Peers' valuation matters for firms' investment: a one standard deviation increase in peers' valuation is associated with a 5.9% increase in corporate investment. This association is stronger when a firm's stock price informativeness is lower or when its managers appear less informed. Also, the sensitivity of a firm's investment to its stock price is lower when its peers' stock price informativeness is higher or when demands for its products and its peers' products are more correlated. Furthermore, the sensitivity of firms' investment to their peers' valuation drops significantly after going public. These findings are uniquely predicted by a model in which managers learn information from their peers' valuation.

Liquidity providers often learn information about an asset from prices of other assets. We show that this generates a self-reinforcing positive relationship between price informativeness and liquidity. This relationship causes liquidity spillovers and is a source of fragility: a small drop in the liquidity of one asset can, through a feedback loop, result in a very large drop in market liquidity and price informativeness (a liquidity crash). This feedback loop provides a new explanation for comovements in liquidity and liquidity dry-ups. It also generates multiple equilibria.

[Cross-listed firms in the United States have a higher investment-to-price sensitivity than do firms that never cross-list. This difference is strong, does not exist prior to the cross-listing date, and does not vanish afterward. Moreover, it does not appear to be primarily driven by improvements in governance, disclosure, and access to capital associated with a U.S. crosslisting. Instead, we argue that a cross-listing enhances managers' reliance on stock prices because it makes stock prices more informative to them. Consistent with this explanation, U. S. cross-listings that are more likely to strengthen the informativeness of stock prices for managers feature a higher investment-to-price sensitivity.]

[Competition among trading platforms has considerably reduced trading fees in stock markets. We show that this evolution is not necessarily beneficial to investors. Although they increase gains from trade when a trade happens, lower trading costs can induce investors to post limit orders with a smaller execution probability. In this case, gains from trade are realized less frequently and investors can be worse off. Our model has testable implications for the effects of trading fees and their breakdown between liquidity suppliers and liquidity demanders on limit order fill rates and bid-ask spreads.]

[We develop a model in which limit order traders possess volatility information. We show that in this case the size of the bid-ask spread is informative about future volatility. Moreover, if volatility information is in part private, we establish that (i) the size of the bid-ask spread and (ii) its informativeness about future volatility should change in the same direction when limit order traders' identifiers stop being disclosed. We test these predictions using data from the Paris Bourse. As expected, we find that the average quoted spread and its informativeness are significantly smaller when limit order traders' identifiers are concealed. These findings suggest that the limit order book is a channel for volatility information.]

We develop a dynamic model of a limit order market populated by strategic liquidity traders of varying impatience. In equilibrium, patient traders tend to submit limit orders, whereas impatient traders submit market orders. Two variables are the key determinants of the limit order book dynamics in equilibrium: the proportion of patient traders and the order arrival rate. We offer several testable implications for various market quality measures such as spread, trading frequency, market resiliency, and time to execution for limit orders. Finally, we show the effect of imposing a minimal price variation on these measures.

This article presents a model of information monitoring and market making in a dealership market. We model how intensively dealers monitor public information to avoid being picked off by professional day traders when monitoring is costly. Price competition among dealers is hampered by their incentives to share monitoring costs. The risk of being picked off by the day traders makes dealers more competitive. The interaction between these effects determines whether a firm quote rule improves trading costs and price discovery. Our empirical results support the prediction that professional day traders prefer stocks with small spreads, but offer less support for the prediction that their trading leads to wider spreads.

We analyze price competition between dealers in a security market where the bidding process is sequential. The model provides an interpretation for the evolution of the best ask and bid prices, in between transactions. We find that convergence to the competitive ask and bid prices can take time. The speed of convergence is determined by the frequency with which dealers check their offers and by the tick size. This creates a relationship between the expected trading cost and the timing of offers posted by the dealers. We also find that a zero minimum price variation never minimizes the expected trading cost. Finally, we study the role of time priority. Journal of Economic Literature Classification Numbers: D43, G10.