Mutual fund performance: false discoveries, bias, and power

Ammann M., Verhofen M.: The impact of prior performance on the risk-taking of mutual fund managers. Ann Financ 5, 69–90 (2009)

Avellaneda, M., Lee, J.: Statistical Arbitrage in the U.S. equities market. SSRN. http://ssrn.com/abstract=1153505 (2008). Accessed 11 July 2008

Barras L., Scaillet O., Wermers R.: False discoveries in mutual fund performance: measuring luck in estimated alphas. J Financ 65(1), 179–216 (2010)

Benjamini Y., Hochberg Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc 57(1), 289–300 (1995)

Benjamini Y., Hochberg Y.: On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25(1), 60–83 (2000)

Benjamini Y., Yekutieli D.: The control of the false discovery rate in multiple testing under dependency. Ann Stat 29(4), 1165–1188 (2001)

Benjamini Y., Krieger A., Yekutieli D.: Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93(3), 491–507 (2006)

Carhart M.: On persistence of mutual fund performance. J Financ 52(1), 57–82 (1997)

Chen H., Jegadeesh N., Wermers R.: The value of active mutual fund management: an examination of the stockholdings and trades of fund managers. J Financ Quant Anal 35, 343–368 (2000)

Cornell B., Cvitanic J., Goukasian L.: Beliefs regarding fundamental value and optimal investing. Ann Financ 6, 83–105 (2010)

Cuthbertson K., Nitzsche D., O’Sullivan N.: UK mutual fund performance: skill or luck?. J Empir Financ 15, 613–634 (2008a)

Cuthbertson, K., Nitzsche, D., O’Sullivan, N.: False discoveries: winners and losers in mutual fund performance. SSRN. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1093624 (2008b)

Daniel K., Grinblatt M., Titman S., Wermers R.: Measuring mutual fund performance with characteristic-based benchmarks. J Financ 52(3), 1035–1058 (1997)

Efron B.: Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96(456), 1151–1160 (2001)

Efron B., Tibshirani R.: Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol 23, 70–86 (2002)

Efron B.: Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J Am Stat Assoc 99(465), 96–104 (2004a)

Efron, B.: Selection and estimation for large-scale simultaneous inference. http://www-stat.stanford.edu/~ckirby/brad/papers/ (2004b). Accessed 25 Jan 2009

Efron, B.: Local false discovery rates. http://www-stat.stanford.edu/~ckirby/brad/papers/ (2005) Accessed 25 Jan 2009

Efron B.: Microarrays, empirical Bayes, and the two-groups model. Stat Sci 23(1), 1–22 (2008a)

Efron B.: Testing the significance of sets of genes. Ann Appl Stat 1(1), 107–129 (2007a)

Efron B.: Size, power, and false discovery rates. Ann Stat 35(4), 1351–1377 (2007b)

Efron B.: Correlation and large-scale simultaneous significance testing. J Am Stat Assoc 102(477), 93–103 (2007)

Efron B.: Simultaneous inference: when should hypothesis testing problems be combined?. Ann Appl Stat 2(1), 197–223 (2008)

Fan J., Fan Y., Lv J.: High dimensional covariance matrix estimation using a factor model. J Econom 147, 186–197 (2008)

Jensen M.: The performance of mutual funds in the period 1945–1964. J Financ 23(2), 389–416 (1968)

Jones C., Shanken J.: Mutual fund performance with learning across funds. J Financ Econ 78, 507–552 (2005)

Kosowski R., Timmermann A., Wermers R., White H.: Can mutual fund “Stars” really pick stocks? New evidence from a bootstrap analysis. J Financ 61(6), 2551–2596 (2006)

Kothari S., Warner J.: Evaluating mutual fund performance. J Financ 56(5), 1985–2010 (2001)

Mamaysky H., Spiegel M., Zhang H.: Improved forecasting of mutual fund alphas and betas. Rev Financ 11, 359–400 (2007)

Otamendi J., Doncel L., Grau P., Sainz J.: An evaluation on the true statistical relevance of Jensen’s alpha trough simulation: An application for Germany. Econ Bull 7(10), 1–9 (2008)

Romano J., Wolf M.: Stepwise multiple testing as formalized data snooping. Econometrica 73, 1237–1282 (2005)

Romano, J., Shaikh, A., Wolf, M.: Control of the false discovery rate under dependence using the bootstrap and subsampling. University of Zurich, Working paper no. 337. http://ssrn.com/abstract=1025410 (2007)

Romano J., Shaikh A., Wolf M.: Formalized data snooping based on generalized error rates. Economet Theor 24(2), 404–447 (2008)

Storey J.: A direct approach to false discovery rates. J Roy Stat Soc B 64, 479–498 (2002)

Storey D.: The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat. 31(6), 2013–2035 (2003)

Storey, D., Tibshirani, R.: Estimating false discovery rates under dependence, with applications to DNA microarrays. http://www.genomine.org/publications.html (2001). Accessed 25 Nov 2009

Storey D., Tibshirani R.: Statistical Significance for genomewide studies. Proc Nat Acad Sci USA 100, 9440 (2003)

Storey J., Taylor J., Siegmund D.: Strong control, conservative point estimation and simultaneous conser-vative consistency of false discovery rates: a unified approach. J Roy Stat Soc 66, 187 (2004)

Turnbull, B.: Optimal estimation of false discovery rates. http://www.stanford.edu/~bkatzen/ (2007). Accessed 25 Jan 2009

van der Laan, M., Hubbard, A.: Quantile function based null distribution in resampling based multiple testing. Stat Appl Genet Mol Biol. 5, article 14 (2005)

White H.: A reality check for data snooping. Econometrica 68, 1097–1126 (2000)

Yekutieli D., Benjamini Y.: Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Infer 82, 171 (1999)