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Text Mining Approach to Analyse Stock Market Movement.

Elagamy, M.N., STANIER, Clare and SHARP, Bernadette (2018) Text Mining Approach to Analyse Stock Market Movement. In: Proceedings of the The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). Springer, pp. 661-670. ISBN 978-3-319-74690-6

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Abstract or description

Stock Market (SM) is a significant sector of countries’ economy and represents a crucial role in the growth of their commerce and industry. Hence, discovering efficient ways to analyse and visualise stock market data is considered a significant issue in modern finance. The use of Data Mining (DM) techniques to predict stock market has been extensively studied using historical market prices but such approaches are constrained to make assessments within the scope of existing information, and thus they are not able to model any random behaviour of stock market or provide causes behind events. One area of limited success in stock market prediction comes from textual data, which is a rich source of information and analysing it may provide better understanding of random behaviours of the market. Text Mining (TM) combined with Random Forest (RF) algorithm offers a novel approach to study critical indicators, which contribute to the prediction of stock market abnormal movements. A Stock Market Random Forest-Text Mining system (SMRF-TM) is developed to mine the critical indicators related to the 2009 Dubai stock market debt standstill. Random forest is applied to classify the extracted features into a set of semantic classes, thus extending current approaches from three to eight classes: critical down, down, neutral, up, critical up, economic, social and political. The study demonstrates that Random Forest has outperformed the other classifiers and has achieved the best accuracy in classifying the bigram features extracted from the corpus.

Item Type: Book Chapter, Section or Conference Proceeding
Faculty: Previous Faculty of Computing, Engineering and Sciences > Computing
Depositing User: Bernadette SHARP
Date Deposited: 19 Mar 2018 10:55
Last Modified: 16 May 2018 08:51
URI: http://eprints.staffs.ac.uk/id/eprint/4259

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