ABSTRACT 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.

Average delta hedged returns for Standard & Poor's 500 index options are large: −0.7% per day. When we decompose these option returns into intraday and overnight components, average close-to-open returns are −1% per day and open-to-close returns are positive, 0.3%. A similar return pattern holds for all maturity and moneyness categories and equity options. These positive intraday returns are particularly difficult to explain. However, our results are consistent with option prices’ failing to account for the well-known fact that stock volatility is substantially higher intraday than overnight. These findings help explain price formation in the options market.

We study how the market return depends on the time of the day using E-mini S&P 500 futures actively traded around the clock. Strikingly, 4 hours around European open account for the entire average market return. This period’s returns have a 1.6 Sharpe ratio and remain high after transaction costs. Average returns are a noisy zero during the remaining 20 hours. High returns are consistent with European investors processing information accumulated overnight and thus resolving uncertainty. Indeed, uncertainty reflected by VIX futures prices rises overnight and falls around European open. The results are stronger during the 2020 COVID crisis.

Conventional estimates of the costs of taking liquidity in options markets are large. Nonetheless, options trading volume is high. We resolve this puzzle by showing that options price changes are predictable at high frequency, and many traders time executions by buying (selling) when the option fair value is close to the ask (bid). Effective spreads of traders who time executions are less than 40% of the size of conventional measures, and the overall average effective spread is one-quarter smaller than conventional estimates. Price impact measures are also affected. These findings alter conclusions about the after-cost profitability of options trading strategies.

We use tick-by-tick quote data for 39 liquid US stocks and options on them, and we focus on events when the two markets disagree about the stock price in the sense that the option-implied stock price obtained from the put-call parity relation is inconsistent with the actual stock price. Option market quotes adjust to eliminate the disagreement, while the stock market quotes behave normally, as if there were no disagreement. The disagreement events are typically precipitated by stock price movements and display signed option volume in the direction that tends to eliminate the disagreements. These results show that option price quotes do not contain economically significant information about future stock prices beyond what is already reflected in current stock prices, i.e., no economically significant price discovery occurs in the option market. We also find no option market price discovery using a much larger sample of disagreement events based on a weaker definition of a disagreement, which verifies that the findings for the primary sample are not due to unusual or unrepresentative market behavior during the put-call parity violations.

We study how intraday and overnight components of past returns predict future stock returns from 1926 to 2019. Portfolios formed on past intraday returns display momentum without long-term reversal, whereas portfolios formed on past overnight returns display no momentum. We link this asymmetric day-night pattern to the fact that most trading occurs intraday, which has remained stable over time. Evidence from international stock markets, intraday intervals, and analyst expectations suggests that investors underreact to private information revealed through trading. This underreaction mechanism is most consistent with Hong and Stein’s (1999) theory of momentum.

Several influential studies show that transformations of implied volatilities calculated from options prices predict stock returns. This predictability is puzzling because market participants readily observe options prices. We find that this predictability is consistent with implied volatilities reflecting stock borrow fees that are known to predict stock returns. We derive a formula relating the option-implied volatility spread to the borrow fee. Motivated by this relation, we show that the return predictability from implied volatility spread and skew decreases by at least two-thirds if high-fee stocks are excluded. The patterns for other predictors computed from option implied volatilities are similar.

ABSTRACT 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.

ABSTRACT Short‐sale costs eliminate the abnormal returns on asset pricing anomaly portfolios. While many anomalies persist out‐of‐sample before accounting for short‐sale costs, they cannot be exploited with long‐short strategies due to stock borrow fees. Using a comprehensive sample of 162 anomalies, the average long‐short portfolio return is a significant 0.14% per month before short‐sale costs, and the returns are due to the short leg. However, the average is −0.01% once returns are adjusted for borrow fees. Moreover, anomalies are not profitable even before fees if the high‐fee observations, representing 12% of stock dates, are excluded from the analysis.

ABSTRACT 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.