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Alternative Thinking

Can Machines Build Better Stock Portfolios?

In the second issue of our 2024 Alternative Thinking series, we showed that machine learning techniques can be used to help improve market timing strategies. In this issue, we extend these concepts to constructing stock selection strategies following a similar framework. Our results indicate more complex models utilizing machine learning techniques yield performance improvements relative to a simple, linear approach in the range of 50-100%, suggesting that machine learning can help to build better stock selection portfolios.

Journal Article

CIO Perspectives: An Interview with Cliff Asness

In a wide ranging interview, AQR managing principal Cliff Asness discusses many aspects of AQR’s investment philosophy and approach from the perspective of a CIO – how we adapt our process to changing market conditions, how we think about adding innovative technology such as machine learning to our process, and more.

Alternative Thinking

Can Machines Time Markets? The Virtue of Complexity in Return Prediction

Common wisdom has suggested that small, simple models are best suited for market timing applications, given finance’s “small data” constraint and naturally low predictability. However, we show that complex models better identify true nonlinear relationships and therefore produce better market timing strategy performance. We validate this "virtue of complexity" result in three practical market timing applications.

Journal Article

The Virtue of Complexity in Return Prediction

Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

Working Paper

How Global is Predictability?

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.

Working Paper

Financial Machine Learning

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.

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

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.

Journal Article

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.