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

arXiv: https://arxiv.org/abs/2411.19766 · HTML: https://mlsanity.com/paper/2411.19766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators](https://mlsanity.com/paper/2403.03606/) (2024)

## Abstract (arXiv)

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 network 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.