I joined Columbia Business School as an assistant professor of finance in July 2017. I currently work in the areas of asset pricing, asset management, and machine learning.
(with S. Abis)
Mutual fund prospectuses contain a wealth of qualitative information about fund strategies, yet a systematic analysis of this content is missing from the literature. We use machine learning to group together funds with similar strategy descriptions, and ask whether they act in accordance with the text. Despite weak legal recourse for investors, we find that mutual funds largely do keep their promises. We document a market-based disciplinary mechanism: when funds diverge from their group's core strategy, investors withdraw capital. Funds respond to these punitive outflows by reducing their divergence from the peer group average at a faster rate.
(with S. Abis, A. M. Buffa, and A. Javadekar)
We study qualitative information disclosures by mutual funds when investors learn from such disclosures in addition to past performance. We show theoretically that fund managers with specialized strategies optimally choose to disclose detailed strategy descriptions, while those with standardized strategies provide generic descriptions. Generic descriptions lead to benchmarking errors by investors who confuse factor returns and skill, resulting in higher fund flow uncertainty. While all managers dislike this uncertainty, those with above-average factor exposures also benefit from the errors on balance and thus grow larger. We find evidence for this trade-off in the data, using a comprehensive dataset of fund prospectuses: funds with more informative descriptions are smaller and more specialized, exhibit higher flow- performance sensitivity, and show lower correlation between size and flow volatility. Investors in these funds make fewer benchmarking errors, and the effects are more pronounced for funds with shorter return histories.
I show that fund managers who are compensated for relative performance optimally shift their portfolio weights towards those of the benchmark when volatility rises, putting downward price pressure on overweight stocks and upward pressure on underweight stocks. In quarters when volatility rises most (top quintile), a portfolio of aggregate-underweight minus aggregate-overweight stocks returns 2% to 5% per quarter depending on the risk adjustment. Placebo tests on institutions without direct benchmarking incentives show no effect. My findings cannot be explained by fund flows and thus constitute a new channel for the price effects of institutional demand.
(with R. Di Mascio and N. Naik)
We document novel facts about the term structure of institutional trading and performance using transaction-level data on professional fund managers. New stock purchases earn positive risk-adjusted returns that decay gradually over the subsequent twelve months, and managers continue to buy the same stock in small increments for as long as the alpha remains positive, with proportional intensity. Greater competition for information and more highly correlated signals are associated with more aggressive trading and lower alpha. Our findings confirm many predictions of informed trading models, but also pose some new challenges for the theoretical literature.
Trade-Based Performance Measurement
(with R. Di Mascio and N. Naik)
We propose new metrics for investment performance based on short-run trading profitability. Since investment opportunities are scarce and value-relevant information decays over time, marginal decisions made by fund managers (i.e., trades) should provide more accurate signals about underlying skill than portfolio alphas, which are contaminated by the returns on "stale" positions. Our measures range from the very simple ("hit rate", or the fraction of trades that outperform the benchmark over the subsequent month) to the more complex (regressions relating trade size to subsequent profitability). We examine the validity of these measures in a global sample of long-only equity funds, for which we observe daily trading activity. In our sample, trade-based metrics are more persistent than portfolio alphas and, more importantly, are better able to forecast future portfolio alphas (in a mean squared error sense). Simple and complex methods are almost equally effective. A hypothetical manager-selection exercise reveals that trade-based performance measurement can improve the risk-adjusted returns to investors by up to 3% per annum.