Whence LASSO? A Rational Interpretation

56 Pages Posted: 22 Nov 2022 Last revised: 17 May 2024

See all articles by Wen Chen

Wen Chen

Texas Tech University

Bo Hu

George Mason University

Liyan Yang

University of Toronto - Rotman School of Management

Date Written: June 19, 2021

Abstract

This paper rationalizes the use of LASSO for return predictions based on robust optimization with uncertain fat-tail priors. Our theory avoids assumptions of heuristic learning or restrictive priors
made in the statistical interpretation of LASSO by its inventor Tibshirani (1996). In our setting, agents (arbitrageurs) are uncertain about the scale of fat-tail shocks. In equilibrium, they rationally discard a wide range of ambiguous signals and respond conservatively to almost unambiguous signals. Using this LASSO equivalent strategy, arbitrageurs can amass extra market power which induces a "cartel" to protect their total trading profits from being competed away. This result shows a new mechanism for limits to arbitrage.

Keywords: LASSO, Fat Tails, Model Risk, Robust Optimization, Limits to Arbitrage

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JEL Classification: C44, D81, G12, G14

Suggested Citation

Chen, Wen and Hu, Bo and Yang, Liyan, Whence LASSO? A Rational Interpretation (June 19, 2021). Rotman School of Management Working Paper No. 4279679, George Mason University School of Business Research Paper No. 4279679, Available at SSRN: https://ssrn.com/abstract=4279679 or http://dx.doi.org/10.2139/ssrn.4279679

Wen Chen

Texas Tech University ( email )

2500 Broadway
Lubbock, TX 79409
United States

Bo Hu (Contact Author)

George Mason University ( email )

4400 University Drive
Fairfax, VA 22030
United States

HOME PAGE: http://sites.google.com/view/bohuhome

Liyan Yang

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

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