Comparison of Random Forest and LSTM in Stock Prediction

As an integral component of the financial market, stock prices have attracted the attention of many investors. Due to the frequent fluctuations and sensitivity to market dynamics, predicting stock prices is challenging. The volatility of stock prices and potential significant differences across different periods add to the difficulty of forecasting and reduce its accuracy. The Random Forest model and the LSTM model, as representative models in decision trees and deep learning algorithms respectively, demonstrate high accuracy and adaptability in predicting stock prices. The paper will separately utilize the Random Forest model and the LSTM model to fit the S&P 500 price data from 2013 to 2018 (represented by Apple's stock prices) as training and testing sets, and then compare the fitting results of the two models. The conclusion is as follows: In the absence of white noise in the data, the Random Forest model demonstrates smaller biases in predicting data compared to the LSTM model, and it can also respond more swiftly to price fluctuations.