Existing empirical studies provide little support for the theoretical prediction that market makers rebalance their inventory through revisions of quoted prices. This study provides evidence that the NYSE's specialist does engage in significant inventory rebalancing, but only when not constrained by the affirmative obligation to provide liquidity imposed by the Price Continuity rule. The evidence also suggests that such obligations are associated with better market quality, but impose significant costs on the specialist. The specialist mitigates these costs through discretionary trading when the rule is not binding. These findings shed light on how exchange rules affect market makers’ behavior and market quality.

We investigate the relation between common institutional ownership of the firms in an industry and product market competition. We find that common ownership is neither robustly positively related with industry profitability or output prices nor is it robustly negatively related with measures of nonprice competition, as would be expected if common ownership reduces competition. This conclusion holds regardless of industry classification choice, common ownership measure, profitability measure, nonprice competition proxy, or model specification. Our point estimates are close to zero with tight bounds, rejecting even modestly sized economic effects. We conclude that antitrust restrictions seeking to limit intra-industry common ownership are not currently warranted.

By collecting and disseminating price-sensitive information, financial analysts should reduce firm insiders’ informational advantage with a consequent impact on trading dynamics and market quality. We empirically examine the impact of complete analysts’ coverage termination on stocks’ liquidity, price discovery, and insider trading profitability. Termination leads to deteriorating liquidity and price efficiency, more informed trading, and higher profitability of insider trades. The magnitude of these effects depends on the strength of insiders’ ownership and on management’s decision whether to improve the firm’s information environment after coverage termination. Institutional investors alleviate, but do not eliminate, the negative effects of termination.

Though trading halts are a common feature in securities markets, the issues associated with the coordination of these halts across markets are not well understood. In fact, regulations often allow traders to circumvent trading halts through the use of alternative venues. Using a sample of order imbalance delayed openings on the NYSE, we examine the costs and benefits of continued trading on alternative venues when the main market calls a halt. We find that trades routed to off-NYSE venues during NYSE halts are associated with significant price discovery and lead to an improved post-halt trading environment. In addition, limit orders routed through ECNs reflect price-relevant information even prior to the halt, with limit book imbalances decreasing and depth filling in during the halt around the eventual reopening NYSE price. However, these informational benefits come at a substantial cost, as both execution costs and volatility are extremely high on off-NYSE venues during NYSE halts.

Stock prices incorporate less news before negative events than positive events. Further, informed agents use less price aggressive (limit) orders before negative events and more price aggressive (market) orders before positive events (buy–sell asymmetry). Motivated by these patterns, we model the execution risk that informed agents impose on each other and relate the asymmetry to costly short selling. When investor base is narrow, security borrowing is difficult, or the magnitude of the event is small, buy–sell asymmetry is pronounced and price discovery before negative events is lower. Overall, we show that the strategies of informed traders influence the process of price formation in financial markets, as predicted by theory.

Many stock exchanges choose to reduce market transparency by allowing traders to hide some or all of their order size. We study the costs and benefits of order exposure and test hypotheses regarding hidden order usage using a sample of Euronext-Paris stocks, where hidden orders represent 44% of the sample order volume. Our results support the hypothesis that hidden orders are associated with a decreased probability of full execution and increased average time to completion, and fail to support the alternate hypothesis that order exposure causes defensive traders to withdraw from the market. However, exposing rather than hiding order size increases average execution costs. We assess the extent to which non-displayed size is truly hidden and document that the presence and magnitude of hidden orders can be predicted to a significant, but imperfect, degree based on observable order attributes, firm characteristics, and market conditions. Overall, the results indicate that the option to hide order size is valuable, in particular, to patient traders.

The trading of shares of the same firm in multiple markets has become common over the last 30 years, but there is little empirical evidence on the extent to which investors actively exploit multimarket environments. We introduce a volume-based measure of multimarket trading to address this question. Analyzing a large set of cross-listed firms, we find higher multimarket trading among markets with similar designs and strong enforcement of insider trading laws and for firms with higher institutional ownership. These findings are important for firms evaluating the benefits of cross listing and for markets competing for order flow.

Using European stock data from two different venues and time periods for which we can identify each trade's aggressor, we test the performance of the bulk volume classification (Easley et al. (2016); BVC) algorithm. BVC is data efficient, but may identify trade aggressors less accurately than “bulk” versions of traditional trade-level algorithms. BVC-estimated trade flow is the only algorithm related to proxies of informed trading, however. This is because traditional algorithms are designed to find individual trade aggressors, but we find that trade aggressor no longer captures information. Finally, we find that after calibrating BVC to trading characteristics in out-of-sample data, it is better able to detect information and to identify trade aggressors. In the new era of fast trading, sophisticated investors, and smart order execution, BVC appears to be the most versatile algorithm.