Long Short-term Memory Applied on Amazon's Stock Prediction

Abstract

More and more investors are paying attention to how to use data mining technology into stock investing decisions as a result of the introduction of big data and the quick expansion of financial markets. Machine learning can automatically apply complex mathematical calculations to big data repeatedly and faster. The machine model can analyze all the factors and indicators affecting stock price and achieve high efficiency. Based on the Amazon stock price published on Kaggle, this paper adopts the Long Short-term Memory (LSTM) method for model training. The Keras package in the Python program is used to normalize the data. The Sequence model in Keras establishes a two-layer LSTM network and a three-layer LSTM network to compare and analyze the fitting effect of the model on stock prices. By calculating RMSE and RMPE, the study found that the stock price prediction accuracy of two-layer LSTM is similar to that of three-layer LSTM. In terms of F-measure and Accuracy, the LSTM model of the three-layer network is significantly better than the LSTM model of the two-layer network layer. In general, the LSTM model can accurately predict stock price. Therefore, investors will know the upward or downward trend of stock prices in advance according to the prediction results of the model to make corresponding decisions.