AlBERTino for stock price prediction: a Gibbs sampling approach

• Published in: Information sciences 2022-06, Vol.597, p.341-357
• Main Authors: Colasanto, Francesco, Grilli, Luca, Santoro, Domenico, Villani, Giovanni
• Format: Article
• Language: English
• Subjects: BERT; MCMC; Price forecasting; Sentiment analysis; Stock market
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2022.03.051

•An Italian BERT model (AlBERTo) has been fine-tuned on financial sentences.•AlBERTino can determine the sentiment score of news present in financial newspapers.•The sentiment score is used to drive the parameters of GBM through a MCMC.•The average of Monte Carlo simulation paths is the predicted stock value. BERT (Bidirectional Encoder Representations from Transformers) is one of the most popular models in Natural Language Processing (NLP) for Sentiment Analysis. The main goal is to classify sentences (or entire texts) and to obtain a score in relation to their polarity: positive, negative or neutral. Recently, a Transformer-based architecture, the fine-tuned AlBERTo (Polignano et al. (2019)), has been introduced to determine a sentiment score in the financial sector through a specialized corpus of sentences. In this paper, we use the sentiment (polarity) score to improve the stocks forecasting. We apply the BERT model to determine the score associated to various events (both positive and negative) that have affected some stocks in the market. The sentences used to determine the scores are newspaper articles published on MilanoFinanza. We compute both the average sentiment score and the polarity, and we use a Monte Carlo method to generate (starting from the day the article was released) a series of possible paths for the next trading days, exploiting the Bayesian inference to determine a new series of bounded drift and volatility values on the basis of the score; thus, returning an exact “directed” price as a result.