How Much Error Is in The Tracking Error? A Study of the Impact of Estimation Risk on Mutual Fund Tracking Errors

Andrew F. Siegel, University of Washington
Artemiza Woodgate, University of Washington

A mutual fund’s tracking error is considered to be a very important measure of performance, and the tracking error volatility is an important measure of the fund’s risk. We propose to look at the impact of estimation risk on these measures and derive closed form formulae for the anticipated mean and variance of the tracking error after adjusting for estimation risk bias. We will test these formulae through simulations and using real data from Morgan Stanley Indices. Additionally we will look at how trading costs are affected by the bias adjustment, and how short-sales or risk constraints affect the adjustment.

A Conditional Characteristics Model of Stock Prices

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Malcolm Baker, Harvard University
Jeffrey Wurgler, New York University

We propose to examine how investor sentiment affects the cross-section of stock returns. Theory predicts that a broad wave of sentiment will disproportionately affect stocks whose valuations are highly subjective and are difficult to arbitrage. We plan to test this prediction by studying how the cross-section of subsequent stock returns varies with proxies for beginning-of-period investor sentiment. When the sentiment is low, we expect subsequent returns on smaller, high volatility, unprofitable, no-dividend-paying, extreme-growth, and distressed stocks to be relatively high, consistent with an initial under pricing of these stocks. When sentiment is high, we expect these patterns to attenuate or fully reverse.

Closing Time? The Signaling Content of Mutual Fund Closures

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Arturo Bris, Yale School of Management
Huseyin Gulen, Virginia Tech
Padma Kadiyala, Fairleigh Dickinson University
P. Raghavendra Rau, Purdue University

We analyze why managers of open-ended mutual funds choose to close their funds to new investment. We use a multi-period signaling model to derive the optimal closing and reopening decision for fund managers when fund performance is a noisy indicator of managerial skill. The model predicts that funds close when they are sufficiently large and when the population of high-quality managers is low. We test the empirical predictions of the model on a sample of 141 mutual funds that closed to new investment between 1992 and 2002. Our most important finding is that the length of the fund closure period is a significant predictor of excess fund flows at reopening.

Market Frictions, Price Delay, and the Cross-Section of Expected Returns

Link to PDF File:Market Frictions, Price Delay, and the Cross-Section of Expected Returns
Kewei Hou, Ohio State University
Tobias J. Moskowitz, University of Chicago

We parsimoniously characterize the severity of market frictions affecting a stock using the average delay with which its share price responds to information. The most severely delayed firms command a large return premium that captures the size effect and part of the value premium. Moreover, idiosyncratic risk is priced only among the most delayed firms. These results are not explained by other sources of return premia, microstructure, or traditional liquidity effects (price impact and cost), but appear most consistent with investor recognition. The very small segment of extremely delayed, neglected firms captures substantial variation in cross-sectional average returns.

Optimal Asset Allocation and Risk Shifting Incentives in Money Management

Suleyman Basak, London Business School and CEPR
Anna Pavlova, Massachusetts Institute of Technology
Alex Shapiro, New York University

Money managers are reward for increasing the value of assets under management, and predominately so in the mutual fund industry. In a dynamic asset allocation framework, we show that as the year-end approaches, the ensuing convexities in the manager’s objective induce her to closely mimic the index, relative to which her performance is evaluated, when the fund’s year-t-date return is sufficiently high. As her relative performance falls behind, she chooses to deviate from the index by either increasing or decreasing the volatility of her portfolio. The maximum deviation is achieved at a critical level of under performance. It may be optimal for the manager to reach such deviation via selling the risky asset despite it positive risk premium. The manager’s policy results in economically significant departures from investors’ desired risk exposure. We then demonstrate how constraining the managers’ invest mend opportunity set, via a simple benchmarking restriction, can ameliorate the adverse effects of managerial incentives. Finally, we employ data on mutual funds holdings to provide empirical support for our implications regarding managerial risk-taking incentives. In that we differ from the existing literature that used portfolio volatility as a measure of risk-taking.