Abstract
Large language models (LLMs) have demonstrated remarkable success in the field of natural language processing (NLP). Despite their origins in NLP, these algorithms possess the theoretical capability to process any data type represented in an NLP-like format. In this study, we use stock data to illustrate three methodologies for processing regression data with LLMs, employing tokenization and contextualized embeddings. By leveraging the well-known LLM algorithm Bidirectional Encoder Representations from Transformers (BERT) [1], we apply quantitative stock price prediction methodologies to predict stock prices and stock price movements, showcasing the versatility and potential of LLMs in financial data analysis.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications |
| Place of Publication | United States |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798331505790 |
| ISBN (Print) | 9798331505806 |
| DOIs | |
| Publication status | Published - 11 Feb 2025 |
| Event | 6th International Conference on Cybernetics, Cognition and Machine Learning Applications - Hamburg, Germany Duration: 19 Oct 2024 → 20 Oct 2024 |
Conference
| Conference | 6th International Conference on Cybernetics, Cognition and Machine Learning Applications |
|---|---|
| Abbreviated title | ICCCMLA 2024 |
| Country/Territory | Germany |
| City | Hamburg |
| Period | 19/10/24 → 20/10/24 |
Keywords
- finance
- quantitative stock price prediction
- natural language processing
- stock movement prediction
- fintech
- machine learning
- large language models