The High-Frequency Factor Zoo (Job Market Paper)
Abstract: I construct a novel dataset of 224 high-frequency factor portfolios in order to study the cross-section of expected returns in a continuous-time setting. I estimate the continuous and semijump risk premia for each of these factors and find that jump and semijump risk are often priced and command a larger risk premia than continuous risk. Furthermore, there only a few clusters of factors, corresponding to less than a third of the zoo, with significant continuous and semijump risk premia. Additionally, I decompose cross-sectional variation in expected returns into variation from exposure to the continuous and jump factor risk. I find that the majority of cross-sectional variation comes from jump risk and that most stocks draw significant jump risk premia.
News and Asset Pricing: A High-Frequency Anatomy of the SDF
(with Tim Bollerslev) - R&R at Review of Financial Studies
Abstract: We rely on a unique set of high-frequency factors to robustly estimate an intraday Stochastic Discount Factor (SDF). Exploiting the precisely timed jumps in the estimated SDF together with real-time newswire data, we identify and precise the news that are priced. We find that news related to monetary policy and finance on average account for the largest portion of the variation in the SDF and the tangency portfolio risk premium, followed by news about international affairs and macroeconomic data. Reflecting investors changing economic concerns, we also uncover significant temporal variation in the relative importance of the news that matter. Relying on a standard mimicking portfolio approach, we further document marked differences in the way in which the news, and the compensation therefor, manifest in the "factor zoo.''
Intraday Market Return Predictability Culled from the Factor Zoo
(with Tim Bollerslev and Mathias Siggaard) - R&R at Management Science
Abstract: We provide strong empirical evidence for time-series predictability of the intraday return on the aggregate market portfolio based on lagged high-frequency cross-sectional returns from the factor zoo. Our results rely crucially on the use of modern Machine Learning techniques to regularize the predictive regressions and help tame the signals stemming from the zoo along with techniques from financial econometrics to differentiate between continuous and discontinuous price increments. Using the general prediction model for the implementation of simple trading strategies for a set of broad-based market ETFs results in sizeable out-of-sample Sharpe ratios and alphas after accounting for transaction costs. Further dissecting the results, we find that most of the superior performance may be traced to periods of high economic uncertainty and a few key factors related to tail risk and liquidity, pointing to slow-moving capital and the gradual incorporation of new information as the underlying mechanisms at work.