  Forecasting financial time series (FTS) is an essential field in finance and
economics that anticipates market movements in financial markets. This paper
investigates the accuracy of text mining and technical analyses in forecasting
financial time series. It focuses on the S&P500 stock market index during the
pandemic, which tracks the performance of the largest publicly traded companies
in the US. The study compares two methods of forecasting the future price of
the S&P500: text mining, which uses NLP techniques to extract meaningful
insights from financial news, and technical analysis, which uses historical
price and volume data to make predictions. The study examines the advantages
and limitations of both methods and analyze their performance in predicting the
S&P500. The FinBERT model outperforms other models in terms of S&P500 price
prediction, as evidenced by its lower RMSE value, and has the potential to
revolutionize financial analysis and prediction using financial news data.
Keywords: ARIMA, BERT, FinBERT, Forecasting Financial Time Series, GARCH, LSTM,
Technical Analysis, Text Mining JEL classifications: G4, C8
