Using NYSE short-sale order data, we investigate whether short sellers’ informational advantage is related to firm earnings and analyst-related events. With a novel decomposition method, we find that while these fundamental event days constitute only 12% of sample days, they account for over 24% of the overall underperformance of heavily shorted stocks. Importantly, short sellers use both public news and private information to anticipate news regarding earnings and analysts. Shorting’s predictive ability remains significant after controlling for information in analyst actions and displays no reversal patterns, indicating that short sellers know more than analysts, and the nature of their information is long term.

We examine returns, order flow, and market conditions in the minutes before, during, and after NYSE and Nasdaq short sales. We find two distinct types of short sales: those that provide liquidity, and those that demand it. Liquidity-supplying shorts are strongly contrarian at intraday horizons. They trade when spreads are unusually wide, facing greater adverse selection. Liquidity-demanding shorts trade when spreads are narrow and tend to follow short-term price declines. These results support a competitive rational expectations model where both market-makers and informed traders short, indicating that these two shorting types are integral to both price discovery and liquidity provision.

We construct a long daily panel of short sales using proprietary NYSE order data. From 2000 to 2004, shorting accounts for more than 12.9% of NYSE volume, suggesting that shorting constraints are not widespread. As a group, these short sellers are well informed. Heavily shorted stocks underperform lightly shorted stocks by a risk‐adjusted average of 1.16% over the following 20 trading days (15.6% annualized). Institutional nonprogram short sales are the most informative; stocks heavily shorted by institutions underperform by 1.43% the next month (19.6% annualized). The results indicate that, on average, short sellers are important contributors to efficient stock prices.

We examine the effects of trading and information flows on the short-run behavior of stock prices by comparing the behavior of stock return volatility during trading and nontrading periods. We define nontrading periods as periods when exchanges and businesses are open but traders endogenously choose not to trade. After correcting for the bid/ask bounce and stickiness in quotes, we find that a large proportion of daily stock return volatility occurs without trades, especially for large firms. Furthermore, we provide new evidence that public (versus private) information is the major source of short-term return volatility.

Since 2012, all European Union countries have required disclosure of large short positions. This reduces short interest, bid-ask spreads, and the informativeness of prices. After specific disclosures, short-run abnormal returns are insignificantly negative, but 90-day cumulative abnormal returns are a statistically significant -5.23%. We find disclosures are likely to be followed by other disclosures, especially when the initial discloser is large or centrally located. However, there is no subsequent increase in short interest, and prices do not subsequently reverse. These results indicate that large short sellers are well informed, and that disclosures are not being used to coordinate manipulative attacks.

Using comprehensive account-level data, we separate Chinese retail investors into 5 groups and document strong heterogeneity in trading dynamics and performances. Retail investors with smaller account sizes cannot predict future returns correctly, display daily momentum patterns, fail to process public news, and show overconfidence and gambling preferences, while retail investors with larger account balances predict future returns correctly, display contrarian patterns, and incorporate public news in trading. Using performance measures established in previous literature, we find that smaller retail investors suffer from poor stock selection abilities and trading costs, while large retail investors’ stock selection abilities are offset by trading costs.

Using 2015–2019 intraday short sale data from CBOE, we show that shorting flows near the open, middle, and close all negatively predict future returns, but the shorting flows near the open and middle have stronger predictive power than shorting flows near the close. We relate our findings to three informed trading models with different predictions on the timing of the trades. The long term predictive power of shorting flows near the open and midday is consistent with Kyle’s (1985) model of steady trading; the intraday variation in shorting flows’ predictive power is more consistent with Holden and Subrahmanyam’s (1992) aggressive trading model, in the sense that predictive power of shorting flows is stronger when there is greater urgency to trade at open and when the securities lending market is more competitive; and the liquidity timing hypothesis from Collin-Dufresne and Fos (2016) is also supported by the finding that opening shorting flows increase for firms with better liquidity conditions.

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade‐related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.

We provide an easy method to identify marketable retail purchases and sales using recent, publicly available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 bps over the following week. Less than half of the predictive power of marketable retail order imbalance is attributable to order flow persistence, while the rest cannot be explained by contrarian trading (proxy for liquidity provision) or public news sentiment. There is suggestive, but only suggestive, evidence that retail marketable orders might contain firm‐level information that is not yet incorporated into prices.

We show that market‐maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity‐supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market‐level and specialist firm‐level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.