A wide range of empirical biases hampers hedge fund databases. In this paper we focus upon survival-related biases and disentangle look-ahead biases due to self-selection of funds and due to fund termination. Self-selection arises because funds voluntarily report their information to data vendors and may decide to stop doing so. By extending existing methodology, we analyze persistence in hedge fund performance over the period 1994–2000, taking into account the above biases. The results show that look-ahead biases due to liquidation and self-selection enforce each other and may lead to overestimating expected returns by as much as 8% per year. Overall, the results are consistent with positive persistence in hedge fund returns at horizons of two and four quarters.

In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.

This paper analyzes the properties of a number of estimators that can be used to estimate short-run persistence in mutual fund returns. When data for different funds are pooled, it is advisable to correct for cross-sectional differences in expected returns. However, these adjustments may induce biases in the estimated persistence coefficients and thus lead to spurious persistence. Theoretical derivations, combined with a Monte Carlo study, show that these biases cannot be neglected for the samples that are typically used in applied work. We also estimate the short-run persistence in two samples of U.S. open-end mutual funds using quarterly returns for 1987–1994. An important conclusion is that the results are quite sensitive to the estimation method that is employed.

We show that a real-time trading strategy which front-runs the anticipated forced sales by mutual funds experiencing extreme capital outflows generates an alpha of 0.5% per month during the 1990–2010 period. The abnormal return stems from selling pressure among stocks that are below the NYSE mean size and cannot be attributed to the arrival of public information. While the largest stocks also exhibit downward price pressure, their prices revert before the front-running strategy can detect it. The duration of the anticipated selling pressure has decreased from about a month in the 1990s to about two weeks in the most recent decade. Our results suggest that publicly available information of fund flows and holdings exposes mutual funds in distress to predatory trading.

We construct hypothetical copycat funds to investigate the performance of free-riding strategies that duplicate the disclosed asset holdings of actively managed mutual funds. On average, copycat funds are able to generate performance that is comparable to their target mutual funds, taking into account transaction costs and expenses. However, their relative success increased significantly after 2004 when the SEC imposed quarterly disclosure regulations on all mutual funds. We also find substantial cross-sectional dispersion in the relative performance of copycat funds. Free-riding on the portfolios disclosed by past winning funds and funds that disclose representative holdings generates significantly better performance net of trading costs and expenses than the vast majority of mutual funds. The results indicate that free-riding on disclosed fund holdings is an attractive strategy and suggest that mutual funds can suffer from the information disclosure requirements.

Using monthly return data of more than 6400 US equity mutual funds we investigate short-run performance persistence over the period 1984–2003. We sort funds into rank portfolios based on past performance, and evaluate the portfolios’ out-of-sample performance. To cope with short ranking periods, we employ an empirical Bayes approach to measure past performance more efficiently. Our main finding is that when funds are sorted into decile portfolios based on 12-month ranking periods, the top decile of funds earns a statistically significant, abnormal return of 0.26 percent per month. This effect persists beyond load fees, and is mainly concentrated in relatively young, small cap/growth funds.

We analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence. In contrast to earlier results for mutual funds, the impact of look-ahead bias is exacerbated for hedge funds due to their greater level of total risk. At the four-quarter horizon, look-ahead bias can be as much as 3.8%, depending upon the decile of the distribution. We find positive persistence in hedge fund quarterly returns after correcting for investment style. The empirical pattern at the annual level is also consistent with positive persistence, but its statistical significance is weak.

We study active investment skills in relation to returns to scale in the active mutual fund industry. Using a sample of 13,807 funds from sixteen domicile countries investing in forty-two equity markets from 2001 to 2014, we find that they achieve negative trading performance on average, driven mainly by particularly low returns to their trades in US equities. Exploring their investment environment, we find convincing evidence of decreasing returns to scale around the world, especially for the US market. Based on theory of optimal fund size, we estimate the optimal size of the active mutual fund industry. We find that the active mutual fund industry in USA has exceeded the optimal level, whereas in the international markets, there may still be room for further expansion. Consistent with this view, we find that mutual fund managers have been gradually reallocating their assets away from the USA and more into international equity markets.

This paper tests the static tradeoff theory against the pecking order theory. We focus on an important difference in prediction: the static tradeoff theory argues that a firm increases leverage until it reaches its target debt ratio, while the pecking order yields debt issuance until the debt capacity is reached. We find that for our sample of US firms the pecking order theory is a better descriptor of firms’ issue decisions than the static tradeoff theory. In contrast, when we focus on repurchase decisions we find that the static tradeoff theory is a stronger predictor of firms’ capital structure decisions.