We complement the conditional CAPM by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally 'learn' the long-level of factor loadings from the observation of realized returns. As a direct consequence of this assumption, conditional betas are...

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...

We show that product differentiation reduces the informativeness of a firm's stock price (or its peers' stock prices) about the value of its growth opportunities. This results in less efficient exercise of a firm's growth options when managers rely on information in stock prices for their...

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert’s behavior. It is well known that, in general, various reward functions can lead to the...

We develop a dynamic model to study the interaction between obfuscation and investor sophistication in retail financial markets. Taking into account different learning mechanisms within the investor population, we characterize the optimal timing of obfuscation for a profit-maximizing monopolist....

Ample empirical and experimental evidence documents that individuals place greater weight on information gained through personal experience -- a phenomenon that Tversky and Kahneman (1982) call availability bias. I embed this bias in an overlapping generations equilibrium model in which the...

How does private information get incorporated into option prices? To study this question, I develop a non-linear, noisy rational expectations equilibrium model with asymmetric information and a full menu of call and put options available for trading. The model allows for an arbitrary...

This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work...