Interest Rate Model with Investor Attitude and Text Mining (Published in IEEE Access)
Kiyohiko G. Nishimura,
Taiga Saito and
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Souta Nakatani: MTEC and Graduate School of Economics, University of Tokyo
Kiyohiko G. Nishimura: National Graduate Institute for Policy Studies (GRIPS) and CARF, University of Tokyo
Taiga Saito: Graduate School of Economics and CARF, University of Tokyo
Akihiko Takahashi: Graduate School of Economics and CARF, University of Tokyo
No CARF-F-479, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo
This paper develops and estimates an interest rate model with investor attitude factors, which are extracted by a text mining method. First, we consider two contrastive attitudes (optimistic versus conservative) towards uncertainties about Brownian motions driving economy, develop an interest rate model, and obtain an empirical framework of the economy consisting of permanent and transitory factors. Second, we apply the framework to a bond market under extremely low interest rate environment in recent years, and show that our three-factor model with level, steepening and flattening factors based on different investor attitudes is capable of explaining the yield curve in the Japanese government bond (JGB) markets. Third, text mining of a large text base of daily financial news reports enables us to distinguish between steepening and flattening factors, and from these textual data we can identify events and economic conditions that are associated with the steepening and flattening factors. We then estimate the yield curve and three factors with frequencies of relevant word groups chosen from textual data in addition to observed interest rates. Finally, we show that the estimated three factors, extracted only from the bond market data, are able to explain the movement in stock markets, in particular Nikkei 225 index.
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