Stock Price Prediction Using LSTM Neural Networks

Introduction

Stock price prediction is a challenging yet crucial task in the financial sector, aiming to forecast future stock prices based on historical data. This project proposal explores the use of Long Short-Term Memory (LSTM) neural networks, which are well-suited for capturing long-term dependencies in time series data, to enhance the accuracy of stock price predictions.

Background

Recent advancements in deep learning have significantly improved the ability to predict stock market trends. LSTM networks, a type of recurrent neural network (RNN), are particularly effective for time series forecasting due to their ability to remember information over extended periods. This capability makes them ideal for analyzing historical stock price data and predicting future trends.

Project Objective

The primary objective of this project is to develop a robust stock price prediction model using LSTM neural networks. The model aims to improve prediction accuracy by leveraging historical stock data and optimizing LSTM architecture and hyperparameters.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets containing historical stock prices, such as those available on Kaggle.
• Data Preprocessing: Clean and normalize the data, handle missing values, and split it into training and testing sets.

2. Model Architecture

• LSTM Network: Implement an LSTM-based model with multiple layers to capture complex patterns in the data.
• Hyperparameter Tuning: Optimize parameters such as the number of layers, number of neurons per layer, learning rate, and dropout rate to prevent overfitting.

3. Training and Evaluation

• Training: Use techniques like backpropagation and gradient descent to train the model on the training dataset.
• Evaluation Metrics: Assess model performance using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

Expected Outcomes

The proposed LSTM-based system is expected to achieve higher accuracy in predicting stock prices compared to traditional methods. By effectively modeling temporal dependencies in stock data, the system should provide valuable insights for making informed investment decisions.

Conclusion

This project aims to advance the field of stock price prediction by developing a state-of-the-art system using LSTM neural networks. The integration of advanced deep learning techniques is anticipated to yield significant improvements in forecasting accuracy.

For further details on related research, please refer to the paper "Deep Learning for Stock Market Prediction Using Technical Indicators and Financial News Articles," available at IEEE Xplore.