Bridge over ocean
1 January 2015 CFA Institute Journal Review

The Cross-Section of Managerial Ability, Incentives, and Risk Preferences (Digest Summary)

  1. Yaw Mante

The analysis suggests that mutual fund alphas depend not only on the fund manager’s ability but also on the manager’s risk preferences and the pay-for-performance sensitivity of the manager’s compensation contract.

What’s Inside?

The author estimates a dynamic model that he uses to try to explain mutual fund investment strategies. The manager’s risk preferences, compensation contract, and investment opportunities are used as key inputs in the developed structural model.

How Is This Research Useful to Practitioners?

The author’s model accommodates the fact that over time, managers optimally adjust their portfolios in response to fund returns and benchmark returns. Such behavior typically results in coefficients of standard performance regressions that change over time.

For the period studied, the structural model shows a more precise estimate of the fund manager’s ability. The approach also provides estimates of the additional expected return per unit of risk for a manager that is actively adjusting allocations to the active strategy and to the benchmark portfolio. Finally, the structural model clarifies the exposures of the fund to passive benchmarks (i.e., the fund’s beta).

The insights obtained from the model could facilitate better performance attribution and assessment of fund manager ability. Investors interested in selecting mutual funds may find this research useful. CEOs, leaders, and human resource departments of mutual funds trying to find effective ways to incentivize their fund managers may also find the research useful.

How Did the Author Conduct This Research?

In constructing the dynamic model, the author assumes a risk-averse mutual fund manager whose utility derives from end-of-period compensation. Compensation consists of a base salary and a variable part that is dependent on fund returns and fund flows from outside investors. Fund flows are, in turn, connected to the fund’s performance relative to a benchmark portfolio. The manager dynamically selects an optimal portfolio that takes positions in the benchmark portfolio, a cash account, and an active portfolio. Thus, managers optimally adjust their portfolios in response to fund returns and benchmark returns.

The author uses US equity mutual funds from 1985 to 2008 for estimates in the model. The parameters are estimated for each fund separately. He combines data from a number of sources for the analysis. Monthly mutual fund returns come from the CRSP Survivor-Bias-Free Mutual Fund Database. CRSP is also the source for data on the 30-day T-bill rate. Additionally, the author uses data on benchmark returns from Standard & Poor’s and Russell Investments and relies on Morningstar and the Thomson/CDA database for fund investment objective and other relevant fund information.

In addition to showing more precise estimates for fund alphas, the author’s analysis confirms that fund alphas depend on the fund manager’s ability and risk preferences as well as the pay-for-performance sensitivity of the manager’s compensation contract. He also finds important family fixed effects in ability and risk preferences. This implies that different families attract systematically different types of managers.

Abstractor’s Viewpoint

The author provides a rigorous basis for addressing two very important questions in the mutual fund industry: (1) whether managers have superior investment skills and (2) whether their incentives have an impact on the investment risk they take. His key addition to the efforts to address these two questions is in the way the developed structural model facilitates the decomposition of a fund’s alpha into three key ingredients: the manager’s risk preferences, compensation contract, and investment opportunities. The developed model also provides a way to estimate the manager’s expected return per unit of risk and the manager’s exposure to the benchmark portfolio.

We’re using cookies, but you can turn them off in Privacy Settings.  Otherwise, you are agreeing to our use of cookies.  Accepting cookies does not mean that we are collecting personal data. Learn more in our Privacy Policy.