Combining ICA with Kernel based regressions for trading support systems on financial options

Shian-Chang Huang; Chuan Chyuan Li; Chih Wei Lee; M. Jen Chang

doi:10.1007/978-3-642-14616-9_15

Combining ICA with Kernel based regressions for trading support systems on financial options

Shian-Chang Huang, Chuan Chyuan Li, Chih Wei Lee, M. Jen Chang

Department of Business Administration

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	163-169
Number of pages	7
Journal	Smart Innovation, Systems and Technologies
Volume	4
DOIs	https://doi.org/10.1007/978-3-642-14616-9_15
Publication status	Published - 2010 Dec 1

All Science Journal Classification (ASJC) codes

Decision Sciences(all)
Computer Science(all)

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title = "Combining ICA with Kernel based regressions for trading support systems on financial options",

abstract = "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.",

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