Journal Article
Empirical Asset Pricing via Machine Learning
October 17, 2018
We show how the field of machine learning can be used to empirically investigate asset premia including momentum, liquidity, and volatility.
Read moreJournal Article
Can Machines "Learn" Finance?
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.
Read moreWorking Paper
Hedging Climate Change News
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.
Read moreWorking Paper
Autoencoder Asset Pricing Models
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.
Read moreJournal Article
A Data Science Solution to the Multiple-Testing Crisis in Financial Research
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.
Read moreThis information is for informational purposes only and not intended to, and does not relate specifically to any investment strategy or product that AQR offers. It is being provided merely to provide a framework to assist in the implementation of an investor’s own analysis and an investor’s own view on the topic discussed herein.
AQR Capital Management, LLC, (“AQR”) provides links to third-party websites only as a convenience, and the inclusion of such links does not imply any endorsement, approval, investigation, verification or monitoring by us of any content or information contained within or accessible from the linked sites. If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which AQR.com has no control. In no event will AQR be responsible for any information or content within the linked sites or your use of the linked sites.