AlBERTino for stock price prediction: a Gibbs sampling approach

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作   者：

Colasanto, Francesco;  Grilli, Luca;  Santoro, Domenico;  Villani, Giovanni; 

作者机构：

Scuola Internazl Super Studi Avanzati SISSA;  Univ Bari Aldo Moro;  Univ Foggia; 

关键词：

Sentiment analysis;  Price forecasting;  MARKETS;  SENTIMENT ANALYSIS;  RETURNS;  MCMC;  INVESTOR SENTIMENT;  MEDIA;  BERT;  Stock market; 

期刊名称：

Information Sciences: An International Journal

i s s n：

0020-0255

年卷期：

2022 年 597 卷

页   码：

341-357

页   码：

摘   要：

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.(c) 2022 Elsevier Inc. All rights reserved.