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Rebalancing of leveraged ETFs (LETFs) and delta-hedging of equity options by intermediaries are two distinct and economically significant sources of liquidity demands. We show that they induce end-of-day momentum and mean-reversion in returns. While gamma effects are persistent throughout our sample, LETFs effects have decreased over time. We empirically study these effects and their potential drivers. We find that LETF flows attract more liquidity provision and their effects on prices are shorter-lived. Intermediaries can strategically decide the timing of their delta-hedging, resulting in less predictable flows. This shows the benefits of information disclosure on market liquidity and price distortion.

Leveraged ETFs and market makers who are active in option markets must ad-just imbalances arising from market movements. Establishing delta-neutrality may cause either return momentum or reversal depending on the sign and size of the imbalance vis-a-vis market prevailing liquidity. We find that a large and negative (positive) aggregated gamma imbalance, relative to the average dollar volume, gives rise to an economically and statistically significant end-of-day momentum (rever-sal). We compare this channel to the rebalancing of leveraged ETFs and find that the effect generated by leveraged ETFs is economically larger. Consistent with the notion of temporary price pressure, the documented effects quickly revert at the next day’s open. Information-based explanations are unlikely to cause the results, suggesting a non-informational channel through which leveraged ETFs and option markets affect underlying stocks towards the market close.

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions, costly arbitrage, and option mispricing.