We propose a dynamic econometric microstructure model of trading, and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity, market depth, and order flow. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for 16 actively traded stocks over 15 years of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades, which we use to construct forecasts of the probability of information-based trade (PIN). These forecasts are used in turn to forecast market liquidity as measured by bid-ask spreads and the price impact of orders. We observe that PINs vary across assets and over time, and most importantly that they are correlated across assets. Our analysis shows that one principal component explains much of the daily variation in PINs and that this systemic liquidity factor may be important for asset pricing. We also find that PINs tend to rise before earnings announcement days and decline afterwards. Copyright The Author 2008., Oxford University Press.
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Volume (Year): 6 (2008) Issue (Month): 2 (Spring) Pages: 171-207 Download reference. The following formats are available: HTML
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Handle: RePEc:oup:jfinec:v:6:y:2008:i:2:p:171-207
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