RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2020

7 Pages Posted: 21 Sep 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

Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.

Note: This is an Accepted Manuscript of an article published by Blue Eyes Intelligence Engineering & Sciences Publication in International Journal of Innovative Technology and Exploring Engineering on February 2020, available online: 10.35940/ijitee.D1555.029420

Keywords: Recurrent Neural Networks, Long Short Term Memory, Log Bi-linear Model, Machine Learning, Prediction System, Stock Price Forecasting, Time Series

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

Suggested Citation

GURAV, UMA and Kotrappa, Dr. S., RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model (2020). International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2020, Available at SSRN: https://ssrn.com/abstract=3691450

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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