A combination forecasting model using machine learning and Kalman filter for statistical arbitrage

In this paper we evaluate the combination of Extreme Learning Machine (ELM) and Support Vector Regression (SVR) with a Kalman filter regression model for financial time series forecasting. We also compare the forecast performance with a set of linear regression combination methods. The application of the traditional Kalman Filter for the statistical arbitrage strategy improves the statistical performance of ELM and SVR individual forecasts. The accuracy of the models is statistically tested and an investigation is performed to confirm the impact of the forecasts combination in terms of annualized returns and volatility.