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Stock market random forest-text mining system mining critical indicators of stock market movements

Elagamy, Mazen Nabil, STANIER, Clare and SHARP, Bernadette (2018) Stock market random forest-text mining system mining critical indicators of stock market movements. In: 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). IEEE Computer Society, pp. 1-8.

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Abstract or description

Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: Presented at 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), Algiers, 2018. “© © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Library STORE team
Date Deposited: 01 Aug 2018 13:12
Last Modified: 06 Aug 2018 10:02
URI: http://eprints.staffs.ac.uk/id/eprint/4624

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