Machine Learning
Understanding The Virtue of Complexity
July 10, 2025
We respond to recent academic challenges to aspects of the “virtue of complexity” described in our prior research. We provide detailed discussions of how complex models learn in small samples, the roles of “nominal” and “effective” complexity, the unique effects of implicit regularization, and the importance of limits to learning. We then present new empirical and theoretical analyses that expand on KMZ. Finally, we introduce and demonstrate the virtue of ensemble complexity.
Tax Aware
Are Completion Portfolios Effective for Managing Concentrated Stock Risk?
May 14, 2025
This paper investigates the most effective ways to manage the risk of concentrated stock positions.
Tax Aware
A Brief Guide to the Mathematics and Taxation of Charitable Remainder Unitrusts
March 18, 2025
We provide a practical guide for financial planners and wealth management professionals on charitable remainder unitrusts (CRUTs).
Tax Aware
Combining Charitable Remainder Unitrusts and Tax-Aware Strategies to Diversify Low-Basis Stock
March 18, 2025
We show how combining charitable remainder unitrusts (CRUTs) with tax-aware strategies can help investors diversify low-basis stock and enhance after-tax wealth accumulation. Our findings suggest that investors and their advisors should integrate philanthropy and investment management to optimize wealth preservation and charitable impact.
ESG Investing
In Search of the True Greenium
March 1, 2024
The greenium (the expected return of green securities relative to brown) is a central impact measure for ESG investors. We propose a robust green score combined with forward-looking expected returns, yielding a more precisely estimated annual equity greenium.
Machine Learning
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.
Fixed Income
Corporate Bond Factors: Replication Failures and a New Framework
October 5, 2023
We demonstrate that the literature on corporate bond factors suffers from replication failures, inconsistent methodological choices, and the lack of a common error-free dataset. Going beyond identifying this replication crisis, we create a clean database of corporate bond returns where outliers are analyzed individually and propose a robust factor construction.
Machine Learning
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.
ESG Investing
Carbon Pricing versus Green Finance
March 14, 2023
We show that green finance should not be used if the carbon price equals its social cost. However, with too low carbon prices, green finance can implement the social optimum if the cost of capital can be controlled and there are no stranded assets. We show explicitly how to "translate" a carbon tax into green finance terms, highlight how green finance should depend on scope 1, 2, and 3 emissions, present its limitations, and illustrate the predictions empirically.
ESG Investing
Is Capital Structure Irrelevant with ESG Investors?
September 23, 2022
We examine whether capital structure is irrelevant for enterprise value and investment when investors care about ESG issues, which we denote “ESG-Modigliani-Miller” (ESG-MM). Theoretically, we show that ESG-MM holds if ESG is additive and markets are perfect. Empirically, we provide evidence of failure of ESG-MM, implying that firms and governments can exploit non-additive ESG or segmented markets.