A big data analysis system for financial trading

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)32-44
Number of pages13
JournalGlobal Business and Finance Review
Volume22
Issue number3
DOIs
Publication statusPublished - 2017 Jan 1

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Investors
Kernel
Derivative pricing
Financial options
Financial forecasting
Profit
Predictors
Cloud computing
Finance
Feature extraction
Performance improvement
Investment decision
Option trading
Canonical correlation analysis
Risk management
Decision maker
Regression model
Fund managers

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Finance

Cite this

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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 journalArticle

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