Interest Rate Model With Investor Attitude and Text Mining

Nakatani, Souta; Nishimura, Kiyohiko G.; Saito, Taiga; Takahashi, Akihiko

doi:10.1109/access.2020.2992477

Cited by 13 publications

(18 citation statements)

References 37 publications

(55 reference statements)

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“…For example, regarding investment, an attitude can be a determinant that can affect a person's intention to invest (Khin, 2019;Dika & Kassim, 2021;Du & Shen, 2021) According to Nofi (2020), attitude can also be interpreted as a tendency obtained by individuals to do something which is the result of cognitive processes. Nakatani et al (2020) added that attitude is a good predictor of intention to do something. East (1993) used attitude as one of the factors to explain intention to invest and found it to be a significant predictor.…”

Section: Attitude and Investor Decisionmentioning

confidence: 99%

Examining the predictors of crypto investor decision

Syarkani

Tristanto

2022

IJRBS

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The rise in the cryptocurrency market has led many individuals to start their investment. However, individuals are affected by several factors in their decision-making process to invest. The objective of this study is to examine the factors that can predict the decision to invest in the cryptocurrency market, by utilizing the variables of overconfidence, financial literacy, and attitude. This study also explores the role of attitude in mediating the influence of overconfidence and financial literacy on investor decisions. The population of this study is individual student investors in three top universities in Indonesia. This study uses a non-probability sampling technique, with purposive sampling that includes several criteria to determine the number of samples. The data is collected by distributing questionnaires to the students using Google Forms. The total number of respondents in this study, who returned the questionnaire that has been filled is 297 students. The data obtained from respondents is analyzed using Structural Equation Modeling with Partial Least Square (SEM-PLS). Finally, the results of this study have proved that: (1) Overconfidence has a positive influence on attitude; (2) Financial literacy has a positive influence on attitude; (3) Overconfidence has a positive influence on investor decision; (4) Financial literacy has a positive influence on investor decision; (5) Attitude has a positive influence on investor decision; (6) Attitude mediates the influence of overconfidence on investor decision; and (7) Attitude mediates the influence of overconfidence on investor decision. The implications for scholars and crypto investors are also provided in this study.

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Section: Attitude and Investor Decisionmentioning

confidence: 99%

Examining the predictors of crypto investor decision

Syarkani

Tristanto

2022

IJRBS

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“…Since x 1,t and x 2,t are Gaussian processes under P , probability measures P λ1,λ2 and P λ * 1 ,λ * 2 are well defined by (2). (e.g.…”

Section: A Three-factor Specificationmentioning

confidence: 99%

“…(ii) The condition c 1 ≥ −(b 1 σ x,1 ) 2 /2 is for a zerocoupon bond price to be well-defined. (iii) The conditions c 1 < 0 and c 2 > 0 imply that the effects of spread factors x 2 1 and x 2 2 on the equilibrium interest rate r are the same directions as those of the investor(agent)'s conservative (λ 1 σ c,1 x 2 1,t < 0) and optimistic (λ 2 σ c,2 x 2 2,t > 0) attitudes, respectively. Specifically, the conditions c 1 < 0 and c 2 > 0 are achieved by setting µ 1 = µ 2 = 0 andλ 2 > σ c,2 in the expected return and volatility parameters ((32), (33)) of the consumption (endowment) process.…”

Section: Special Case: Transitory and Permanent Factorsmentioning

confidence: 99%

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

et al. 2020

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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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“…Also, people’s views on financial markets and economics are observed on social media. The revenue management model by Saito et al ( 2019 , 2016 ), the interest rate model with sentiments by Nishimura et al ( 2019 ) and Nakatani et al ( 2020 ), and the high-frequency trading equity market model by Saito and Takahashi ( 2019 ), are quantitative theoretical models that express behaviors of customers, investors, and market participants. Moreover, these models incorporate big data, such as booking data, financial data with text data, and order and execution data into modeling.…”

Section: Introductionmentioning

confidence: 99%

Big data applications with theoretical models and social media in financial management

Saito

Gupta

2022

Ann Oper Res

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This study presents big data applications with quantitative theoretical models in financial management and investigates possible incorporation of social media factors into the models. Specifically, we examine three models, a revenue management model, an interest rate model with market sentiments, and a high-frequency trading equity market model, and consider possible extensions of those models to include social media. Since social media plays a substantial role in promoting products and services, engaging with customers, and sharing sentiments among market participants, it is important to include social media factors in the stochastic optimization models for financial management. Moreover, we compare the three models from a qualitative and quantitative point of view and provide managerial implications on how these models are synthetically used along with social media in financial management with a concrete case of a hotel REIT. The contribution of this research is that we investigate the possible incorporation of social media factors into the three models whose objectives are revenue management and debt and equity financing, essential areas in financial management, which helps to estimate the effect and the impact of social media quantitatively if internal data necessary for parameter estimation are available, and provide managerial implications for the synthetic use of the three models from a higher viewpoint. The numerical experiment along with the proposition indicates that the model can be used in the revenue management of hotels, and by improving the social media factor, the hotel can work on maximizing its sales.

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Interest Rate Model With Investor Attitude and Text Mining

Cited by 13 publications

References 37 publications

Examining the predictors of crypto investor decision

Examining the predictors of crypto investor decision

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

Big data applications with theoretical models and social media in financial management

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