1 citations · 2 across the 8 of their papers we have counts for
10 papers
E-TRENDS: Enhanced LSTM Trend Forecasting for Equities
Harris Buchanan, Eric Benhamou
Trend-following strategies underpin many systematic trading approaches yet struggle under nonstationary and nonlinear market regimes. We propose an LSTM-based framework to forecast…
Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy
Alban Etienne, Jean-Jacques Ohana, Eric Benhamou +3
Recent work has emphasized the diversification benefits of combining trend signals across multiple horizons, with the medium-term window-typically six months to one year-long viewe…
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…
HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
Benjamin Coriat, Eric Benhamou
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combin…
Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach
Eric Benhamou, Jean-Jacques Ohana, Alban Etienne +3
Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directiona…
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…
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting
Eric Benhamou, David Saltiel, Serge Tabachnik +2
Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial mar…