This article presents a new methodology for testing economic restrictions on the price schedules offered in a limit order book that are based on (i) break-even conditions for marginal limit orders and (ii) rational updating conditions for order book revisions over time. Using order flow data from the Stockholm Stock Exchange, I find strong evidence of insufficient depth in the limit order books relative to the theoretical predictions. An extended model, which allows the model parameters to depend on market conditions, captures some of the systematic variation in the observed order book depth.

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.

This article presents a new methodology for testing economic restrictions on the price schedules offered in a limit order book that are based on (i) break-even conditions for marginal limit orders and (ii) rational updating conditions for order book revisions over time. Using order flow data from the Stockholm Stock Exchange, I find strong evidence of insufficient depth in the limit order books relative to the theoretical predictions. An extended model, which allows the model parameters to depend on market conditions, captures some of the systematic variation in the observed order book depth.

We provide empirical restrictions of a model of optimal order submissions in a limit order market. A trader's optimal order submission depends on the trader's valuation for the asset and the trade-offs between order prices, execution probabilities and picking off risks. The optimal order submission strategy is a monotone function of a trader's valuation for the asset. We test the monotonicity restriction in a sample of order submissions and their realized outcomes from the Stockholm Stock Exchange. We do not reject the monotonicity restriction for buy orders or sell orders considered separately, but reject the monotonicity restriction for buy and sell orders considered jointly.

Is liquidity better when a trade counterparty’s brokerage firm is unknown (anonymous) or known (transparent)? We examine a quasinatural experiment where some firms switched from transparent to anonymous trading and then, 1 year later, switched back. Our results for inside spread, price impact, and limit order book depth suggest that liquidity improves when anonymous post-trade reporting is introduced and liquidity worsens when anonymous post-trade reporting is reversed.

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 provide empirical restrictions of a model of optimal order submissions in a limit order market. A trader's optimal order submission depends on the trader's valuation for the asset and the trade-offs between order prices, execution probabilities and picking off risks. The optimal order submission strategy is a monotone function of a trader's valuation for the asset. We test the monotonicity restriction in a sample of order submissions and their realized outcomes from the Stockholm Stock Exchange. We do not reject the monotonicity restriction for buy orders or sell orders considered separately, but reject the monotonicity restriction for buy and sell orders considered jointly.

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.

We present a method to estimate the gains from trade in limit‐order markets and provide empirical evidence that the limit‐order market is a good market design. Using observations on order submissions and execution and cancellation histories, we estimate both the distribution of traders' unobserved valuations for the stock and latent trader arrival rates. We use the resulting estimates to compute the current gains from trade, the gains from trade in a perfectly liquid market, and the gains from trade with a monopoly liquidity supplier. The current gains are 90% of the maximum gains and 150% of the monopolist gains.