Innovations in Hedge Funds
Saturday, Jan. 6, 2018 2:30 PM - 4:30 PM
- Chair: Mila Getmansky Sherman, University of Massachusetts-Amherst
Information Environment, Sophisticated Investors, and Market Efficiency: Evidence From a Natural Experiment
AbstractThis paper examines how changes in information environment affect the trading behavior of sophisticated investors and stock price efficiency. Using closures of brokerage firms as an exogenous shock to information environment, we study how hedge funds trade the affected stocks and how their trades in turn impact price efficiency. We find that, after exogenous reduction of analyst coverage, 1) the magnitudes of post-earnings-announcement-drift (PEAD) become stronger; 2) hedge funds trade more aggressively on the affected stocks in that their abnormal holdings increase (decrease) more prior to positive (negative) earnings announcements; 3) hedge funds obtain higher abnormal returns on the affected stocks; and 4) conditional on high levels of hedge fund holdings prior to earnings announcements, the increase in the magnitudes of PEAD becomes significantly weaker and is indistinguishable from zero, suggesting that the participation of hedge funds can restore the impaired market efficiency. Furthermore, based on a novel dataset of Internet search traffic for EDGAR filings, we identify a channel through which hedge funds increase information acquisition about the affected firms after reduction of analyst coverage. Overall, these results are consistent with a substitution effect between sophisticated investors and providers of public information in facilitating market efficiency.
Upside Potential of Hedge Funds as a Predictor of Future Performance
AbstractThis paper examines whether chasing past high returns has a rational basis for hedge fund investors. We measure upside potential based on the maximum monthly returns of hedge funds (MAX) over a fixed time interval, and show that MAX successfully predicts cross-sectional differences in future fund returns. Hedge funds with strong upside potential generate 0.70% per month higher average returns than funds with weak upside potential. After controlling for alternative risk and performance measures and a large set of fund characteristics, the positive link between MAX and future returns remains highly significant. Moreover, funds with strong upside potential have higher probability of survival, attract more capital, and are rewarded with higher fees. The results indicate that the market/macro-timing ability of hedge funds together with their extensive use of dynamic trading strategies is the source behind MAX’s predictive power.
The Fix is In: Properly Backing out Backfill Bias
AbstractHedge 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.
- G1 - General Financial Markets