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pro vyhledávání: '"Lefort, Baptiste"'
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Guez, Beatrice, Saltiel, David, Jacquot, Thomas
This paper explores the application of the Condorcet Jury theorem to the domain of sentiment analysis, specifically examining the performance of various large language models (LLMs) compared to simpler natural language processing (NLP) models. The th
Externí odkaz:
http://arxiv.org/abs/2409.00094
In this paper, we demonstrate that non-generative, small-sized models such as FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4 models in zero-shot learning settings in sentiment analysis for financial news. These fine-tuned
Externí odkaz:
http://arxiv.org/abs/2409.11408
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Saltiel, David, Guez, Beatrice, Jacquot, Thomas
This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress de
Externí odkaz:
http://arxiv.org/abs/2404.00012
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Saltiel, David, Guez, Beatrice, Challet, Damien
We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document
Externí odkaz:
http://arxiv.org/abs/2401.05447