Tax Aware
Liquidity without Liquidation
August 28, 2026
This paper shows that investors may be able to access meaningful liquidity from tax-aware strategies while preserving the remaining portfolio’s tracking error, leverage, and pre-tax return potential. An adaptive withdrawal process may also better manage capital-gain realization than a fixed withdrawal schedule.
Tax Aware
The Tax Benefits of Pre-Tax Alpha
March 18, 2026
This paper shows that pre-tax alpha may enhance tax benefits in tax-aware long-short strategies by accelerating NAV growth and creating new positions that support loss realization. The result is meaningfully higher after-tax wealth—especially at longer horizons and higher levels of leverage.
ESG Investing
Climate Risk Pricing
December 30, 2025
This paper develops a macro-finance model of how transition and physical climate risks are priced in markets. It shows that brown assets can hedge climate risk when carbon taxes are too low, challenging the view that risk-based investing naturally supports climate goals.
ESG Investing
Can Sustainable Finance Save the Planet?
October 4, 2025
This paper asks whether sustainable finance can meaningfully mitigate climate change by raising polluters’ costs of capital. While theory implies a strong pricing of emissions, the evidence suggests markets currently price carbon far too weakly to drive a net-zero transition.
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.
Machine Learning
APT or “AIPT”? The Surprising Dominance of Large Factor Models
October 1, 2024
The authors introduce artificial intelligence pricing theory (AIPT), which conjectures that returns are driven by a large number of factors.
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.