Sign Prediction and Sign Regression

Posted: 9 Nov 2020 Last revised: 13 Oct 2022

See all articles by Weige Huang

Weige Huang

Zhongnan University of Economics and Law; Temple University

Date Written: September 19, 2020

Abstract

Intuitively, model-predicted signs matter a lot in finance, especially for investment strategy constructions. This paper proposes an approach whereby the loss function regularizes the errors in prediction in different ways. In particular, the loss function considers errors in predicted signs and the sizes and signs of the residuals in the model prediction simultaneously. Less weight is given to residuals with correctly predicted signs, while more weight is assigned to residuals with wrongly predicted signs. This is important because agents make decisions according to model predictions, especially the signs of the predictions. At the same time, larger residuals are penalized more and smaller residuals are penalized less. The signs of the residuals are considered in the loss function because they also affect decision-making processes. This paper proposes a new approach, termed “sign regression”, which takes these considerations into account. We show that ordinary least squares estimators generate better Sharpe ratios than sign regression does for most of the assets studied in this paper. However, sign regression can perform better for some assets.

(Published at Journal of Investment Strategies, DOI: 10.21314/JOIS.2021.002)

Keywords: Financial Prediction, Prediction Signs, Signs of Residuals, Decision Making

JEL Classification: C51, D81, G17

Suggested Citation

Huang, Weige, Sign Prediction and Sign Regression (September 19, 2020). Available at SSRN: https://ssrn.com/abstract=3695594 or http://dx.doi.org/10.2139/ssrn.3695594

Weige Huang (Contact Author)

Zhongnan University of Economics and Law ( email )

182# Nanhu Avenue
East Lake High-tech Development Zone
Wuhan, Hubei 430073
China

Temple University ( email )

Philadelphia, PA 19122
United States

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