This study analyzes information production and trading behavior of banks with lending relationships. We combine trade-by-trade supervisory data and credit-registry data to examine banks' proprietary trading in borrower stocks around a large number of corporate events. We find that relationship...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

We propose a dynamic equilibrium model of asset prices and trading volume with heterogeneous agents facing fixed transactions costs. We show that even small fixed costs can give rise to large 'no-trade' regions for each agent's optimal trading policy and a significant illiquidity discount in...

This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have...

This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context...

Using over eight trillion observations of market data, we use a regression discontinuity design to analyze the effect of increasing the minimum price variation (MPV) for quoting equity securities in light of recent proposals to increase the MPV from $0.01 to $0.05. We show that a larger MPV...

We study the impact of model disagreement on the dynamics of asset prices, return volatility, and trade in the market. In our continuous-time framework, two investors have homogeneous preferences and equal access to information, but disagree about the length of the business cycle. We show that...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We show that firms in industries in which firm-specific stock price variation is larger use more external financing and allocate capital with greater precision in the sense that their marginal q ratios are closer to one. According to the Efficient Markets Hypothesis, greater firm-specific stock...

This paper explores environments in which either the revelation or diffusion of information, or its incorporation into stock prices, is gradual, and develops appropriate estimation techniques. This paper has implications both for event study methodology and for understanding the process by which...