IDEAS home Printed from https://ideas.repec.org/p/cfi/fseres/cf479.html
   My bibliography  Save this paper

Interest Rate Model with Investor Attitude and Text Mining (Published in IEEE Access)

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining (Published in IEEE Access)," CARF F-Series CARF-F-479, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  • Handle: RePEc:cfi:fseres:cf479
    as

    Download full text from publisher

    File URL: https://www.carf.e.u-tokyo.ac.jp/admin/wp-content/uploads/2020/05/F479.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leippold, Markus & Wu, Liuren, 2002. "Asset Pricing under the Quadratic Class," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(2), pages 271-295, June.
    2. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2019. "Online Appendix for Interest Rate Model with Investor Attitude and Text Mining," CIRJE F-Series CIRJE-F-1136, CIRJE, Faculty of Economics, University of Tokyo.
    3. Masafumi Nakano & Akihiko Takahashi & Muhammad Soichiro Takahashi, 2017. "Creating Investment Scheme with State Space Modeling," CIRJE F-Series CIRJE-F-1038, CIRJE, Faculty of Economics, University of Tokyo.
    4. Hisashi Nakamura & Wataru Nozawa & Akihiko Takahashi, 2009. "Macroeconomic Implications of Term Structures of Interest Rates Under Stochastic Differential Utility with Non-Unitary EIS," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 16(3), pages 231-263, September.
    5. Ho, Thomas S Y & Lee, Sang-bin, 1986. "Term Structure Movements and Pricing Interest Rate Contingent Claims," Journal of Finance, American Finance Association, vol. 41(5), pages 1011-1029, December.
    6. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    7. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    8. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    9. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    10. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1037, CIRJE, Faculty of Economics, University of Tokyo.
    11. Kiyohiko G. Nishimura & Seisho Sato & Akihiko Takahashi, 2019. "Term Structure Models During the Global Financial Crisis: A Parsimonious Text Mining Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(3), pages 297-337, September.
    12. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1037, CIRJE, Faculty of Economics, University of Tokyo.
    13. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Creating Investment Scheme with State Space Modeling," CARF F-Series cf406, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. Akihiko Takahashi & Seisho Sato, 2001. "A Monte Carlo Filtering Approach for Estimating the Term Structure of Interest Rates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 50-62, March.
    15. Takaya Fukui & Seisho Sato & Akihiko Takahashi, 2017. "Style analysis with particle filtering and generalized simulated annealing," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-29, June.
    16. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2019. "Online Appendix for Interest Rate Model with Investor Attitude and Text Mining," CARF F-Series CARF-F-470, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    17. Felix Ming Fai Wong & Zhenming Liu & Mung Chiang, 2014. "Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization," Papers 1406.7330, arXiv.org.
    18. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2019. "State Space Approach to Adaptive Fuzzy Modeling for Financial Investment," CIRJE F-Series CIRJE-F-1120, CIRJE, Faculty of Economics, University of Tokyo.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2024. "Multi-agent Equilibrium Model with Heterogeneous Views on Fundamental Risks in Incomplete Market," CARF F-Series CARF-F-578, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    3. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Akihiko Takahashi & Soichiro Takahashi, 2022. "A state space modeling for proactive management in equity investment "Forthcoming in International Journal of Financial Engineering"," CARF F-Series CARF-F-543, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2023. "Multi-agent Robust Optimal Investment Problem in Incomplete Market," CARF F-Series CARF-F-575, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    6. Taiga Saito & Akihiko Takahashi, 2021. "Portfolio Optimization with Choice of a Probability Measure," CIRJE F-Series CIRJE-F-1165, CIRJE, Faculty of Economics, University of Tokyo.
    7. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2022. "Multi-agent Robust Optimal Investment Problem in Incomplete Market," CIRJE F-Series CIRJE-F-1198, CIRJE, Faculty of Economics, University of Tokyo.
    8. Daiya Mita & Akihiko Takahashi, 2022. "Multi-Agent Model Based Proactive Risk Management For Equity Investment," CIRJE F-Series CIRJE-F-1207, CIRJE, Faculty of Economics, University of Tokyo.
    9. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2024. "Multi-agent Equilibrium Model with Heterogeneous Views on Fundamental Risks in Incomplete Market," CIRJE F-Series CIRJE-F-1224, CIRJE, Faculty of Economics, University of Tokyo.
    10. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2021. "Equilibrium Multi-Agent Model with Heterogeneous Views on Fundamental Risks," CIRJE F-Series CIRJE-F-1173, CIRJE, Faculty of Economics, University of Tokyo.
    11. Akihiko Takahashi & Soichiro Takahashi, 2022. "A State Space Modeling for Proactive Management in Equity Investment," CIRJE F-Series CIRJE-F-1197, CIRJE, Faculty of Economics, University of Tokyo.
    12. Taiga Saito & Akihiko Takahashi, 2022. "Portfolio optimization with choice of a probability measure (forthcoming in proceedings of IEEE CIFEr 2022)," CARF F-Series CARF-F-534, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining," CIRJE F-Series CIRJE-F-1152, CIRJE, Faculty of Economics, University of Tokyo.
    2. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi & Takami Tokioka, 2018. "On the Effect of Bank of Japan’s Outright Purchase on the JGB Yield Curve," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(1), pages 47-70, March.
    3. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2019. "Online Appendix for Interest Rate Model with Investor Attitude and Text Mining," CARF F-Series CARF-F-470, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2019. "Online Appendix for Interest Rate Model with Investor Attitude and Text Mining," CIRJE F-Series CIRJE-F-1136, CIRJE, Faculty of Economics, University of Tokyo.
    5. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    6. Wei-Choun Yu & Donald M. Salyards, 2009. "Parsimonious modeling and forecasting of corporate yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 73-88.
    7. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591.
    8. Nagy, Krisztina, 2020. "Term structure estimation with missing data: Application for emerging markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 347-360.
    9. Molenaars, Tomas K. & Reinerink, Nick H. & Hemminga, Marcus A., 2013. "Forecasting the yield curve - Forecast performance of the dynamic Nelson-Siegel model from 1971 to 2008," MPRA Paper 61862, University Library of Munich, Germany.
    10. Leo Krippner, 2003. "Modelling the Yield Curve with Orthonomalised Laguerre Polynomials: An Intertemporally Consistent Approach with an Economic Interpretation," Working Papers in Economics 03/01, University of Waikato.
    11. Choong Tze Chua & Dean Foster & Krishna Ramaswamy & Robert Stine, 2008. "A Dynamic Model for the Forward Curve," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 265-310, January.
    12. Emma Berenguer-Carceles & Ricardo Gimeno & Juan M. Nave, 2012. "Estimation of the Term Structure of Interest Rates: Methodology and Applications," Working Papers 12.06, Universidad Pablo de Olavide, Department of Financial Economics and Accounting (former Department of Business Administration).
    13. Engle, Robert & Roussellet, Guillaume & Siriwardane, Emil, 2017. "Scenario generation for long run interest rate risk assessment," Journal of Econometrics, Elsevier, vol. 201(2), pages 333-347.
    14. Tunaru, Diana, 2017. "Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 119-129.
    15. Christensen, Bent Jesper & van der Wel, Michel, 2019. "An asset pricing approach to testing general term structure models," Journal of Financial Economics, Elsevier, vol. 134(1), pages 165-191.
    16. Mei-Mei Kuo & Shih-Wen Tai & Bing-Huei Lin, 2012. "Forecasting Term Structure of HIBOR Swap Rates," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 6(4), pages 87-100.
    17. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    18. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    19. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    20. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cfi:fseres:cf479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/catokjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.