Fund survival; Performance measurement; Persistence analysis; Mutual funds
Abstract :
[en] Using an international database featuring 1624 mutual funds over 15 years, this paper analyses the joint abilities of performance measures to predict subsequent fund failure. We examine the probability of disappearance over a time window, and expected fund survival time, and study the circumstances of a fund’s disappearance, its currency and domicile. By combining relevant measures, fund failure appears to a significant extent predictable, more than with single classical measures. Survivorship predictability has significant economic value. Such evidence suggests that past performance does not only influence investors’ perception of fund quality, but also reflects managers’ ability to sustain performance.
Disciplines :
Finance
Author, co-author :
Cogneau, Philippe ; Université de Liège - ULiège > HEC-Ecole de gestion : UER > Gestion financière
Hübner, Georges ; Université de Liège - ULiège > HEC-Ecole de gestion : UER > Gestion financière
Language :
English
Title :
The prediction of fund failure through performance diagnostics
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Bollen N.P.B., Busse J.A. On the timing ability of mutual fund managers. Journal of Finance 2001, 56:1075-1094.
Bollerslev T., Gibson M., Zhou H. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities. Journal of Econometrics 2011, 160:235-245.
Brown S.J., Goetzmann W.N. Performance persistence. Journal of Finance 1995, 50:679-698.
Brown, S.J., Goetzmann, W.N., Park, J., 1997. Conditions for Survival: Changing Risk and the Performance of Hedge Fund Managers and CTAs. Unpublished Manuscript, Yale University.
Bu Q., Lacey N. On understanding mutual fund terminations. Journal of Economics and Finance 2009, 33:80-99.
Butera G., Faff R. An integrated multi-model credit rating system for private firms. Review of Quantitative Finance and Accounting 2006, 27:311-340.
Cameron A.C., Hall A.D. A survival analysis of Australian equity mutual funds. Australian Journal of Management 2003, 28:209-226.
Carhart M.M. On persistence in mutual fund performance. Journal of Finance 1997, 52:57-82.
Carhart M.M., Carpenter J., Lynch A.W., Musto D. Mutual fund survivorship. Review of Financial Studies 2002, 15:1439-1463.
Chapman, L., Stevenson, M., Hutson, E., 2008. Identifying and predicting financial distress in Hedge funds. Working Paper, 28th International Symposium on Forecasting - Nice.
Chen S.-N. An examination of risk-return relationship in bull and bear markets using time-varying betas. Journal of Financial and Quantitative Analysis 1982, 17:265-286.
Chen N.-F., Roll R., Ross S.A. Economic forces and the stock market. Journal of Business 1986, 59:383-403.
Christopherson J.A., Ferson W.E., Turner A.L. Performance evaluation using conditional alphas and betas. Journal of Portfolio Management 1999, 26:59-72.
Cogneau P., Hübner G. The (more than) 100 ways to measure portfolio performance - part 1: standardized risk-adjusted measures. Journal of Performance Measurement 2009, 13:56-71.
Cogneau P., Hübner G. The (more than) 100 ways to measure portfolio performance - part 2: special measures and comparison. Journal of Performance Measurement 2009, 14:56-69.
Coles, J.L., Daniel, N.D., Nardari, F., 2006. Does the choice of model or benchmark affect inference in measuring mutual fund performance? Working Paper, Arizona State University.
Darolles, S., Gagliardini, P., Gouriéroux, C., 2014. Contagion and systematic risk: an application to the survival of Hedge funds. Working Paper.
Duffie D., Eckner A., Horel G., Saita L. Frailty correlated default. Journal of Finance 2009, 64:2089-2183.
Elton E.J., Gruber M.J., Blake C.R. Survivorship bias and mutual fund performance. Review of Financial Studies 1996, 9:1097-1120.
Fama E.F., French K.R. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 1993, 33:3-56.
Ferson W.E., Harvey C.R. Predictability and time-varying risk in world equity markets. Research in Finance 1995, 13:25-88.
Hu Y.-C., Ansell J. Measuring retail company performance using credit scoring techniques. European Journal of Operational Research 2007, 183:1595-1606.
Hübner G. The Generalized Treynor Ratio. Review of Finance 2005, 9:415-435.
Jensen M.C. The performance of mutual funds in the period 1945-64. Journal of Finance 1968, 23:389-416.
Jobson J.D., Korkie B.M. Performance hypothesis testing with the Sharpe and Treynor measures. Journal of Finance 1981, 36:888-908.
Kim M.K., Zumwalt J.K. An analysis of risk in bull and bear markets. Journal of Financial and Quantitative Analysis 1979, 14:1015-1025.
King M., Wadhwani S. Transmission of volatility between stock markets. Review of Financial Studies 1990, 60:5-33.
Koenker R., Hallock K. Quantile regression: an introduction. Journal of Economic Perspectives 2001, 15:143-156.
Laitinen E.K. Predicting a corporate credit analyst's risk estimate by logistic and linear models. International Review of Financial Analysis 1999, 8:97-121.
Liang B., Park H. Predicting Hedge fund failure: a comparison of risk measures. Journal of Financial and Quantitative Analysis 2010, 45:199-222.
Lobosco A., DiBartolomeo D. Approximating the confidence intervals for Sharpe style weights. Financial Analysts Journal 1997, 53:80-85.
Lunde A., Timmermann A., Blake D. The hazards of mutual fund underperformance: a Cox regression analysis. Journal of Empirical Finance 1999, 6:121-152.
Malkiel B.G. Returns from investing in equity mutual funds 1971-1991. Journal of Finance 1995, 50:549-572.
Massa M., Patgiri R. Incentives and mutual fund performance: higher performance or just higher risk taking?. Review of Financial Studies 2009, 22:1777-1815.
Melnik A.L., Plaut S.E. Industrial structure in the Eurocredit underwriting market. Journal of International Money and Finance 1996, 15:623-636.
Newson R. Confidence intervals for rank statistics: Somers' D and extensions. Strata Journal 2006, 6:309-334.
Ng, M.S.F., 2008. Development of a Forecasting Model for Hedge Fund Failure: A Survival Analysis Approach (Thesis). University of Sydney.
Niklis, D., Doumpos, M., Zopounidis, C., 2012. Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines. Working Paper, Technical University of Crete.
Paul P., Pennell M.L., Lemeshow S. Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Statistics in Medicine 2013, 32:67-80.
Rohleder M., Scholz H., Wilkens M. Survivorship bias and mutual fund performance: relevance, significance, and methodical differences. Review of Finance 2011, 15:441-474.
Sawicki J. Investors' differential response to managed fund performance. Journal of Financial Research 2001, 24:367-384.
Sharpe W.F. Asset allocation: management style and performance measurement. Journal of Portfolio Management 1992, 18:7-19.
Sirri E.R., Tufano P. Costly search and mutual fund flows. Journal of Finance 1998, 53:1589-1622.
Somers R.H. A new asymmetric measure of association for ordinal variables. American Sociological Review 1962, 27:799-811.
ter Horst J.R., Nijman T.E., Verbeek M. Eliminating look-ahead bias in evaluating persistence in mutual fund performance. Journal of Empirical Finance 2001, 8:345-373.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
