Searching for the Best Conditional Equity Premium Model

82 Pages Posted: 2 Aug 2022

See all articles by Hui Guo

Hui Guo

University of Cincinnati - Department of Finance - Real Estate

Saidat Sanni

University of Cincinnati

Yan Yu

University of Cincinnati - Department of Business Analytics

Date Written: July 20, 2022

Abstract

Three most prominent theoretical state variables, inflation, scaled market prices, and market variance, are identified as best market return predictor variables via best subset variable selection. They work better together than individually, consistent with multifactor equity premia stressed by recent asset pricing models. Bootstrap simulation shows that best subset has high accuracy rates. The variables are consistently chosen by alternative variable selection techniques. The multifactor model is strikingly stable across subsamples and has an out-of-sample R2 of 9.6% over the 1965Q1 to 2020Q4 period, outperforming machine learning models and the combination forecast method. We find similar results using international data.

Keywords: Market equity risk premium, Machine Learning, Random Forecasts, Boosted Regression Trees, Neural Networks, Stock market variance, Scaled market prices, Inflation, Multifactor model, Consumption-based asset pricing model, The Great Depression

JEL Classification: G10, G12, G17

Suggested Citation

Guo, Hui and Sanni, Saidat and Yu, Yan, Searching for the Best Conditional Equity Premium Model (July 20, 2022). Available at SSRN: https://ssrn.com/abstract=4168294 or http://dx.doi.org/10.2139/ssrn.4168294

Hui Guo (Contact Author)

University of Cincinnati - Department of Finance - Real Estate ( email )

College of Business
418 Carl H. Lindner Hall
Cincinnati, OH 45221
United States
513.556.7077 (Phone)
513.556.0979 (Fax)

HOME PAGE: http://homepages.uc.edu/~guohu/

Saidat Sanni

University of Cincinnati ( email )

Cincinnati, OH 45221-0389
United States

Yan Yu

University of Cincinnati - Department of Business Analytics ( email )

606 Carl H. Lindner Hall 2925 Campus Green Drive
PO Box 210211
Cincinnati, OH 45221-0211
United States

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