Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-9 Issue-4, April, 2020

7 Pages Posted: 5 Oct 2020

See all articles by Uma Gurav

Uma Gurav

K.I.T's college of Engineering

Dr. S. Kotrappa

KLE Dr. M.S.S CET

Date Written: 2020

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

JEL Classification: C22,C32,C61,G14,C02, B26, G12, G14, C58, N20

Suggested Citation

GURAV, UMA and Kotrappa, Dr. S., Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor (2020). International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-9 Issue-4, April, 2020, Available at SSRN: https://ssrn.com/abstract=3697046

UMA GURAV (Contact Author)

K.I.T's college of Engineering ( email )

Gokul Shirgaon
Kolhapur, MA Maharashtra 416008
India

Dr. S. Kotrappa

KLE Dr. M.S.S CET ( email )

Belagavi
India

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