Price declines over the previous quarter lead to stronger reversals across the subsequent 2 months. We explain this finding based on the dual notions that liquidity provision can influence reversals and that agents who act as de facto liquidity providers may be less active in past losers. Supporting these observations, we find that active institutions participate less in losing stocks and that the magnitude of monthly return reversals fluctuates with changes in the number of active institutional investors. Thus, we argue that fluctuations in liquidity provision with past return performance account for the link between return reversals and past returns.
A basic intuition is that arbitrage is easier when markets are most liquid. Surprisingly, we find that momentum profits are markedly larger in liquid market states. This finding is not explained by variation in liquidity risk, time-varying exposure to risk factors, or changes in macroeconomic condition, cross-sectional return dispersion, and investor sentiment. The predictive performance of aggregate market illiquidity for momentum profits uniformly exceeds that of market return and market volatility states. While momentum strategies have been unconditionally unprofitable in the United States, in Japan, and in the Eurozone countries in the last decade, they are substantial following liquid market states.
This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk. This paper was accepted by Kay Giesecke, finance.
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