Alternative Thinking
November 7, 2024
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.
Alternative Thinking
May 6, 2024
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
March 1, 2024
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
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.
Working Paper
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.
Journal Article
June 7, 2019
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
August 18, 2022
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
July 7, 2020
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
March 2, 2020
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
December 19, 2019
We introduce a new text-mining methodology that
extracts sentiment information from news articles to predict asset returns.
Journal Article
October 17, 2018
We show how the field of machine learning can be
used to empirically investigate asset premia including momentum, liquidity, and
volatility.
Working Paper
May 22, 2019
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.
Working Paper
May 22, 2019
We propose a new latent factor conditional asset
pricing model, which delivers out-of-sample pricing errors that are far smaller (and
generally insignificant) compared to other leading factor models.
Journal Article
February 1, 2019
In this paper, we present a real example of how
multiple testing information can be reported. We use that information to estimate
the Deflated Sharpe Ratio of an investment strategy.
