRefGoogle Scholar
White, H. “A Reality Check for Data Snooping.” Econometrica, 68 (2000), 1097–1126.CrossRefGoogle Scholar
Zheng, L. “Is Money Smart? A Study of Mutual Fund Investors’ Fund Selection Ability.” Journal of Finance, 54 (1999), 901–933.CrossRefGoogle Scholar
Zou, H., and Hastie, T.. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67 (2005), 301–320.CrossRefGoogle Scholar
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