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Generalized Tests of Investment Fund Performance

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  • Márcio Laurini

    (IBMEC Business School)

Abstract

The paper discusses the use of statistical methods in the comparison of investment fund performance indicators. The analysis is based on the robust statistics proposed by Ledoit and Wolf (2008), for the pairwise comparison of funds and two generalizations for sets of multiple investment funds. The multiple investment fund tests use the Wald and Distance Metric statistics, based on estimation by Generalized Method of Moments using HAC matrices. In order to correct power limitations in the GMM estimation in the case of a large number of moment conditions, the test distributions are obtained through block-bootstrap procedures. We applied the proposed procedures to daily return data for the largest 97 actively managed equity funds in the Brazilian market, covering the period from July 2006 to July 2008. The results indicate that there are no significant differences in the performances of the 97 funds in the sample, both in pairwise and joint comparisons, thus providing what is believed to be the first Brazilian market evidence for the so-called herding hypothesis.

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File URL: http://professores.ibmecrj.br/erg/dp/papers/dp201203.pdf
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Bibliographic Info

Paper provided by Economics Research Group, IBMEC Business School - Rio de Janeiro in its series IBMEC RJ Economics Discussion Papers with number 2012-03.

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Date of creation: 22 Mar 2012
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Handle: RePEc:ibr:dpaper:2012-03

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Related research

Keywords: Sharpe Ratio; GMM; Investment Analysis;

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References

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  1. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May.
  2. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
  3. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
  4. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 417-442, November.
  5. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W., 1992. "The impact of institutional trading on stock prices," Journal of Financial Economics, Elsevier, vol. 32(1), pages 23-43, August.
  6. Stanislav Anatolyev, 2009. "Inference in Regression Models with Many Regressors," Working Papers w0125, Center for Economic and Financial Research (CEFIR).
  7. Gregory, Allan W & Veall, Michael R, 1985. "Formulating Wald Tests of Nonlinear Restrictions," Econometrica, Econometric Society, vol. 53(6), pages 1465-68, November.
  8. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Rejoinder on: Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 461-471, November.
  9. Jobson, J D & Korkie, Bob M, 1981. "Performance Hypothesis Testing with the Sharpe and Treynor Measures," Journal of Finance, American Finance Association, vol. 36(4), pages 889-908, September.
  10. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  11. Inoue, Atsushi & Shintani, Mototsugu, 2006. "Bootstrapping GMM estimators for time series," Journal of Econometrics, Elsevier, vol. 133(2), pages 531-555, August.
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