Generalized exponential moving average (EMA) model with particle filtering and anomaly detection

- We propose a new exponential moving average (EMA) model in a state space framework.
- We develop 3 anomaly detectors with a particle filter used for investment decision.
- We implement investment analysis with our method by using global asset price data.
- Our scheme outperforms practically well-known strategies including standard EMAs.

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 framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum-variance and risk parity portfolios.Our dataset is monthly total returns of global financial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.