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Journal Article
Business News and Business Cycles
August 9, 2024
We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns.
Journal Article
The Virtue of Complexity in Return Prediction
March 1, 2024
Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.
Working Paper
Financial Machine Learning
August 1, 2023
In this survey the nascent literature on machine learning in financial markets, we highlight the best examples of what this line of research has to offer and recommend promising directions for future research.
Working Paper
Machine Learning and the Implementable Efficient Frontier
August 18, 2022
We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning
Journal Article
Is There a Replication Crisis in Finance?
March 5, 2021
Several papers argue that financial economics faces a replication crisis because many studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions, including finding the majority of asset pricing factors can be replicated.
Working Paper
Modeling Corporate Bond Returns
December 2, 2020
We propose a new conditional factor model for corporate bond returns with four factors and time-varying factor loadings instrumented by observable bond characteristics. We find our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin.
Working Paper
Climate Finance
October 29, 2020
The paper reviews the literature studying interactions between climate change and financial markets, including various approaches to incorporating climate risk in macro-finance models as well as the empirical literature that explores the pricing of climate risks across several asset classes.
Journal Article
Principal Portfolios
July 7, 2020
We propose a new asset-pricing framework in which all securities’ signals are used to predict each individual return. While the literature focuses on each security’s own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross-predictability.
Working Paper
Understanding Momentum and Reversals
June 9, 2020
Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation.
Working Paper
Equity Term Structures without Dividend Strips Data
March 12, 2020
We use a large cross-section of equity returns to estimate a rich affine model of equity prices, dividends, returns and their dynamics.. The new term structure data generated by our model represent new empirical moments that can be used to guide and evaluate asset pricing models.