Data-driven causal modelling for de-biasing sentiment analysis models and multivariate stock price movement prediction

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.