Abstract——In previous study, we proposed a Bayesian
modeling technique to decompose a daily time series of Nikkei
Stock Average (NSA) into three components which include a
trend component, and analyzed the behavior of each estimated
component. It was confirmed that there is time-varying
correlation between the trend component and the coincident
Composite Index in Japan (CIJ). In this paper, as an extension
of the previous study we analyze the dynamic relationship
between the trend component in the NSA and the CIJ using a
regression model with a time-varying coefficient and a lag
parameter. The regression model is constructed using the NSA
as the dependent variable and the CIJ as the explanatory
variable. Bayesian smoothness prior technique is applied to
estimate the time-varying coefficient. Moreover, we explain the
dynamic relationship between business cycles and stock prices
based on the estimates of the time-varying coefficient and the
lag parameter. As an empirical example, we analyze the daily
time series of NSA closing values from January 4, 1991, to
March 30, 2018, together with the monthly CIJ data over the
same period.
Index Terms—Bayesian modeling, state space model, big data
analysis, daily stock price data, business cycles in Japan.
Koki Kyo is with Obihiro University of Agriculture and Veterinary
Medicine, Inada-cho, Obihiro, Hokkaido 080-8555, Japan (e-mail:
kyo@obihiro.ac.jp).
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Cite: Koki Kyo, "Big Data Analysis of the Dynamic Relationship between Stock Prices and Business Cycles Via Bayesian Methods," International Journal of Trade, Economics and Finance vol.9, no.6, pp. 224-230, 2018.