We utilize Bayesian model averaging to estimate a stochastic discount factor (SDF) for single-stock options. A Bayesian model averaging SDF outperforms reduced-form benchmark models in-sample and out-of-sample in pricing option return anomalies and portfolios. We document that the SDF is dense...

This paper investigates how investors' abnormal attention affects the cross-section of cryptocurrency returns in the period from 2018 to 2022. We capture abnormal attention using the (log) number of Twitter posts on individual cryptocurrencies on the current day minus a 30-day average. Our...

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP)...

Certain institutional features of 401(k) plans can create conflicts of interest between plan participants and financial advisors that advise them. We study one such conflict: when advisors are affiliated with the plan's recordkeeper. Using a large dataset of 401(k) plans, we find that affiliated...

This study provides evidence on the performance and labor market trajectories of sell-side equity analysts with prior experience or education in accounting. Analysts with work experience in accounting issue both more accurate earnings per share (EPS) forecasts and more profitable sell...

I provide the first systematic analysis of collateral choices in one of the main short-term funding markets, the repurchase agreement (repo) market. In repos, long-term bonds serve as collateral connecting short-term and long-term funding markets. In general collateral repos, banks can choose...

We investigate whether eponymous hedge funds-those named after their founder/manager-signal managerial ability or ethical behavior. While such funds do not outperform non-eponymous peers, they exhibit lower operational and fraud risks. Survey evidence supports these findings. Eponymous funds...

We predict earnings for forecast horizons of up to five years by using the entire set of Compustat financial statement data as input and providing it to state-of-the-art machine learning models capable of approximating arbitrary functional forms. Our approach improves prediction one year ahead...

We predict earnings for forecast horizons of up to five years by using the entire set of Compustat financial statement data as input and providing it to state-of-the-art machine learning models capable of approximating arbitrary functional forms. Our approach improves prediction one year ahead...

We investigate whether eponymous hedge funds-those named after their founder/manager-signal managerial ability or ethical behavior. While such funds do not outperform non-eponymous peers, they exhibit lower operational and fraud risks. Survey evidence supports these findings. Eponymous funds...