BERT’s sentiment score for portfolio optimization: a fine-tuned view in Black and Litterman model

In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) w...

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Bibliographic Details
Published in:Neural computing & applications 2022, Vol.34 (20), p.17507-17521
Main Authors: Colasanto, Francesco, Grilli, Luca, Santoro, Domenico, Villani, Giovanni
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data mining
Data Mining and Knowledge Discovery
Economic forecasting
Image Processing and Computer Vision
International finance
Mathematical models
Monte Carlo simulation
Optimization
Original
Original Article
Probability and Statistics in Computer Science
Sentences
Sentiment analysis
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Summary:In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new “view” in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of “dynamic” portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07403-1