Open Access

ARTICLE

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Manish Agrawal¹, Piyush Kumar Shukla², Rajit Nair³, Anand Nayyar^4,5,*, Mehedi Masud⁶

1 Department of Computer Science & Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal-462033, Madhya Pradesh, India
2 Faculty of Department of Computer Science & Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal-462033, Madhya Pradesh, India
3 Faculty of Department of Computer Science & Engineering, School of Engineering and Technology, Jagran Lakecity University, Bhopal, 462026, Madhya Pradesh, India
4 Graduate School, Duy Tan University, Da Nang, 550000, Vietnam
5 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam
6 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Anand Nayyar. Email:

Computers, Materials & Continua 2022, 70(1), 287-304. https://doi.org/10.32604/cmc.2022.014637

Received 05 October 2020; Accepted 12 May 2021; Issue published 07 September 2021

Abstract

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 of Nov 2008 to the 15 of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%, and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

---

Keywords

---

Citations