Stock Market Prediction using Artificial Neural Network & Text Mining
Jibendu Kumar Mantri1, Amiya Kumar Sahoo2, Sipali Pradhan3, Debabrat Dehury4
1Dr. Jibendu Kumar Mantri*, Associate Professor, PG Department of Computer Application, North Orissa University, Baripada, India.
2Amiya Kumar Sahoo, Senior Lecturer in the Department of Computer Science and Engineering in Aryan College of Engineering, Bhubaneswar.
3Dr. Sipali Pradhan, Department Computer Science and IT from North Orissa University, Baripada, Odisha.
4Debabrat Dehury, M. Phil. Scholar, Department of Computer Applications, North Orissa University, Baripada, Odisha.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4040-4043 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6624018520/2020©BEIESP | DOI: 10.35940/ijrte.E6624.018520
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The art of prediction of stock market volatility has always been a most challenged interdisciplinary research problem among scientist due to its highly non- linear nature of market flow. This paper tries to analysis the historical data of BSE Sensex using extreme volatilities estimators, GARCH, ANN and new proposed Text Mining approach for stock market predictions. Finally experimental results illustrates that the new proposed Text model can able to predict the volatilities of the stock price better than other models.
Keywords: MLP, GARCH, Text Mining.
Scope of the Article: Text Mining.