1 citations · 1 across the 3 of their papers we have counts for
5 papers
FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification
Baptiste Lefort, Eric Benhamou, Beatrice Guez +3
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combin…
Examining Independence in Ensemble Sentiment Analysis: A Study on the Limits of Large Language Models Using the Condorcet Jury Theorem
Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana +3
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…
Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning
Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana +2
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 learnin…
Stress index strategy enhanced with financial news sentiment analysis for the equity markets
Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana +3
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 interp…
Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana +3
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…