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Working Paper
How Global is Predictability?
November 3, 2023
We show that asset pricing has a strong global component in the sense that a common global model has stronger predictability of stock returns than local models estimated in each country – even when the global model is estimated without the use of local data. Nevertheless, asset pricing has a small local component – in order to detect it, we develop a refined transfer learning model that gains power and precision by building off the global component.
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
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.
Journal Article
Enhanced Portfolio Optimization
March 2, 2020
We show how to identify the portfolios that cause problems in standard mean-variance optimization (MVO) and develop an enhanced portfolio optimization (EPO) method that addresses the problems. Applying EPO on several realistic datasets, we find significant gains relative to standard benchmarks.
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
Hedging Climate Change News
May 22, 2019
We propose and implement a procedure to dynamically hedge climate change risk and discuss multiple directions for future research on financial approaches to managing climate risk.
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.
Journal Article
A Data Science Solution to the Multiple-Testing Crisis in Financial Research
February 1, 2019
In this paper, we present a real example of how multiple testing information can be reported. We use that information to estimate the Deflated Sharpe Ratio of an investment strategy.