State–space approach to adaptive fuzzy modeling for financial investment

Nakano, Masafumi; Takahashi, Akihiko; Takahashi, Soichiro

doi:10.1016/j.asoc.2019.105590

Cited by 10 publications

(3 citation statements)

References 68 publications

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“…Here, we use the daily zero yields of Japanese Government Bond (JGB) from January 4th 2012 to December 30th 2017. The zero yields are estimated from par rate data available on the website of Ministry of Finance (MOF) Japan, with a 9 News reports are often about anticipation of possible events in the future. Thus, it sometimes happens that appearance of particular news words of, say, a month ago is strongly correlated with today's events.…”

Section: Empirical Analysis Results a Estimation Results For Two-factor Modelmentioning

confidence: 99%

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Interest Rate Model With Investor Attitude and Text Mining

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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.INDEX TERMS Interest rate model, text mining, filtering.

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Section: Empirical Analysis Results a Estimation Results For Two-factor Modelmentioning

confidence: 99%

“…and x 2 2,t ), and define the optimal F n−lagged (A i ) (F n−lagged (B i )) as F lagged (A i ) (F lagged (B i )). 9 10). We use time series {log( i F lagged (A i ) + 1)} t and {log( i F lagged (B i ) + 1)} t in an observation equation for a steepening factor and a flattening factor, respectively.…”

Section: Text Miningmentioning

confidence: 99%

Interest Rate Model With Investor Attitude and Text Mining

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“…This appendix describes the outline of an algorithm of Monte Carlo filter used in this work, which is an adaptation of Fukui et al (2017) and Nakano et al (2017aNakano et al ( ,2017bNakano et al ( ,2019. We introduce a state space model that consists of the following system and observation models:…”

Section: Algorithm For Monte Carlo Filtermentioning

confidence: 99%

Online Appendix for Interest Rate Model with Investor Attitude and Text mining

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This online appendix provides some results omitted in the paper entitled Interest rate model with investor attitude and text mining by Nakatani et al. (2019).Let (Ω, F, {F} 0≤t≤T , P ) be a filtered probability space satisfying the usual conditions. We consider an economy with a representative agent and an endowment to the agent. Then, in equilibrium where the agent consumes all the given endowment at each instant, the agent's optimal consumption must be equal to the endowment process. Hence, let us assume a nonnegative consumption (i.e. endowment) process c exogenously whose expected return and volatility depend on a R l -valued state vector x, as the {F t }-adapted progressively measurable process satisfying the following stochastic differential equations (SDEs):Here, while the economy is driven by specific Brownian motions representing fundamental risk sources, the agent is not certain about all Brown motions. The agent thinks there is fundamental uncertainty about some of these fundamental risks, Brownian motions. We follow Nisimura, Sato and Takahashi (2019) formulating that under fundamental uncertainty about them, the representative agent does not face a single probability measure, but a set of probability measures. In particular, in the diffusion process framework, we postulate that the agent's fundamental uncertainty is represented by a set of different Brownian motions, i.e. a set of d-dimensional Brownian motions B λ1,λ2 characterized by the equation (4) with a particular set of stochastic processes, (5) below. Moreover, the representative agent may be "conservative" about the fundamental uncertainty for some Brownian motions (in the sense that the agent considers their worst possible case), * The views expressed in this paper are our own and do not reflect the institutions we are affiliated with. Financial supports from CARF at the University of Tokyo and JSPS KAKEN(S) #18H05217 are gratefully acknowledged. † Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd. ‡ The views expressed here are those of the author and do not represent the official views of MTEC.

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