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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)163-169
Number of pages7
JournalSmart Innovation, Systems and Technologies
Volume4
DOIs
Publication statusPublished - 2010 Dec 1

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Independent component analysis
Decision support systems
Feature extraction
Profitability
Managers
Financial options
Kernel
Industry
Financial markets
Investment decision
Option trading

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)

Cite this

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Combining ICA with Kernel based regressions for trading support systems on financial options. / Huang, Shian-Chang; Li, Chuan Chyuan; Lee, Chih Wei; Chang, M. Jen.

In: Smart Innovation, Systems and Technologies, Vol. 4, 01.12.2010, p. 163-169.

Research output: Contribution to journalArticle

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