Stock Market Prediction Model using LSTM

Abstract: The stock market is a complex and dynamic financial system that constantly undergoes fluctuations driven by various economic, political, and social factors. Accurate prediction of stock prices is a challenging yet essential task for investors, traders, and financial analysts. The objective of this project is to design and implement an LSTM-based stock market prediction system that leverages historical market data to forecast future stock prices. The methodology involves the creation of a deep learning model leveraging LSTM layers for temporal dependency modeling, Dense layers for feature extraction, Dropout layers for regularization, and a Sequential architecture for seamless integration. The model is trained on historical stock data, incorporating essential features such as past prices, trading volumes, and market indicators. The model's performance is rigorously evaluated through back testing and out-of-sample testing using historical stock data. The results indicate the model's ability to make predictions that outperform traditional buy-and-hold strategies. This research contributes to the ongoing efforts to harness the power of machine learning and artificial intelligence in predicting stock market trends and demonstrates the potential for improved decision-making in the financial industry. It is hoped that this model will serve as a valuable resource for market participants seeking to navigate the complexities of the stock market.