On the Impact of News for Reliable Stock Market Predictions: An LSTM-based Ensemble using FinBERT Word-Embeddings

Abstract

Stock market (SM) prediction methods can be divided into two categories based on the number of information sources used: single-source methods and dual-source approaches. To estimate the price of a stock, single-source approaches rely solely on numerical data. The Efficient Market Hypothesis (EMH), [1]. States that the stock price will represent all important information. Different sources of information might complement one another and influence the stock price. Machine learning and deep learning techniques have long been used to anticipate stock market movements, [2], [3]. The researcher gathered the dataset, [4], [5], [6], [7]. The dataset contains the date of the reading, the opening price, the high and low value of the stock, news about the stock, and the volume. The researcher uses a variety of machine Learning and deep learning approaches to compare performance and prediction error rates, in addition, the researcher also compared the effect of adding the news text as a feature and as a label model. and using a dedicated model for news sentiment analysis by applying the FinBERT word embedding and using them to construct a Long Short-Term Memory (LSTM). From our observation, it is evident that Deep learning-based models performed better than their Machine learning counterparts. The author shows that information extracted from news sources is better at predicting rather than its direction of price movement. And the best-performing model without news is the LSTM with an RMSE of 0.0259 while the best-performing model with news is the LSTM with a stand-alone and LSTM model for news yields RMSE of 0.0220.