We exploit detailed transaction and position data for a sample of long-short equity hedge funds to study the trading activity of fundamental investors. We find that hedge funds exhibit skill in opening positions, but that they close their positions too early, thereby forgoing about one-third of the trades’ potential profitability. We explain this behavior with the limits of arbitrage: hedge funds close positions early in order to reallocate their capital to more profitable investments and/or to accommodate tightened financial constraints. Consistent with this view, we document that hedge funds leave more money on the table after opening new positions, negative returns, or increases in funding constraints and volatility

The distress anomaly reflects the abnormally low returns of high credit risk stocks during financial distress. Evidence from stocks and corporate bonds reinforces the anomaly and challenges rationales based on shareholders’ ability to extract value from bondholders, time-varying betas, lottery-type preferences, biased earnings expectations, and limits-to-arbitrage. Moreover, mispricing of distressed stocks and bonds is associated with excess investment and excess external financing. Potential real distortions are materially understated when assessed based only on equity mispricing. We emphasize the important role of corporate bonds in dissecting the distress anomaly, and show that the anomaly is an unresolved puzzle

By applying machine learning to the accurate and cost-effective classification of photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) obtained from a large sample of news photos. Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear. We examine whether Photo Pessimism and pessimism embedded in news text act as complements or substitutes for each other in predicting stock returns and find evidence that the two are substitutes

This paper shows the cross-sectional and time series momentum in currencies, which cannot be explained by carry and dollar factors, summarize the autocorrelation of these factors. These momentum strategies long currency factors following positive factor returns and short them following losses. Carry and dollar factors are strongly autocorrelated and only earn significantly positive excess returns following positive factor returns. By contrast, idiosyncratic currency returns contain little momentum. Consequently, factor momentum not only outperforms the cross-sectional and time series momentum but also explains them. Limits to arbitrage and time-varying risk premium help explain factor momentum

We provide the first systematic evidence on the link between long‐short anomaly portfolio returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high‐dimensional setting. We find that long‐short anomaly portfolio returns evince statistically and economically significant out‐of‐sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence

This paper introduces a real-time, continuous measure of national sentiment that is language-free and thus comparable globally: the positivity of songs that individuals choose to listen to. This is a direct measure of mood that does not pre-specify certain mood-affecting events nor assume the extent of their impact on investors. We validate our music-based sentiment measure by correlating it with mood swings induced by seasonal factors, weather conditions, and COVID-related restrictions. We find that music sentiment is positively correlated with same-week equity market returns and negatively correlated with next-week returns, consistent with sentiment-induced temporary mispricing. Results also hold under a daily analysis and are stronger when trading restrictions limit arbitrage. Music sentiment also predicts increases in net mutual fund flows, and absolute sentiment precedes a rise in stock market volatility. It is negatively associated with government bond returns, consistent with a flight to safety

The cash conversion cycle (CCC) is important for fundamental analysis as an indicator of management effectiveness in cash and financing. However, there is a lack of empirical evidence for its implications on asset pricing except for the very recent findings that high CCCs negatively predict stock returns in the U.S. By investigating 47 developed and emerging markets from 1993 to 2018, we find a mild CCC effect across the globe. The Low-minus-High equal-weighted hedge portfolios sorted by components of CCC yield significant Fama-French five-factor alphas ranging from 0.277 to 0.730% per month. Our results are consistent with a mispricing explanation by analyzing earnings prediction, announcement returns around future earnings, and limits of arbitrage although there is also some evidence for a risk-based explanation. Moreover, the CCC effect is stronger in emerging markets than developed markets and for markets with more political risk and less integrated with the global market