I show that the inventory risk faced by market‐makers has a first‐order effect on option prices. I introduce a simple approach that decomposes the price impact of trades into inventory risk and asymmetric information components. While both components are large for option trades, the inventory risk component is larger. Using the full panel of daily option returns, I find that option order imbalances attributable to inventory risk have five times larger impact on option prices than previously thought. Finally, I find that past order imbalances have greater predictive power than any other commonly used predictor of option returns.

We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.

Recent research argues that uncertainty about future stock borrowing fees hinders short‐selling, and this risk explains the performance of short strategies. One possible mechanism is that borrowing fee risk carries a risk premium. Since the present value of the uncertain borrowing fee is reflected in options prices, the difference between option‐implied and realized fees estimates this premium. We find that the risk premium is small. Moreover, if the risk premium is substantial, it should be reflected in the returns to short‐selling stock after adjusting for stock borrowing fees. However, borrowing fee risk does not predict fee‐adjusted returns.