In this thesis, we address the stock price movement prediction problem by investigating the interdependencies between sentiments from financial news and international stock markets in stock forecasting. To provide reliable sen- timent analysis results, especially to reduce bias, we studied sentiment anal- ysis methods and detected, evaluated, and mitigated bias that was picked up on and amplified by large pre-trained models. To retrieve intra- and inter- market interdependencies, we adopt the Transfer Entropy theory to detect and incorporate the information flow between financial news sentiment and the dynamics of the stock markets. We contribute to these two sub-tasks by (i) proposing a new method for de-biasing sentiment analysis models that leverages the causal mediation analysis to identify the parts of the model primarily responsible for the bias and apply targeted counterfactual train- ing for model debiasing. Furthermore, (ii) a causal-enhanced multi-modality model for multivariate stock price movement prediction is proposed based on establishing an accurate information flow propagation between stocks and sentiments. To repeatedly validate the feasibility, the Dow Jones Industrial Average indexes of 13 countries and daily financial news data from the New York Times are used in stock Price and Return forecasting.
Da, Y 2024, 'Data-driven causal modelling for de-biasing sentiment analysis models and multivariate stock price movement prediction', Vrije Universiteit Brussel, Brussels.
Da, Y. (2024). Data-driven causal modelling for de-biasing sentiment analysis models and multivariate stock price movement prediction. [PhD Thesis, Vrije Universiteit Brussel]. Crazy Copy Center Productions.
@phdthesis{8b0d6fe712e147829fd909bee4987e47,
title = "Data-driven causal modelling for de-biasing sentiment analysis models and multivariate stock price movement prediction",
abstract = "In this thesis, we address the stock price movement prediction problem by investigating the interdependencies between sentiments from financial news and international stock markets in stock forecasting. To provide reliable sen- timent analysis results, especially to reduce bias, we studied sentiment anal- ysis methods and detected, evaluated, and mitigated bias that was picked up on and amplified by large pre-trained models. To retrieve intra- and inter- market interdependencies, we adopt the Transfer Entropy theory to detect and incorporate the information flow between financial news sentiment and the dynamics of the stock markets. We contribute to these two sub-tasks by (i) proposing a new method for de-biasing sentiment analysis models that leverages the causal mediation analysis to identify the parts of the model primarily responsible for the bias and apply targeted counterfactual train- ing for model debiasing. Furthermore, (ii) a causal-enhanced multi-modality model for multivariate stock price movement prediction is proposed based on establishing an accurate information flow propagation between stocks and sentiments. To repeatedly validate the feasibility, the Dow Jones Industrial Average indexes of 13 countries and daily financial news data from the New York Times are used in stock Price and Return forecasting.",
author = "Yifei Da",
year = "2024",
language = "English",
isbn = "9789464948288",
publisher = "Crazy Copy Center Productions",
address = "Belgium",
school = "Vrije Universiteit Brussel",
}