Stock Market Predictions Using Moving Average and LSTM Techniques

This research aims to predict stock market movements by combining Long Short-Term Memory (LSTM) deep learning with moving average analysis. The goal is to create a reliable model for forecasting stock prices by leveraging historical data. Due to the stock market's complexity and volatility, specialized methods are needed to capture patterns accurately. Moving average indicators are used to identify trends and reduce price fluctuations. The model effectively analyzes time-series data, capturing long-term relationships in stock prices using LSTM, a type of recurrent neural network. LSTM allows the model to learn from extensive historical data, crucial for handling large-scale stock market datasets spanning years. Performance is assessed using measures like mean squared error and accuracy, and the model's predictions are rigorously compared to actual stock prices. The research demonstrates the effectiveness of this strategy, providing valuable insights for traders and investors in the unpredictable stock market. The combination of moving average analysis and LSTM-based deep learning yields promising results, opening new avenues for financial market applications.