Summary of Stock Price Prediction Using Multi-faceted Information Based on Deep Recurrent Neural Networks, by Lida Shahbandari et al.
Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks
by Lida Shahbandari, Elahe Moradi, Mohammad Manthouri
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict stock prices by combining sentiment analysis of social network data with candlestick data. The approach uses a Random Forest algorithm to classify tweets as positive or negative, allowing for a more subtle assessment of market sentiment. CNN extracts short-term features while LSTM models long-term dependencies, enabling a comprehensive analysis of market trends and patterns. This study aims to provide accurate stock price predictions for informed investment decisions and effective portfolio management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict the stock market better by combining two ways of analyzing data: social media and candlestick charts. It uses special kinds of computer models called CNNs and LSTMs to look at this data in a new way. By combining these different types of analysis, we can get a more complete picture of what’s happening in the stock market and make better predictions about where prices will go. |
Keywords
» Artificial intelligence » Cnn » Lstm » Random forest
