Stock Returns and Real Growth: A Bayesian Nonparametric Approach

28 Pages Posted: 10 Apr 2018 Last revised: 26 Jun 2019

See all articles by Qiao Yang

Qiao Yang

ShanghaiTech University - School of Entrepreneurship and Management

Date Written: June 25, 2019

Abstract

This study constructs a Bayesian nonparametric model to investigate whether stock market returns predict real economic growth. Unlike earlier studies, our use of an infinite hidden Markov model enables parameters to be time-varying across an infinite number of Markov-switching states estimated from data rather than fixed like a prior. Our model exhibits significantly greater accuracy in out-of-sample density forecasts. We uncover strong evidence of the time-varying power of lagged stock returns to predict economic growth.

Keywords: hierarchical Dirichlet process prior, beam sampling, Markov switch- ing, MCMC

JEL Classification: C58, C14, C22, C11

Suggested Citation

Yang, Qiao, Stock Returns and Real Growth: A Bayesian Nonparametric Approach (June 25, 2019). ShanghaiTech SEM Working Paper No. 2018-003. Available at SSRN: https://ssrn.com/abstract=3159711 or http://dx.doi.org/10.2139/ssrn.3159711

Qiao Yang (Contact Author)

ShanghaiTech University - School of Entrepreneurship and Management ( email )

100 Haike Rd
Pudong Xinqu, Shanghai
China

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