Papers by Emmanuel Hauptmann
Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Quantitative investing relies on extracting quantitative features or signals from various data sources including market prices, economic indicators, financial text, etc. |
| Approach: | They propose to integrate LLMs’ token-level embeddings into a forecasting module and compare their results to those of encoder-only and decoder-based models. |
| Outcome: | The proposed model outperforms conventional sentiment scores on multiple investment universes and is based on encoder-only and decoder-based models. |