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A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators

A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators

Shruti Mittal, Anubhav Chauhan
Copyright: © 2021 |Volume: 7 |Issue: 1 |Pages: 13
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781799808275|DOI: 10.4018/IJRSDA.288521
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MLA

Mittal, Shruti, and Anubhav Chauhan. "A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators." IJRSDA vol.7, no.1 2021: pp.1-13. http://doi.org/10.4018/IJRSDA.288521

APA

Mittal, S. & Chauhan, A. (2021). A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators. International Journal of Rough Sets and Data Analysis (IJRSDA), 7(1), 1-13. http://doi.org/10.4018/IJRSDA.288521

Chicago

Mittal, Shruti, and Anubhav Chauhan. "A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators," International Journal of Rough Sets and Data Analysis (IJRSDA) 7, no.1: 1-13. http://doi.org/10.4018/IJRSDA.288521

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Abstract

The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the stock market to predict the future prices of the stock using Recurrent Neural Network based Long Short-Term Memory (LSTM) algorithm. The historical transactional data set is amalgamated with the technical indicators to create a more effective input dataset. The historical data is taken from 2010-2019 ten years in total. The dataset is divided into 80% training set and 20% test set. The experiment is carried out in two phases first without the technical indicators and after adding technical indicators. In the experimental setup, it has been observed the LSTM with technical indicators have significantly reduced the error value by 2.42% and improved the overall performance of the system as compared to other machine learning frameworks that are not accounting the effect of technical indicators.