Lbl – Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor
Uma Gurav1, S. Kotrappa2
1Uma Gurav*, Assistant Professor, Department of CSE, K.I.T’s College of Engineering, Kolhapur.
2S. Kotrappa, Professor, Department of CSE, K L E’s Dr MSS College of Engineering & Technology, Belgaum.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 2057-2063 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8680049420/2020©BEIESP | DOI: 10.35940/ijeat.D8680.049420
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© 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: Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank’s lending rates. The external factor bank’s lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.
Keywords: Data Mining, Artificial Intelligence, Stock Market Prediction, Long -Short Term Memory, Machine Learning Algorithms.