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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
Working Paper
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.
Working Paper
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.
Working Paper
Predicting Returns with Text Data
December 19, 2019
We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns.
Journal Article
Can Machines "Learn" Finance?
June 7, 2019
Can Machines “Learn” Finance?” was named the winner of the 2020 Harry M. Markowitz Award. Machine learning for asset management faces a unique set of challenges that differ markedly from other domains where machine learning has excelled. We discuss a variety of beneficial use cases and potential pitfalls for machine learning in asset management, and emphasize the importance of economic theory and human expertise for achieving success through financial machine learning.
Working Paper
Autoencoder Asset Pricing Models
May 22, 2019
We propose a new latent factor conditional asset pricing model, which delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models.