This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e.:(i) a module for preparing candidate investment proposals and,(ii) a module for their evaluation to construct a well-performed...

This paper proposes a new state space approach to adaptive fuzzy modeling under the dynamically changing environment, where Bayesian filtering sequentially learns parameters including model structures as state variables. Moreover with a particle filtering algorithm, our approach is widely...

This paper examines an impact of Bank of Japan (BOJ)'s outright purchase on the JGB (Japanese government bond) yield curve. Particularly, we develop a simple state space model, which incorporates new factors regarding the BOJ's announcement for its outright purchase and the current market...

This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space...

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...

This paper proposes a unified approach to creating investment strategies with various desirable properties for investors.Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as...

This paper proposes a novel scheme for achieving high investment performances with Mean-Variance (MV) portfolios. As is well-known, MV portfolio performances largely depend on the quality of estimates of parameters, namely expected returns and covariance matrices. Particularly, easily...