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

Masafumi Nakano, Akihiko Takahashi and Soichiro Takahashi
Additional contact information
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

No CARF-F-422, CARF F-Series from Center for Advanced Research in Finance, Faculty 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.

Pages: 22 pages
Date: 2018-02
New Economics Papers: this item is included in nep-cmp and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf422

Access Statistics for this paper

More papers in CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by ().