Decoding the News with Transformers
This thesis evaluates an ensemble-based framework for predicting stock prices using pretrained transformer models for sentiment analysis and locally trained transformers for time series forecasting. Sentiment signals were generated from financial news using FinBERT, RoBERTa, and VADER, then combined with historical stock price data from Refinitiv Eikon. To enhance label quality, a custom ensemble
