Responding to a September 2002 regulatory enforcement, the Island electronic communications network stopped displaying its limit order book in the three most active exchange-traded funds (ETFs) where it was the dominant venue. Island's share of trading activity and price discovery fell, fragmenting the market. ETF prices adjust more slowly when Island goes dark, and there is substantial price discovery movement from ETFs to the futures market. Trading costs increase on Island and decrease off Island, with higher trading costs overall. When Island later redisplays its orders, market quality improves, with transparency and the reduction in fragmentation both playing important roles.

We examine the effects of trading after hours on the amount and timing of price discovery over the 24-hour day. A high volume of liquidity trade facilitates price discovery. Thus prices are more efficient and more information is revealed per hour during the trading day than after hours. However, the low trading volume after hours generates significant, albeit inefficient, price discovery. Individual trades contain more information after hours than during the day. Because information asymmetry declines over the day, price changes are larger, reflect more private information, and are less noisy before the open than after the close.

We examine the effects of trading after hours on the amount and timing of price discovery over the 24-hour day. A high volume of liquidity trade facilitates price discovery. Thus prices are more efficient and more information is revealed per hour during the trading day than after hours. However, the low trading volume after hours generates significant, albeit inefficient, price discovery. Individual trades contain more information after hours than during the day. Because information asymmetry declines over the day, price changes are larger, reflect more private information, and are less noisy before the open than after the close.

This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group’s past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a “chain” of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.

Empirical studies linking liquidity provision to asset prices follow naturally from inventory models. Liquidity suppliers and market markers profit from providing immediacy to less patient investors, but have limited inventory-carrying and risk-bearing capacity. Similarly, limits to arbitrage arguments rely on certain market participants accommodating buying or selling pressure. These liquidity suppliers/arbitrageurs are willing to accommodate trades—and, therefore, hold suboptimal portfolios—only if they are able to buy (sell) at a discount (premium) relative to future prices. Thus, large liquidity-supplier inventories should coincide with large buying or selling pressure, which causes price movements that subsequently reverse themselves. By identifying and studying the inventories of traders who are central to the trading process and whose primary roll is to provide liquidity—New York Stock Exchange (NYSE) market

This paper examines liquidity externalities by analyzing trading costs after hours. There is less than 1/20 as many trades per unit time after hours as during the trading day. The reduced trading activity results in substantially higher trading costs: quoted and effective spreads are three to four times larger than during the trading day. The higher spreads reflect greater adverse selection and order persistence, but not higher dealer profits. Because liquidity provision remains competitive after hours, the greater adverse selection and higher trading costs provide a direct measure of the magnitude of the liquidity externalities generated during the trading day.

We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.

We show that the consolidation of orders is important for producing efficient prices, especially during times of high liquidity demand. The NYSE's centralized opening call market performs better than Nasdaq's decentralized opening process on typical trading days. The NYSE is much better than Nasdaq on witching days when index arbitrage activity subjects S&P 500 stocks to large, predictable, and mostly informationlessorder flow around quarterly futures contract expirations. Nasdaq opening price efficiency improves to NYSE levels once Nasdaq initiates a consolidated opening call in November 2004, but prices on the decentralized Nasdaq remain less efficient at other times of day.