Open AccessLbl Lstm Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor
Author(s) -
Uma Gurav,
S. Kotrappa
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d8680.049420
Subject(s) - stock (firearms) , stock market , bilinear interpolation , long short term memory , econometrics , computer science , financial crisis , economics , artificial intelligence , artificial neural network , recurrent neural network , engineering , macroeconomics , mechanical engineering , paleontology , horse , computer vision , biology
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.