Academic Finance Research and Insights

Can Machine Learning Improve Factor Returns? Not Really

By |April 29th, 2024|Factor Investing, Research Insights, Basilico and Johnsen, Academic Research Insight, AI and Machine Learning|

Can AI models improve on the failures in predicting returns strictly from a practical point of view?  In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks.  These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to traditional sorting and cross-sectional regressions used in factor research. The authors hope to overcome the drawbacks and confirm the results of traditional quant methods. As it turns out, those hopes are only weakly fulfilled by the MLM framework.

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Social Media, Analyst Behavior and Market Efficiency

By |April 26th, 2024|Larry Swedroe, Research Insights, Guest Posts, Other Insights, Behavioral Finance|

Hibbert, Kang, Kumar and Mishra provided us with yet another explanation: social media is providing analysts with information that reduces their forecasting errors. The result has been an increase in market efficiency, leading to a reduction in the PEAD anomaly. The bottom line is that the ability to generate alpha continues to be under assault—trying to outperform the market by stock selection is becoming even more of a loser’s game.  

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Is Sector Neutrality in Factor Investing a Mistake?

By |April 15th, 2024|Research Insights, Factor Investing, Basilico and Johnsen, Academic Research Insight|

The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven't had an empirical model to mimic the impact of removing sector "effects" on the measurement and performance of factor strategies. The authors develop and test a two-component model to address the question of, "Is Sector Neutrality in Factor Investing a Mistake?"

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Minimizing the Risk of Cross-Sectional Momentum Crashes

By |April 12th, 2024|Research Insights, Factor Investing, Larry Swedroe, Other Insights, Momentum Investing Research|

While the empirical research on cross-sectional (long-short) momentum has shown that returns have been high, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926 there have been several momentum crashes that featured short, but persistent, periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%, followed by a second drawdown from April to July 1933.

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