Comparing Asset Pricing Models: Distance-Based Metrics and Bayesian Interpretations

61 Pages Posted: 15 Nov 2017 Last revised: 23 Jan 2018

See all articles by Zhongzhi Lawrence He

Zhongzhi Lawrence He

Brock University, Goodman School of Business

Date Written: December 1, 2017

Abstract

In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian investor views model performance as the shortest distance between his dogmatic belief (model-implied distribution) and complete skepticism (data-based distribution) in the model, and favors models that produce low dispersion of alphas with high explanatory power. In this view, the momentum factor is a crucial addition to the five-factor model of Fama and French (2015), alleviating his prior concern of model mispricing by -8% to 8% per annum. The distance metrics complement the frequentist p-values with a diagnostic tool to guard against bad models.

Keywords: Distance-based Metrics; Bayesian Interpretations; Model Comparison; Power Problems; Mispricing Uncertainty; Optimal Transport Method

JEL Classification: C11; G11; G12

Suggested Citation

He, Zhongzhi Lawrence, Comparing Asset Pricing Models: Distance-Based Metrics and Bayesian Interpretations (December 1, 2017). Available at SSRN: https://ssrn.com/abstract=3069952 or http://dx.doi.org/10.2139/ssrn.3069952

Zhongzhi Lawrence He (Contact Author)

Brock University, Goodman School of Business ( email )

500 Glenridge Avenue
Finance
St. Catherine's, Ontario L2S 3A1
Canada

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
56
Abstract Views
547
rank
441,365
PlumX Metrics