We study the determinants of US dollar demand across market participants and traded instruments using survey-based exchange rate and macroeconomic expectations. Leveraging granular foreign exchange trading data, we show that forward-looking expectations accurately predict both currency returns...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

We study the determinants of US dollar demand across market participants and traded instruments using survey-based exchange rate and macroeconomic expectations. Leveraging granular FX trading data and forward looking expectations, we present three results. First, currency investors increase...

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns...

We introduce a simple yet powerful method for enhancing mutual fund performance prediction by combining individual predictors into a composite predictor. This composite approach integrates information from 19 well-established return-based and portfolio holdings-based predictors from the...

We uncover momentum and reversal patterns in half-day option returns that persist for up to at least 20 business days, with economic magnitudes of 0.22% to 0.45% per half-day. Specifically, returns show strong momentum within the same period (e.g., intraday-to-intraday) but reverse sharply...

We study the green bond premium, defined as the yield differential between green and matched conventional bonds in the secondary market. Existing estimates vary widely, raising questions about their robustness. We address this by estimating the premium across more than 500,000 empirical designs...

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 analyze a market-wide panel dataset on retail options trading from India, a market with an 80% share in option contracts traded worldwide. Retail traders both concentrated in and dominate index options trading. They exhibit short-term speculative behavior with significant day trading, short-...