10.57647/j.spre.2025.0901.02

Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks

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  • Lida Shahbandari1,
  • Elahe Moradi*2,
  • Mohammad Manthouri3,
  1. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
  2. Department of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
  3. Department of Electrical Engineering and Electronic Engineering, Shahed University, Tehran, Iran

Received: 2024-10-09

Revised: 2024-11-22

Accepted: 2024-11-24

Published 2025-03-01

Copyright (c) 2025 Lida Shahbandari, Elahe Moradi, Mohammad Manthouri (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

How to Cite

Shahbandari, L. ., Moradi, E., & Manthouri, M. . (2025). Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks. Signal Processing and Renewable Energy (SPRE), 9(1 (March 2025). https://doi.org/10.57647/j.spre.2025.0901.02
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Abstract

Accurate prediction of stock market trends is crucial for informed investment decisions and effective portfolio management, ultimately leading to enhanced wealth creation and risk mitigation. This study proposes a novel approach for predicting stock prices in the stock market by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, using sentiment analysis of social network data and candlestick data (price). The proposed methodology consists of two primary components: sentiment analysis of social networks and candlestick data. By amalgamating candlestick data with insights gleaned from Twitter, this approach facilitates a more detailed and accurate examination of market trends and patterns, ultimately leading to more effective stock price predictions. Additionally, a Random Forest algorithm is used to classify tweets as either positive or negative, allowing for a more subtle and informed assessment of market sentiment. This study uses CNN and LSTM networks to predict stock prices. The CNN extracts short-term features, while the LSTM models long-term dependencies. The integration of both networks enables a more comprehensive analysis of market trends and patterns, leading to more accurate stock price predictions.

Keywords

  • Stock Price Prediction, Deep Learning, Sentiment Analysis, Long Short-Term Memory, Convolutional Neural Network
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