Machine learning and deep learning models for stock price prediction, case study: Google Company

Abstract

Stock prediction is crucial for investors to manage risk, diversify portfolios, and plan for long-term financial goals. Companies use predictions to allocate capital wisely, attract investors, and make strategic decisions. Stock prediction is to analyze competitive stock (e.g. Google, Apple and Microsoft), involving parameters of revenue, profit, PE ratio, growth rate, and to predict the future price of the stock. Based on these assumptions, this project objective is to forecast the trend in Google stock prices with four predictive models: linear regression, XGBoost, Long Short-Term Memory Network (LSTM) and Recurrent neural network (RNN). The dataset is Google stock prediction dataset, which involves 17,598 stock information. Among all four models, LSTM method obtains the best final result with 0.0012 of MSE and 0.95115 of R square. LSTM has the lowest MSE, and its R square is similar to that of other models. For future plan, this project may expand more on feature engineering, model techniques and fine-tuning methods.