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Working Paper

Principal Portfolios

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.

White Paper

Enhanced Portfolio Optimization

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

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns.

Journal Article

Can Machines "Learn" Finance?

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

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.

Working Paper

Hedging Climate Change News

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.

Journal Article

A Data Science Solution to the Multiple-Testing Crisis in Financial Research

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.

Journal Article

Empirical Asset Pricing via Machine Learning

We show how the field of machine learning can be used to empirically investigate asset premia including momentum, liquidity, and volatility.