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.

Financial regulators frequently restrict short selling without knowing how much activity they suppress or which traders they constrain. We study 183 tightenings and relaxations across 38 markets using global securities-lending data. Strikingly, most regulations have little effect on short selling. Even covered bans reduce shares on loan by only about 13%. Reexamining the 2008-2009 bans, we find that the widely cited spread increase is entirely driven by the United States and does not reverse when bans are lifted. Market effects instead depend on whose trading a rule constrains. Covered bans that exempt market makers reduce directional shorting but leave liquidity and market stability unchanged. Rules that primarily bind market makers worsen liquidity and stability while leaving shorting largely unchanged. Short-sale regulation therefore matters through the trading function it disrupts, not through its formal stringency.

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.

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.

ABSTRACTWe 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 In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

The recent surge in retail option trading has sparked concerns about gambling and significant losses. We show that these concerns may be exaggerated using a novel trader-level dataset of about $20 billion in retail stock and option trades between 2020 and 2022. Option trades account for nearly half of all trades in 2022, making them a vital part of retail trading. Moreover, many investors trade only options. Despite wide bid-ask spreads, retail option trades incur relatively small losses. Although options theoretically resemble lottery tickets, we find little evidence of positive skewness in realized dollar profits, contradicting gambling-driven trading. A typical retail trade is the purchase of a one-day S&P 500 index call held for an hour. Retail investors tend to trade options to affordably participate in high-priced underlyings and to obtain leverage. Overall, we offer the first thorough trader-level analysis of current retail option trading.

Options market makers (OMMs) are essential as they provide continuous two-sided quotes and facilitate most option trades. However, little is known about how they perform or manage risk. We use unique account-level data for KOSPI 200 index options and futures to identify and study 43 OMMs. While OMMs' strategies are surprisingly heterogeneous, they share several common features. First, OMMs are highly profitable and make money on most days. Second, although option investors are commonly believed to regularly delta-hedge in the underlying, we find that only four out of 43 OMMs delta-hedge and study delta-hedgers' strategies. Finally, OMMs quickly revert inventory positions to the desired level by providing liquidity with limit orders. Overall, OMMs primarily rely on active inventory rebalancing to manage risk.

Closing auctions set daily closing prices for U.S. stocks and account for a striking 7.5% of daily volume in 2018, up from 3.1% in 2010. We study closing auctions in the new regime of record volume. Closing auctions appear to match volumes at low cost: closing prices typically match pre-close bid or ask prices, and price impact is lower than during continuous trading. Auction price deviations revert quickly and almost completely, on average. Auction-to-intraday volume spikes on S&P 500 additions and increases permanently afterwards, suggesting that closing volume is fueled directly and indirectly by the growth of indexing and ETFs.

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.

Overnight returns are mostly driven by news, whereas intraday returns are mostly driven by investors' trading. We use this fact to test theories of momentum and reversal with a sample of intraday and overnight returns spanning 1926 to 2019. Portfolios formed on past intraday returns display short-term reversal and momentum without long-term reversal. In contrast, portfolios formed on past overnight returns display only long-term reversal. These results are consistent with underreaction theories of momentum, where investors underreact to the information conveyed by the trades of other investors.

We find that short sale costs eliminate the abnormal profits generated by asset pricing anomalies. While many anomalies persist out-of-sample, they cannot be profitably exploited due to stock borrow fees. Using a comprehensive sample of 162 anomalies, we show that the average of these long-short anomalies earns a significant 0.15% per month before costs. However, this average is -0.02% once portfolio returns are adjusted for stock borrow fees. Moreover, the anomalies are not profitable before accounting for borrow fees if the stocks with high borrow fees, 12% of all stocks, are excluded from the analysis. Thus, short sale costs explain why these anomalies exist despite arbitrageurs' best efforts to exploit them.

Informed trading plays a crucial role in financial markets. But how do informed traders gain their edge? We study option traders – an important class of informed investors – and find that their trades used to strongly predict future stock returns. However, the put-call ratio and other measures that aggregate the information content of options trades suddenly and permanently ceased to predict stock returns after October 2009. This timing coincides with the arrest of Raj Rajaratnam and the launch of an unprecedented campaign against insider trading by large institutional investors. We hypothesize that insider trading in the options market fueled the return predictability, which stopped after Rajaratnam’s arrest. Further empirical tests are more consistent with this hypothesis than with alternative explanations. Thus, insider trading may have been more common than previously thought.

We study how trading activity affects liquidity and volatility by introducing two periodicities in trading activity. First, trades and quote updates are much more frequent within the first 100 ms of a second than during its remainder. Second, trading activity often spikes at intervals of exactly one second. For these two periodicities, higher trade and quote intensities lead to higher volatility, but they do not significantly affect stock liquidity. These periodicities are likely caused by algorithms that trade predictably by repeating instructions in loops with round start times and time increments. Such predictable behavior may provide an example of behavioral biases in trading algorithms.

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.

We examine which categories of option trading volume carry information about future stock prices around corporate news announcements. We predict and find that purchases of options are informative on news days and ahead of unscheduled events but not before scheduled events, and sales of options predict returns only ahead of scheduled news releases. Therefore, although the arrival of new information is an important reason why option volume predicts stock returns, this relation depends on whether the information is scheduled or unscheduled because only the former affects volatility and thus option prices. We also study how trading costs and margin costs affect ex post profitability around news. This paper was accepted by Karl Diether, finance. Funding: D. Weinbaum gratefully acknowledges research support from the Harris Fellowship in Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4543 .

When activist shareholders file Schedule 13D filings, the average stock-price volatility drops by approximately 10%. Prior to filing days, volatility information is reflected in option prices. Using a comprehensive sample of trades by Schedule 13D filers that reveals on what days and in what markets they trade, we show that on days when activists accumulate shares, option-implied volatility decreases, implied volatility skew increases, and implied volatility time slope increases. The evidence is consistent with a theoretical model where it is common knowledge that informed trading occurs only in the stock market and market makers update option prices based on stock-price and order-flow dynamics.

Do order flows in index derivatives play an informational role? Weekly index put order flow on the International Securities Exchange positively and robustly predicts weekly S&P 500 index returns. This result obtains mainly for net put buying and is stronger in high VIX periods and in periods following macroeconomic announcements. We explore rationales for our findings, which include investor sentiment, the notion that market makers trade on information in options markets, and option-based risk protection strategies used by retail investors. The last explanation accords best with our analysis. This paper was accepted by Tyler Shumway, finance.

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.