Stock Market Forecasting using Network Analysis and LSTM Network

Key Idea of the Project

The goal of this project is to develop an accurate predictive model for stock prices using a combination of network analysis and Long Short-Term Memory (LSTM) network.

Accurately predicting stock prices is crucial for investors and traders to make informed decisions and manage risks effectively. Traditional methods often rely on technical analysis and historical data. However, by leveraging the power of neural networks and network analysis, we aim to create a more robust and accurate predictive model.

Similar approaches combining network analysis and neural networks have been used in various domains, including social network analysis, recommendation systems, and fraud detection. In recent years, the application of such techniques to financial data analysis, including stock price prediction, has gained attention.

To achieve our goal, we plan to follow these steps:

1. Data Collection: Gather historical stock price data from reputable financial data sources;
2. Network Analysis: Construct a network representation of the stock data to capture relationships and dependencies;
3. Model Development: Develop and train the LSTM network using the processed data;
4. Model Optimization: Optimize the model to ensure it generalizes well to new data, avoiding overfitting and underfitting;
5. Model Evaluation: Evaluate the model using appropriate metrics to ensure accuracy and reliability.

Dataset Required to Do the Project

Historical stock price data will be collected from reputable financial data sources such as Yahoo Finance, Alpha Vantage, etc. The dataset should include features such as opening price, closing price, highest price, lowest price, and trading volume for a specific time period. Data should cover a sufficiently long time frame to capture various market conditions and trends. Data quality is essential for reliable predictions. Therefore, we will ensure that the data is accurate and free from errors.

Anticipated Methods

• Network Analysis: Build a network representation of the stock price data to capture relationships and dependencies among different stocks. This approach allows us to represent the data in a structured format suitable for network analysis.

• Long Short-Term Memory (LSTM) Network: Develop and train an LSTM network to capture temporal dependencies in the data and make predictions based on historical trends. LSTMs are well-suited for capturing temporal dependencies in sequential data, making them ideal for analyzing time series data like stock prices.

• Model Optimization: Optimize the model during the training process to ensure it generalizes well to new data.

• Model Evaluation: Evaluate the performance of the LSTM network using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²) to ensure the accuracy and reliability of the predictions.

By integrating network analysis with LSTM network and implementing robust evaluation methods, we aim to create a comprehensive and accurate predictive model for stock market forecasting.