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 | |
| Publication status | Published - 2010 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Computer Science(all)
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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 journal › Article
TY - JOUR
T1 - Combining ICA with Kernel based regressions for trading support systems on financial options
AU - Huang, Shian-Chang
AU - Li, Chuan Chyuan
AU - Lee, Chih Wei
AU - Chang, M. Jen
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84879320585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879320585&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-14616-9_15
DO - 10.1007/978-3-642-14616-9_15
M3 - Article
AN - SCOPUS:84879320585
VL - 4
SP - 163
EP - 169
JO - Smart Innovation, Systems and Technologies
JF - Smart Innovation, Systems and Technologies
SN - 2190-3018
ER -