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

TL;DR

This paper introduces a deep learning framework combining CNN, LSTM, and sentiment analysis of social media and candlestick data to improve stock price prediction accuracy.

Contribution

It presents a novel integration of CNN, LSTM, and sentiment analysis from social media and candlestick data for stock prediction.

Findings

Enhanced prediction accuracy through multi-faceted data integration

Effective sentiment classification of social media data

Improved modeling of short-term and long-term market trends

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 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.