Fuzzy Logic-Based Portfolio Selection with Particle Filtering and Anomaly Detection

This paper proposes a new fuzzy logic (FL)-based expert system with particle filtering and anomaly detection to create high-performance investment portfolios. In particular, our FL system selects a portfolio with fine risk-return profiles from a number of candidates by integrating multilateral performance measures including Sharpe, Sortino and Sterling ratios. The candidates consist of various mean-variance portfolios with multiple time-series models estimated by a particle filter and anomaly detectors. In an out-of-sample numerical experiment with a dataset of international financial assets, we demonstrates our expert system successfully generates a series of mean-variance portfolios with satisfactory investment records