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

Jarley P. Nobrega,Adriano L. I. Oliveira

Systems, Man and Cybernetics（2014）

引用 7|浏览13

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摘要

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.

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关键词

Kalman filters,financial management,learning (artificial intelligence),regression analysis,statistical testing,support vector machines,time series,ELM,Kalman filter regression model,SVR,extreme learning machine,financial time series forecasting,forecasting model,linear regression combination methods,statistical arbitrage strategy,statistical performance,statistical testing,support vector regression,Extreme Learning Machine,Forecast Combinations,Kalman Filter,Pair Trading,Statistical Arbitrage,Support Vector Regression

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