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

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn 2014-January, Issue January, 2014, Pages 1294-1299

  Nóbrega, Jarley P ^a   Oliveira, Adriano L I ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

Extreme learning machine; Forecast combinations; Kalman filter; Pair trading; Statistical arbitrage; Support vector regression

Indexed keywords

ARTIFICIAL INTELLIGENCE; CYBERNETICS; FINANCIAL DATA PROCESSING; FORECASTING; KALMAN FILTERS; KNOWLEDGE ACQUISITION; REGRESSION ANALYSIS;

EXTREME LEARNING MACHINE; FORECAST COMBINATIONS; PAIR TRADING; STATISTICAL ARBITRAGE; SUPPORT VECTOR REGRESSION (SVR);

LEARNING SYSTEMS;

EID: 84938149965     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SMC.2014.6974093     Document Type: Conference Paper

References (21)

1. M. Deutsch, C.W.J. Granger, T. Teräsvirta, "The combination of forecasts using changing weights, " International Journal of Forecasting 10 (1) (1994) 47-57.
2. G.-B. Huang, "Learning capability and storage capacity of two hiddenlayer feedforward networks, " IEEE Trans. Neural Networks, Vol. 14, no. 2, pp. 274-281, 2003.
3. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, "Extreme learning machine: Theory and applications", Neurocomputing, Vol.70, no.1-3, pp. 489- 501, Dec. 2006.
4. C.-J. Lu, T.-S Lee, C.-C. Chiu, "Financial time series forecasting using independent component analysis and support vector regression, " Decision Support Systems, 47(2), 2009, 115-125. doi:10.1016/j.dss.2009.02.001.
5. V. Vapnik, S. Golowich, A. Smola, "Support vector machine for function approximation, regression estimation, and signal processing, " Advances in Neural Information Processing Systems, vol. 9, p. 281-287, 1996.
6. J. P. Nóbrega, A. L. I. Oliveira, "Improving the Statistical Arbitrage Strategy in Intraday Trading by Combining Extreme Learning Machine and Support Vector Regression with Linear Regression Models", IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), 182- 188, 2013.
7. S.L. Goh, D.P. Mandic, "An augmented extended Kalman Filter algorithm for complex-valued recurrent neural networks, " Neural Computation 19 (4) (2007) 1039-1055.
8. G. Sermpinis, C. Dunis, J. Laws, C. Stasinakis, "Forecasting and Trading the EUR/USD Exchange Rate with Stochastic Neural Network Combination and Time-varying Leverage, " Decision Support Systems, ISSN 0167-9236 (2013).
9. N. Terui, H.K. Van Dijk, "Combined forecasts from linear and nonlinear time series models, " International Journal of Forecasting 18 (3) (2002) 421-438.
10. S.T. Buckland, K.P. Burnham, N.H. Augustin, "Model selection: An integral part of inference", Biometrics 53 (2) (1997) 603-618.
11. C.W.J. Granger, R. Ramanathan, "Improved Methods of Combining Forecasts, " Journal of Forecasting 3 (2), 197-204 (1984).
12. H. Wang, G. Li, G. Jiang, "Robust Regression Shrinkage and Consistent Variable Selection through the LAD-Lasso, " Journal of Business and Economic Statistics 25 (3), 347-355 (2007).
13. G. Vidyamurthy, "Pairs Trading, Quantitative Methods and Analysis, " John Wiley & Sons, (2004).
14. W.F. Sharpe, "The Sharpe Ratio, " The Journal of Portfolio Management 21 (1), 2010, 49-58, doi:10.3905/jpm.1994.409501.
15. A. Pole, "Statistical Arbitrage: Algorithmic Trading Insights and Techniques, " Wiley Finance, 2007.
16. A.C. Harvey, "Forecasting, Structural Time Series Models and the Kalman Filter, " Cambridge University Press, Cambridge, U.K., 1990.
17. M. A. Hall, "Correlation-based Feature Subset Selection for Machine Learning", Hamilton, New Zealand, 1998.
18. A. S. Moraglio, C. D. Chio, R. Poli, "Geometric Particle Swarm Optimisation", In: Proceedings of the 10th European Conference on Genetic Programming, Berlin, Heidelberg, 125-136, 2007.
19. "Caret R Package", June, 2013 [online], Available: http://cran.rproject.org/web/packages/caret/caret.pdf.
20. R.F. Engle, C.W.J. Granger, "Cointegration and error-correction: Representation, estimation and testing, " Econometrica, 55, 1987, 251- 276.
21. E. Bibbona, G. Panfilo and P. Tavella: "The Ornstein-Uhlenbeck process as a model of a low pass filtered whitenoise, " Metrologia, 45, 2008, S117-S126.