Predicting Apple’s Stock Price with LSTM

In the capital market, accurate forecasting of stock prices is important for investors’ investment decisions as well as risk management. As the technology company with the highest market capitalization in the world, the movement of Apple’s stock price is highly concerned. However, traditional forecasting methods are often difficult to apply to the current increasingly complex capital market, thus making it difficult to capture the complex dynamics of stock price changes. In this study, a time-series analysis is performed on a dataset containing Apple’s stock price for the past eleven years to capture its long-term dependence by introducing a long-short-term memory model (LSTM). After experimental verification, the model performs well in terms of the Mean Squared Error and other indicators, and can reflect Apple’s stock price trend more accurately, which provides investors with a forecasting basis with practical reference value, and also provides a new modeling perspective and methodology for stock market time series forecasting research.