Research

Publications

Machine Learning and the Implementable Efficient Frontier (2024)

with Bryan Kelly, Semyon Malamud, and Lasse H. Pedersen

Conditionally accepted, Review of Financial Studies
Code

Abstract: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. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of "economic feature importance."

Is There a Replication Crisis in Finance? (2023)

with Bryan Kelly and Lasse H. Pedersen

Journal of Finance,
Winner of the Dimensional Fund Advisor Prize distinguished paper
Code: Learn how to work with global return and accounting data and how to use empirical Bayes to estimate a hierarchical model of global equity factor returns
Data: Stock returns, firm characteristics, and equity factors from 93 different countries

Abstract:Several papers argue that financial economics faces a replication crisis because the majority of 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, which leads to different conclusions. The majority of asset pricing factors: (1) can be replicated, (2) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio, (3) work out of sample in a new large data set covering 93 countries, and (4) have evidence that is strengthened (not weakened) by the large number of observed factors.

Working Papers

The Power of the Common Task Framework (2025)

with Oliver Hellum, Bryan Kelly, and Lasse H. Pedersen

Abstract:

The “Common Task Framework” (CTF) is a collaborative and competitive process in which researchers solve a task using shared data, a predefined success metric, and a leaderboard. Using an economic model, we show that the CTF incentivizes effort, increases innovation, and curbs misrepresentation by reducing research costs and improving comparability. Historical examples from computer science underscore its effectiveness. To demonstrate its broader applicability, we propose a CTF for financial economics: a platform open to all researchers designed to identify the pricing kernel and systematically evaluate asset pricing models, from traditional factor-based approaches to modern machine learning techniques.

In Search of the True Greenium (2024)

with Marc Eskildsen, Markus Ibert, and Lasse H. Pedersen

R&R, Journal of Financial Economics
Code: Implied cost of capital estimates from around the world

Abstract:The greenium (the expected return of green securities relative to brown) is a central impact measure for ESG investors. Replicating the literature’s wide range of greenium estimates based on realized returns, we find that these are not robust to changing the greenness measure or time period. Instead, we propose a robust green score combined with forward-looking expected returns, yielding a more precisely estimated annual equity greenium of -25 basis points per standard deviation increase in greenness. The greenium is more negative in greener countries and over time. Finally, we provide greeniums for corporate bonds, weighted-average costs of capital, and sovereign bonds.

Subjective Risk and Return (2024)

Abstract:
Traditional asset pricing models like the CAPM explain realized returns worse than newer asset pricing models like Fama-French-5, but why? I show that traditional models explain subjective risk and return expectations well but also predict return-subtracting mispricing. Newer models, by contrast, explain subjective risk and return expectations poorly but predict return-enhancing mispricing. These results imply that the CAPM is a good model of risk but fails to explain realized returns because risk is correlated with mispricing. I explain this disconnect by a model in which all the CAPM assumptions hold, except that some investors have an optimism bias.

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