Abstract
Big data analysis and cloud computing are becoming increasingly involved in the area of finance. The high computation capability enables one to apply complicated analysis utilizing large amounts offinancial data. Big data analysis can find hidden patterns in large amounts of data.This capability can help investors in derivatives pricing, risk management and financial forecasting, and profitable trading. Owing to the high risk associated with trading financial options, this study aims to develop anintelligent option trading support system, where nonlinear or kernel canonical correlation analysis (KCCA) is used to extract the hidden forces that drive the price movement of an option, and a generalized dynamic kernel based predictors are employed to generate trading signals. 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 substantially increased. The resultant intelligent trading support system can help investors, fund managers and investment decision-makers make better and profitable decisions.
| Original language | English |
|---|---|
| Pages (from-to) | 32-44 |
| Number of pages | 13 |
| Journal | Global Business and Finance Review |
| Volume | 22 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Business and International Management
- Finance
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A big data analysis system for financial trading. / Huang, Shian-Chang.
In: Global Business and Finance Review, Vol. 22, No. 3, 01.01.2017, p. 32-44.Research output: Contribution to journal › Article
TY - JOUR
T1 - A big data analysis system for financial trading
AU - Huang, Shian-Chang
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Big data analysis and cloud computing are becoming increasingly involved in the area of finance. The high computation capability enables one to apply complicated analysis utilizing large amounts offinancial data. Big data analysis can find hidden patterns in large amounts of data.This capability can help investors in derivatives pricing, risk management and financial forecasting, and profitable trading. Owing to the high risk associated with trading financial options, this study aims to develop anintelligent option trading support system, where nonlinear or kernel canonical correlation analysis (KCCA) is used to extract the hidden forces that drive the price movement of an option, and a generalized dynamic kernel based predictors are employed to generate trading signals. 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 substantially increased. The resultant intelligent trading support system can help investors, fund managers and investment decision-makers make better and profitable decisions.
AB - Big data analysis and cloud computing are becoming increasingly involved in the area of finance. The high computation capability enables one to apply complicated analysis utilizing large amounts offinancial data. Big data analysis can find hidden patterns in large amounts of data.This capability can help investors in derivatives pricing, risk management and financial forecasting, and profitable trading. Owing to the high risk associated with trading financial options, this study aims to develop anintelligent option trading support system, where nonlinear or kernel canonical correlation analysis (KCCA) is used to extract the hidden forces that drive the price movement of an option, and a generalized dynamic kernel based predictors are employed to generate trading signals. 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 substantially increased. The resultant intelligent trading support system can help investors, fund managers and investment decision-makers make better and profitable decisions.
UR - http://www.scopus.com/inward/record.url?scp=85031304434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031304434&partnerID=8YFLogxK
U2 - 10.17549/gbfr.2017.22.3.32
DO - 10.17549/gbfr.2017.22.3.32
M3 - Article
AN - SCOPUS:85031304434
VL - 22
SP - 32
EP - 44
JO - Global Business and Finance Review
JF - Global Business and Finance Review
SN - 1088-6931
IS - 3
ER -