Data mining driven computational analysis of stock markets, methods and strategies
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Authors
Lai, Anthony
Phang, Shaoning
Holmes, Wayne
Phang, Shaoning
Holmes, Wayne
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Grantor
Date
2015-12
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Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
stock markets
computational correlation analysis
cross correlation
auto correlation
forecasts
computational correlation analysis
cross correlation
auto correlation
forecasts
ANZSRC Field of Research Code (2020)
Citation
Lai, A., Phang, S., & Holmes, W. (2015, December). Data Mining Driven Computational Analysis of Stock Markets, Methods and Strategies. In ECBA (Ed.), Paper presented at Engineering and Technology, Computer, Basic & Applied International Conference (pp.106-113).
Abstract
The stock market is a complex, dynamic and non-linear environment. The prediction of any future market reaction is further complicated by huge amounts of often unstructured financial data and uncertainty due to the effects of unforeseen market events. The application of correlation analysis to significant market events is still seen as a useful tool in the prediction of future trends on the stock market in a global sense.
This paper proposes the application of data mining computation correlation analysis to the stock market to enhance the durability of predictions. This method of correlation analysis is a combination of cross correlation, auto correlation and fundamental analysis that is further enhanced by Channel correlation, Weighted Pearson’s correlation and added correlation Support Vector Regression. Channel correlation traces the similarity of trends while the weighted Pearson’s correlation acts as a noise filter during the correlation extraction process
Publisher
Academic Fora (Kuala Lumpur, Malaysia)
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Academic Fora (Kuala Lumpur, Malaysia)
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