Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection (Subsequently published in "Knowledge-Based Systems")

Masafumi Nakano, Akihiko Takahashi and Soichiro Takahashi
Additional contact information
Masafumi Nakano: Graduate School of Ecnonomics, University of Tokyo
Akihiko Takahashi: Faculty of Economics, University of Tokyo
Soichiro Takahashi: Graduate School of Ecnonomics, University of Tokyo

No CARF-F-405, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: This paper proposes a new knowledge-based system (KBS) featuring fuzzy logic (FL) with particle filtering and anomaly detection to create high-performance investment port-folios. In particular, our FL system selects a portfolio with fine risk-return profiles from a number of candidates by integrating multilateral performance measures. The candidates consist of various portfolios based on multiple time-series models estimated by a particle filter with anomaly detectors. In an out-of-sample numerical experiment with a dataset of international financial assets, we demonstrate our KBS successfully generates a series of selected portfolios with satisfactory investment records.

Pages: 26 pages
Date: 2017-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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:cf405

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 ().