Fund performance and subsequent risk: a study of mutual fund tournaments using holdings-based measures


The tournament hypothesis of Brown et al. (J Finance 51(1):85–110, 1996) posits that managers of poorly performing funds actively increase portfolio risk in the second half of the year. At the same time, it is a well-established fact that stock returns and the subsequent return standard deviation are negatively related. We propose a decomposition of fund return standard deviation for the second half of the year using holdings-based measures to distinguish between risk changes that result from holding the portfolio and those that are due to managers’ trades. We extend the return gap of Kacperczyk et al. (Rev Financ Stud 21(6):2379–2416, 2008) to the return standard deviation dimension and define the volatility gap as the difference between fund return volatility and buy-and-hold portfolio volatility. Our empirical findings show that changes in the return volatilities of equity mutual funds are largely explained by shifts in buy-and-hold portfolio volatility. Thus, we find only weak evidence of tournament behavior among mutual funds.

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  1. 1.

    Because the fund holdings are available in the CRSP database only from January 2003 onward, we complement this database with the Morningstar database to cover the earlier period. As Morningstar was the holdings data source for CRSP until 2008, using a combination of the two is unlikely to introduce major inconsistencies in the sample.

  2. 2.

    Kempf and Ruenzi (2008) argue that the 1-year horizon is a natural choice as fund managers are usually compensated based on calendar-year performance. Furthermore, Sirri and Tufano (1998) show that fund flows mostly react to prior-year performance.

  3. 3.

    The Stata ado-file for two-dimensional clustering is available on Mitchell Petersen’s website at:

  4. 4.

    The model in Acker and Duck (2006) predicts that poorly performing funds tend to adopt extreme portfolios. These portfolios will not necessarily exhibit higher volatility. For example, when the market is expected to rise, managers may decide to bet against the market by lowering their betas.

    Table 1 The change in return standard deviation and past performance
  5. 5.

    We also run Eqs. (6), (7), and (8) using time fixed effects. The t statistics of the coefficient estimates remain very similar to those shown in Table 3.

  6. 6.

    In unreported results, we extend the analysis of Table 1 and assume that the fund manager pursues an equally weighted strategy and rebalances his portfolio every 6 months. We then compute the monthly returns for such a strategy and use these hypothetical returns, together with Eq. (1), to test for tournament behavior. Even when we use such a placebo strategy, the results remain qualitatively unchanged compared to those obtained with the original sample of mutual funds.

    Table 4 The tournament test regression with buy-and-hold volatility, volatility gap, and cross-period term


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This paper has benefited from the comments of Zhi Da, Raman Kuma, Nicolas Papageorgiou, Bruno Rémillard, Xiaolu Wang, Gong Zhan, and participants at the Northern Finance Association 2010 Conference in Winnipeg and the Eastern Finance Association 2010 Meetings in Miami Beach. We thank an anonymous referee for very helpful comments.

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Correspondence to Iwan Meier.



The table below repeats the pooled regression for non-index funds shown in Table 1 using for each fund \(i\) the return rank within its respective investment style for any given calendar year \(t\), \(R_{[ {1,6} ],i,t}^{\mathrm{IS}} \). Specifically, we estimate the following specification: \(\sigma _{[ {7,12} ],i,t} -\sigma _{[ {1,6} ],i,t} =\alpha +\beta _1 R_{[ {1,6} ]i,t}^{\mathrm{IS}} +\beta _2 \sigma _{[ {1,6} ],i,t} +\varepsilon _{i,t} \). We use the investment style definitions of Pastor and Stambaugh (2002) to sort funds into six categories: small company growth, other aggressive growth, growth, income, and growth and income, and maximal capital gains (we exclude sector funds from our sample). As before, return ranks within each investment style are normalized and expressed as fractional ranks ranging from 0 to 1.


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Karoui, A., Meier, I. Fund performance and subsequent risk: a study of mutual fund tournaments using holdings-based measures. Financ Mark Portf Manag 29, 1–20 (2015).

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  • Mutual funds
  • Tournament
  • Risk shifting
  • Holdings-based measure

JEL Classification

  • G11
  • G12
  • G14