Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach...

It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and...

This article studies long-horizon dynamic asset allocation strategies with recursive parameter updating. The parameter estimates for the regime-switching dynamics vary as more and more datapoints are observed and the sample size increases. In such a setting, the globally optimal portfolio...

The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and...

Prices and investors' behavior are heavily influenced by risk aversion. As it is unobservable, estimating risk aversion has been challenging for a long time. This paper proposes using a Machine Learning approach (a combination of Autoencoder and Long-Short Term Memory) to estimate the...

For the purpose of stock return prediction, we propose LASSO methods augmented by further penalties related to differences in coefficient estimates at t and t+1. The economic motivation is that the coefficient for a characteristic should not change too much from t to t+1, i.e., the market's...

This article studies long-horizon dynamic asset allocation strategies with recursive parameter updating. The parameter estimates for the regime-switching dynamics vary as more and more datapoints are observed and the sample size increases. In such a setting, the globally optimal portfolio...

The latest development in empirical Asset Pricing is the employment of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. I review and...

In this paper we introduce a new class of approaches to empirical asset pricing research, namely LASSO methods augmented by further penalties related to differences in adjacent coefficient estimates (at t and t+1) for a given characteristic. The economic motivation for this is that the...