Effectiveness Validation of LSTM for Stock Prices Prediction on Four Stocks

Abstract

Due to the significant volatility and complexity of financial data, stock price prediction is a difficult undertaking. Researchers have also begun using various models to predict stock prices. LSTM (Long short-term memory) are used in this study to predict the stock prices of four companies between 2013 and 2019. The author compares the performance of two loss functions, MSE (Mean squared error) and MAE (Mean absolute error), and evaluate the effectiveness of the proposed approach. The experiments show that LSTM is a promising model for stock price prediction, and the data's long-term dependencies can be captured by it. The predicted stock price curves closely match the real data curves, although there are some limitations in predicting the changes in stock prices. Furthermore, the study demonstrates that the choice of loss function has a significant impact on the prediction results.As a result, it's crucial to choose the loss function carefully depending on the dataset's features. Overall, the research shows that LSTM and other machine learning models have great potential for stock price prediction. Yet it's crucial to remember that the stock market is incredibly volatile and uncertain, and these models should be used as complementary tools to assist human experts in making investment decisions.