Big Data Analysis of the Dynamic Effects of Business Cycles on Stock Prices in Japan

Abstract

In this paper, we analyze the dynamic effects of business cycles on stock prices using a regression model with a time-varying coefficient. The regression model is constructed using the Nikkei Stock Average (NSA) as the dependent variable and the coincident Composite Index in Japan (CIJ) as the explanatory variable. The Bayesian smoothness priors technique is applied to estimate the time-varying coefficient. Moreover, we analyze the behavior of the estimated time-varying coefficient to explain the dynamic relationship between business cycles and stock prices. The impact of some economic and social events on stock prices in Japan is also analyzed by examining the estimated observation noise in the regression model. As an empirical example, we analyze the daily time series of NSA closing values from January 4, 1991, to December 29, 2017, together with monthly CIJ data over the same period.