On May 27th 2024 at 16:00, Yifei Da will defend their PhD entitled “DATA-DRIVEN CAUSAL MODELLING FOR DE-BIASING SENTIMENT ANALYSIS MODELS AND MULTIVARIATE STOCK PRICE MOVEMENT PREDICTION”.

Everybody is invited to attend the presentation in room I.0.01, or digitally via this link.

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 sentiment analysis results, especially to reduce bias, we studied sentiment analysis 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 debiasing 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 training 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.