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Working Paper

Machine Learning and the Implementable Efficient Frontier

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?

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

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

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

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

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

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

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

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.