Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ

Analyse du sentiment de l'information diffusée par Bloomberg Markets à l'aide de ChatGPT: Application au NASDAQ

Baptiste Lefort (), Eric Benhamou (), Jean-Jacques Ohana, David Saltiel (), Beatrice Guez and Thomas Jacquot ()
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Baptiste Lefort: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay, A.I. For Alpha
Eric Benhamou: A.I. For Alpha
Jean-Jacques Ohana: A.I. For Alpha
David Saltiel: A.I. For Alpha
Beatrice Guez: A.I. For Alpha
Thomas Jacquot: A.I. For Alpha

Working Papers from HAL

Abstract: In this paper, we use a comprehensive dataset of daily Bloomberg Financial Market Summaries spanning from 2010 to 2023, published by Yahoo Finance, CNews and multiple large financial medias, to determine how global news headlines may affect stock market movements. To make this analysis more effective, we employed ChatGPT. First, from the vast pool of daily financial updates, we identify the top global news headlines that could potentially have a significant influence on stock prices. Second, for each headline, we question ChatGPT to answer whether the news might lead to a rise, fall in stock prices or is indecisive. This two-stage method proves more effective than posing a direct question to the entire text. By gathering ChatGPT's predictions day by day, we formed an overall market sentiment score. We transform this score into a practical investment strategy in the NASDAQ index, demonstrating the significance of minimizing noise in sentiment scores by initially accumulating and then detrending them. This approach showcases that ChatGPT's analysis of news headlines can provide valuable insights into future stock market behaviors and be a valuable tool to develop intuitive NLP-driven investment strategies leveraging news predictive power.

Keywords: bloomberg; sentiment analysis; ChatGPT; stock exchange; financial news (search for similar items in EconPapers)
Date: 2024-10-16
Note: View the original document on HAL open archive server: https://hal.science/hal-04739924v1
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