Almost all limit orders are canceled. We examine two economic channels that can motivate cancellations: reductions in the expected profit at execution, and reductions in the probability of execution. An order-level analysis shows that changes in depth at the best bid and offer prices, as well as changes in the order queue position, influence cancellation in a way consistent with the former channel, that market makers monitor the expected profit at execution of each limit order. Although buy-side investors use passive orders extensively, our findings indicate that limit order cancellations on aggregate are best understood through models of liquidity provision.

The effective bid-ask spread measured relative to the spread midpoint overstates the trueeffective bid-ask spread in markets with discrete prices and elastic liquidity demand. Theaverage bias is 13%–18% for S&P 500 stocks in general, depending on the estimator usedas benchmark, and up to 97% for low-priced stocks. Cross-sectional bias variation acrossstocks, trading venues, and investor groups can influence research inference. The use of themidpoint also undermines liquidity timing and trading performance evaluations, and canlead non-sophisticated investors to overpay for liquidity. To overcome these problems, thepaper proposes new estimators of the effective bid-ask spread.

To improve the efficiency of the closing price, many equity exchanges apply volatility extensions to their closing call auctions (CCAs). If an imminent auction execution implies a large price change, the order submission period is extended to let traders reconsider their orders. This paper uses the introduction of closing auction volatility extensions at NASDAQ Nordic to provide the first analysis of the effects of such mechanisms. We find that the volatility extensions reduce transitory volatility and deter price manipulation at the close. Consistent with increased trust in the mechanism, the CCA attracts higher volumes after the change.

How does information get revealed in decentralized markets? We test several hypotheses inspired by recent dealer-network theory. To do so we construct an empirical map of information revelation where two dealers are connected based on the synchronicity of their quote changes. The tests, based on EUR/CHF quote data including the 2015 crash, largely support theory: Strongly connected (i.e., central) dealers are more informed. Connections are weaker when there is less to be learned. The crash serves to identify how a network forms when dealers are transitioned from no-learning to learning, that is, from a fixed to a floating rate.

We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs’ trading performance. HFTs that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for various strategies including market making and cross-market arbitrage. We find empirical support for many predictions regarding relative latency competition.

We develop a structural model for the price formation and liquidity supply of an asset. Ourmodel facilitates decompositions of both the bid–ask spread and the return variance intocomponents related to adverse selection, inventory, and order processing costs. Furthermore,the model shows how the fragmentation of trading volume across trading venues influencesinventory pressure and price discovery. We use the model to analyze intraday price formationfor gold futures traded at the Shanghai Futures Exchange. We find that order processing costsexplain about 50% of the futures bid–ask spread, whereas the remaining 50% is equally due toasymmetric information and to inventory costs. About a third of the variance in futures returnsis attributable to microstructure noise. Trading at the spot market has a significant influence onfutures price discovery, but only a limited impact on the futures bid–ask spread.

We exploit an optional colocation upgrade at NASDAQ OMX Stockholm to assess how speed affects market liquidity. Liquidity improves for the overall market and even for noncolocated trading entities. We find that the upgrade is pursued mainly by participants who engage in market making. Those that upgrade use their enhanced speed to reduce their exposure to adverse selection and to relax their inventory constraints. In particular, the upgraded trading entities remain competitive at the best bid and offer even when their inventories are in their top decile. Our results suggest that increasing the speed of market making participants benefits market liquidity.

We investigate the effects from the introduction of a closing call auction (CCA) at the index futures market. Limit order book models, where trader patience determines trading strategies, predict that a CCA increases trader patience and, hence, improves closing price accuracy and end‐of‐day liquidity. We find that the introduction leads to increased trader patience, improved futures closing price accuracy, unaffected tightness and resiliency, and decreased depth. Decreased depth is likely due to less order fishing activity. With the CCA, opportunistic patient traders’ posting of limit orders deep in the order book, to profit from impatient traders, is no longer feasible.

We study order aggressiveness of market-making high-frequency traders (MM-HFTs), opportunistic HFTs (Opp-HFTs), and non-HFTs. We find that MM-HFTs follow their own group's previous order submissions more than they follow other traders’ orders. Opp-HFTs and non-HFTs tend to split market orders into small portions submitted in sequence. HFTs submit more (less) aggressive orders when the same-side (opposite-side) depth is large, and supply liquidity when the bid–ask spread is wide. Thus, HFTs adhere strongly to the tradeoff between waiting cost and the cost of immediate execution. Non-HFTs care less about this tradeoff, but react somewhat stronger than HFTs to volatility.

This paper implements a conditional version of the liquidity adjusted CAPM (LCAPM). The conditional LCAPM allows for a time-varying decomposition of the total illiquidity premium into a level component and three risk components. The estimated average annual total illiquidity premium for US stocks 1927–2010 is 1.74–2.08%, which is substantially lower than in most previous studies. The contributions from illiquidity level and illiquidity risk are 1.25–1.28% and 0.46–0.83%, respectively. Of the three illiquidity risk components, risk related to the hedging of wealth shocks is the most important, while commonality risk is the least important. The illiquidity premia are clearly time-varying, with peaks in downturns and crises, but with no general tendency to decrease over time. The level premium and the risk premium are significantly positively correlated, at around 0.35; indicating that in periods of turbulence both illiquidity cost and illiquidity risk premia tend to be high.