[When a market order arrives, the NYSE specialist can offer a price one tick better than the limit orders on the book and trade for his own account. Alternatively, the specialist can "stop" the market order, which means he guarantees execution at the current quote but provides the possibility of price improvement. My model shows that specialists can use stops to sample the future order flow before making a commitment to trade. I present empirical evidence that both stops and immediate price improvement impose adverse selection costs on limit order traders.]

When a market order arrives, the NYSE specialist can offer a price one tick better than the limit orders on the book and trade for his own account. Alternatively, the specialist can "stop" the market order, which means he guarantees execution at the current quote but provides the possibility of price improvement. My model shows that specialists can use stops to sample the future order flow before making a commitment to trade. I present empirical evidence that both stops and immediate price improvement impose adverse selection costs on limit order traders.

Price improvement is the difference between the execution price of an order and the quoted bid or ask when the order was submitted. We show that expected price improvement falls off dramatically as the size of the order approaches the quoted depth, and becomes negative for larger orders. This is particularly important for small firms because the quoted depths are low. Using quoted spreads and depths and our estimate of expected price improvement, we show that trading strategies that attempt to exploit the weekly predictability of small-firm returns would be swamped by transaction costs.

[Price improvement is the difference between the execution price of an order and the quoted bid or ask when the order was submitted. We show that expected price improvement falls off dramatically as the size of the order approaches the quoted depth, and becomes negative for larger orders. This is particularly important for small firms because the quoted depths are low. Using quoted spreads and depths and our estimate of expected price improvement, we show that trading strategies that attempt to exploit the weekly predictability of small-firm returns would be swamped by transaction costs.]

We analyze contemporaneous and predictive relations between credit ratings and measures of equity market liquidity and find that common measures of adverse selection, which reflect a portion of the uncertainty about future firm value, are larger when credit ratings are poorer. We also show that future rating changes can be predicted using current levels of adverse selection. Collectively, our results validate widely used microstructure measures of adverse selection and offer new insights into the value of credit ratings and the specific nature of the information they contain.

We use a robust regression estimator to analyze the risk premia on size and book‐to‐market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.

We use a robust regression estimator to analyze the risk premia on size and book-to-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1% most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.

The authors use a robust regression estimator to analyze the risk premia on size and book-to-market. They find that the risk premium on size that was estimated by Eugene F. Fama and Kenneth R. French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. The authors also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the sixteen months with the most extreme coefficients. They argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.

This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. The authors document two potential problems with quote-based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. The authors then propose and test relatively simple procedures for improving trade classifications.

[For a sample of NYSE firms, we show that wide spreads are accompanied by low depths, and that spreads widen and depths fall in response to higher volume. Spreads widen and depths fall in anticipation of earnings announcements; these effects are more pronounced for announcements with larger subsequent price changes. Spreads are also wider following earnings announcements, but this effect dissipates quickly after controlling for volume. Collectively, our results suggest liquidity providers are sensitive to changes in information asymmetry risk and use both spreads and depths to actively manage this risk.]