UCLA

This paper presents the idea of predicting Apple stock price using three different types of time series models based on many features including news headlines related to Apple. To collect data on if the news headlines have positive, neutral, or negative information, we used LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), BERT (Bidirectional Encoder Representations from Transformers) Sentimental Analysis, and BERT Fine-Tuning. The BERT Fine-Tuning model has the best result. For Apple stock price forecasting we used the classical time series models, ARIMA (Autoregressive Integrated Moving Average) and SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors), the supervised models, linear regression using PCA (Principal Component Analysis) and random forest, and the deep learning model, LSTM. The linear regression model using PCA performed the best. For further investigation, we could change the parameters of the LSTM model to get better results.