Speaker: Dr. Kemal Kırtaç

https://scholar.google.com/citations?user=FxUzM20AAAAJ&hl=en

Title: Leveraging LLM-based sentiment analysis for stock portfolio optimization

Abstract: Large Language Models (LLMs) are transforming financial sentiment analysis by capturing contextual, emotional, and semantic nuances that traditional dictionary-based methods cannot detect. This presentation introduces an end-to-end framework for sentiment-driven trading using advanced transformer models, including BERT, FinBERT, OPT, and LLaMA-3, fine-tuned on nearly one million firm-specific financial news articles from Refinitiv. After fine-tuning the models on 3-day abnormal returns, I evaluate their predictive accuracy, econometric significance, and trading performance across long, short, and long–short portfolios. Models such as OPT and BERT consistently outperform dictionary baselines, delivering the highest classification accuracy, strongest predictive coefficients for next-day returns, and robust long–short Sharpe ratios under realistic market frictions. I further integrate LLM-derived sentiment into a reinforcement-learning portfolio strategy—Sentiment-Augmented PPO (SAPPO)—which improves risk-adjusted performance relative to standard PPO and benchmark equity indices. The results demonstrate that LLM-based sentiment signals can enhance market prediction, support systematic trading, and strengthen reinforcement-learning policy optimization. The presentation concludes with implications for quant research, model interpretability, and cross-market generalization, highlighting LLMs as a scalable and powerful component of modern investment pipelines.