Simple regression tests that have power against the alternatives that. asset prices and expected future asset returns are excessively volatile are developed and performed for the foreign exchange and stock markets. These tests have a number of advantages over alternative, variance hounds...

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,...

This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. We find that most relevant news, both idiosyncratic and systematic, lead quickly to price jumps, as market...

We present two valuation models that we use to account for the annual data on price per share and dividends per share for the CRSP Value-Weighted Index from 1929-2023. We show that it is a simple matter to account for these data based purely on a model of variation in the expected ratio of...

Despite positive and significant earnings announcement premia, we find that institutional investors reduce their exposure to stocks before earnings announcements. A novel result on the sensitivity of flows to individual stock returns provides a potential explanation. We show that extreme...

Portfolio optimization focuses on risk and return prediction, yet implementation costs critically matter. Predicting trading costs is challenging because costs depend on trade size and trader identity, thus impeding a generic solution. We focus on a component of trading costs that applies...

We study the optimal design of corporate tax policy in a textbook life-cycle model featuring two key deviations: (i) firms are imperfectly competitive and (ii) households save by purchasing equity shares in a stock market. In this simple environment, the financial wealth of savers is equal to...