Machine Learning

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Machine Learning and Investing: Forecasting Fundamentals w/ Ensembles

By |2020-05-13T10:20:28-04:00May 13th, 2020|Research Insights, Guest Posts, Academic Research Insight, Machine Learning|

Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. The key assumption behind this approach is that past fundamentals proxy for elements of risk and/or systematic mispricing. However, what if we could [...]

Forecasting US Equity Market Returns with Machine Learning

By |2020-01-06T14:09:24-05:00January 7th, 2020|Research Insights, Machine Learning, Tactical Asset Allocation Research|

Shiller's CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. [...]

Alpha Architect Weekly Recap: Editorials on Machine Learning

By |2018-12-21T10:24:05-05:00December 21st, 2018|Research Insights, Machine Learning, Weekly Research Recap Videos|

You can watch the video via this link: This week Ryan and I discuss two editorials on machine learning and its impact and use within Research. The first paper is an Editorial by [...]

Machine Learning Classification Methods and Factor Investing

By |2018-12-21T11:14:03-05:00December 21st, 2018|Research Insights, Machine Learning, Momentum Investing Research|

In the last post in our machine learning series, we showed how nonlinear regression algos might improve regression forecasting relative to plain vanilla linear regression (i.e., when underlying reality is nonlinear with complex interactions). In this [...]

A Protocol to Prevent “Quants Gone Wild”

By |2018-12-18T10:16:08-05:00December 17th, 2018|Academic Research Insight, Machine Learning|

A Backtesting Protocol in the Era of Machine Learning Rob Arnott, Campbell Harvey, and Harry Markowitz Working paper A version of this paper can be found here Want to read our summaries of academic finance papers? [...]

Machine Learning for Financial Market Prediction — Time Series Prediction With Sklearn and Keras

By |2018-06-25T16:50:17-04:00June 5th, 2018|Research Insights, Machine Learning|

Recently, Wes pointed me to this interesting paper by David Rapach, Jack Strauss, Jun Tu and Guofu Zhou: "Dynamic Return Dependencies Across Industries: A Machine Learning Approach." The paper presents a strategy that forecasts industry [...]

Machine Learning for Investors: A Primer

By |2017-10-05T11:14:33-04:00September 27th, 2017|Research Insights, Guest Posts, Machine Learning|

If you are out to describe the truth, leave elegance to the tailor. — Albert Einstein Machine learning is everywhere now, from self-driving cars to Siri and Google Translate, to news recommendation systems and, of [...]