Christopher Schwarz

The Mutual Fund Scandal and Investor Response

Financial scandals permeate the news. The mutual fund scandal was one of the biggest financial news stories in the early part of the last decade and the largest in the 65-year history of mutual funds. Many fund companies—totaling more than 1,000 funds and

The Delisting Bias in Hedge Fund Databases

1 trillion in assets—were investigated for late trading and market timing allegations. This study empirically examines the effects of a large-scale scandal on investor behavior, investor reaction, and fund performance. The authors find that although equity funds involved in a scandal performed well before and after the scandal period, these same funds significantly underperformed their peers during the scandal period, even after adjusting for market effects and fund characteristics, and in spite of increasing their own risk profiles. Specifically, funds involved in scandals experienced performance declines of more than 80 basis points per year. This finding suggests the existence of a meaningful and significant “distraction” penalty for fund families, and ultimately investors. Funds involved in scandals experienced a significant reduction in flows that continued during the post-scandal period, averaging nearly 20% of the affected fund’s customer base. The authors attribute this decline in flows to an increase in monitoring costs for investors—a so-called “scandal tax.” Funds involved in investigations that were announced later in the scandal period experienced a much smaller exodus. They also find that retail investment channel flows continued to exit affected funds regardless of post-scandal performance, whereas institutional channel flows returned to funds that performed well post-scandal. Finally, a subsequent reduction in expenses charged to investors was not effective in re-attracting flows to affected funds.

Decision Making and Risk Aversion in the Cash Cab

As is well known, hedge fund databases suffer from various types of serious biases. While many of these biases have been addressed, the delisting bias is much more difficult to control. In this article, the authors use information from three hedge fund databases to provide direct estimates of this bias. Based on the fact that funds delisted in one database often continue to report returns to another, they estimate the delisting bias is at least 35 bps per annum. Their analysis also provides estimates of frequencies and average losses for different delisting reasons. The delisting bias largely explains the puzzling differences between the performance of the direct hedge fund investments and that implied by funds of hedge funds. The authors estimate that the performance of hedge fund indices should be adjusted downward by about 50 bps to account for the delisting bias.

The Fix is In: Properly Backing Out Backfill Bias

We use the Emmy Award-winning game show Cash Cab to study decision-making in a risky framework. This is a unique environment because, unlike other game shows used to examine risk-aversion, players participate individually or in teams varying in number from two to five. This creates a natural laboratory to measure performance and risk aversion conditional upon the size of the team as well as the characteristics of the team members. Teams are much more likely to complete overall tasks successfully. Most importantly, risk aversion estimates indicate that when participants are part of a group, they focus on the overall size of the dollar amounts that are “at risk”, rather than their “slice of the pie”. The implications of our results span a number of areas where groups are part of the financial decision-making process, including investment analysis and portfolio management, corporate governance, and corporate finance.

Are Mutual Fund Investors Bayesian Learners

Hedge fund researchers have long known about backfill bias, typically correcting for it by truncating a fixed number of returns from the beginning of each fund’s return series. However, we document that this practice decreases the percentage of backfilled returns by only 25%. Thus, empirical conclusions using this correction are still biased by backfill, including average performance and performance’s relation with size, age, and other fund characteristics. Unfortunately, many databases do not include the listing dates needed to properly control for this bias (now including TASS.) We therefore propose a novel method to infer listing dates when not available.

Estimating Operational Risk for Hedge Funds: The ω-Score

We study how fast investors learn about manager skills by examining the speed at which their disagreement converges. Using a novel measure of disagreement, we find that hedge fund investors learn as fast as suggested by Bayes’ rule. However, we also find mutual fund investors learn much more slowly than Bayes’ rule. Mutual fund investors’ slow learning is not caused by investors potentially paying attention to different performance measures, institutional frictions such as loads, or lack of sophistication, but is likely due to a low payoff from learning. Our results suggest learning speed depends on the motivation of financial participants.

Mandatory Disclosure and Operational Risk: Evidence from Hedge Fund Registration

Using a complete set of the SEC filing information on hedge funds (Form ADV) and the TASS data, we develop a quantitative model called the ω-Score to measure hedge fund operational risk. The ω-Score is related to conflict of interest issues, concentrated ownership, and reduced leverage in the ADV data. With a statistical methodology, we further relate the ω-Score to readily available information such as fund performance, volatility, size, age, and fee structures. Finally, we demonstrate that this risk score can be used to effectively predict fund failures in the future.