Combining ICA with Kernel Based Regressions for Trading Support Systems on Financial Options
Options are highly non-linear and complicated products in financial markets. Owing to the high risk associated with option trading, investment on options is a knowledge-intensive industry. This study develops a novel decision support system for option trading. In the first stage, independent component analysis (ICA) is employed to uncover the independent hidden forces of the stock market that drive the price movement of an option. In the second stage, a dynamic kernel predictors are constructed for trading decisions. Comparing with convectional feature extractions and pure regression models, the performance improvement of the new method is significant and robust. The cumulated trading profits are substantialy increased. The resultant intelligent investment decision support system can help investors, fund managers and investment decision-makers make profitable decisions.
KeywordsIndependent Component Analysis Kernel Partial Least Square Regression Support Vector Machine Financial Trading Algorithmic Trading System
Unable to display preview. Download preview PDF.
- Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
- Rosipal, R.: Kernel Partial Least Squares for Nonlinear Regression and Discrimination. Neural Network World 13(3), 291–300 (2003)Google Scholar
- Schoelkopf, B., Burges, C.J.C., Smola, A.J.: Advances in kernel methods - support vector learning. MIT Press, Cambridge (1999)Google Scholar