Automation and trading speed are increasingly important aspects of competition among financial markets. Yet we know little about how changing a market's automation and speed affects the cost of immediacy and price discovery, two key dimensions of market quality. At the end of 2006 the New York Stock Exchange introduced its Hybrid Market, increasing automation and reducing the execution time for market orders from 10 seconds to less than one second. We find that the change raises the cost of immediacy (bid-ask spreads) because of increased adverse selection and reduces the noise in prices, making prices more efficient.

We show that market-maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity-supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market-level and specialist firm-level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.ASSET MARKET LIQUIDITY VARIES considerably over time. This variation matters to market participants who worry about the cost of trading into or out of a desired position in a short period of time. Liquidity can affect asset prices, too. For example, investors may demand higher rates of return as compensation for holding illiquid assets and assets that are particularly sensitive to fluctuations in liquidity. However, despite the interest in aggregate liquidity from both of these angles, we know relatively little about exactly why market liquidity varies over time. Recent theoretical work by Gromb and Vayanos (2002) and Brunnermeier and Pedersen (2009), among others, postulates that limited market-maker capital can explain empirical features of asset market liquidity.

This paper examines whether investors care more about trading their exact quantity demands at some times than at others. Using a new data set of foreign-exchange transactions, I find that customers trade more precise quantities at quarter-end, as evidenced by less trade-size clustering. Customers trade more odd lots and fewer round lots, while the number of trades and total volume are not significantly changed. I also find that the price impact of order flow is greater when customers care more about trading precise quantities. This work sheds new light on trade-size clustering and offers a potential explanation for timeseries and cross-sectional variations in common liquidity measures.

Using a database of daily institutional trades, we document that a majority of short-term institutional trades lose money. In aggregate, over 23% of round-trip trades are held for less than three months, and the returns on these trades average -3.91% (non-annualized). These losses are pervasive across all types of stocks, with the lowest returns occurring in small stocks, value stocks, and low-momentum stocks. Short-term trades lose more in more volatile markets. Across funds, the worst short-term returns accrue to funds that do the most trading, and there is no evidence of persistent skill or disposition effect in shortterm institutional trades.