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State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System

Author

Listed:
  • Masafumi Nakano

    (Graduate School of Economics, the University of Tokyo)

  • Akihiko Takahashi

    (Graduate School of Economics, the University of Tokyo)

  • Soichiro Takahashi

    (Graduate School of Economics, the University of Tokyo)

Abstract

This paper proposes a new state space approach to adaptive artificial intelligence (AI) modeling under the dynamic environment, where Bayesian filtering sequentially learns the model parameters including model structures themselves as state variables. In particular, our approach is widely applicable to the machine learning of non-linear AI models for real-time observation data flows through Monte-Carlo simulation-based filtering algorithms called particle filters. To show the effectiveness of our framework, we concretely design a Takagi-Sugeno-Kang fuzzy model for financial portfolio construction, where particle filtering learns the model parameters as state variables. As a promising application, we suppose that the model parameters follow mean-reversion processes, which makes it possible to update these parameters around predetermined levels. Therefore, by deciding the levels based on existing state-of-art learning methods over the training data, our approach successfully incorporates and extends their learning results through adjusting those to the changing environment. An out-of-sample simulation with long term time-series data of stock and bond prices demonstrates the validity of our framework.

Suggested Citation

  • 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.
  • Handle: RePEc:cfi:fseres:cf422
    as

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    References listed on IDEAS

    as
    1. 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.
    2. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection (Subsequently published in "Knowledge-Based Systems")," CARF F-Series CARF-F-405, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. 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.
    4. 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.
    5. 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.
    6. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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