Abstract: We develop a novel measure of effort to revisit the fundamental questions of asset management: how does effort relate to incentives; and how does effort affect performance? Using unique observations of daily work activity, we define mutual fund manager effort by focusing on weekends. We find that managers facing competitive incentives exert more weekend effort. Focusing on within-advisor variation, we find that more effort follows outflows and increased volatility. Regarding future performance, more effort is followed by higher returns, especially for funds with competitive incentives, high active share, and low turnover. Finally, we use exogenous variation in effort due to weather conditions to demonstrate a causal link between effort and future returns.
Discussant: Ryan Israelsen, Michigan State University
Abstract: Big data allows active asset managers to find new trading signals but doing so requires new skills. Thus, it can reduce the ability of asset managers lacking these skills to produce superior returns. Consistent with this hypothesis, we find that the release of satellite imagery data tracking firms' parking lots reduces active mutual funds’ stock picking abilities in stocks covered by this data. This decline is stronger for funds more likely to rely on traditional sources of expertise, leading them to divest from covered stocks. These results suggest that big data has the potential to displace high-skill workers in finance.
Abstract: We argue at least 65% more total assets should be included in estimating scale of actively managed portfolios. By merging two major datasets on institutional products, we identify trillions of institutional assets that are managed under the same investment strategy as their twin mutual funds with an average return correlation of 99.9%. Overlooking the assets under management for institutional products skews crucial estimates in asset management research. We show that after including these assets in the scale metric reduces fund-level (industry-level) diminishing returns to scale of mutual funds by up to 90% (50%), suggesting a larger capacity of active asset management than the literature believed. We also observe that dollar value added of active strategies is more substantial and persistent than past assessments suggested.
Abstract: We find that hedge funds’ positions in exchange-traded fund (ETF) options contain volatility information about underlying ETF returns. Greater hedge fund option demand predicts higher variance of ETF returns over the following quarter and on days of macroeconomic news releases. The predictive power holds for options on both equity and non-equity ETFs, like fixed income and currency ETFs. A tracking portfolio of straddles based on funds’ straddle positions earns quarterly abnormal returns of 7.95%. Net of fees, funds using ETF straddles deliver lower risk and higher benchmark-adjusted returns than nonusers. We conclude that ETF options are an important venue for market volatility timing strategies.
