ChatGPT-generated Financial News Sentiment and NASDAQ Returns

Can ChatGPT extract market sentiment from financial news that is useful for timing equity markets? In their April 2024 paper entitled “Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot use ChatGPT to assess whether daily Bloomberg Global Markets Wrap, Market Talks and Morning Reports anticipate NASDAQ returns. Specifically, they each day:

• Ask ChatGPT to identify important news themes and characterize them as headlines.
• Ask ChatGPT to assess whether each headline is positive, negative or neutral for future stock prices.
• Compute a sentiment score that combines sentiments for all daily headlines.
• Compute a daily cumulative sentiment score (C) for the last 20 trading days.
• Compute a daily detrended cumulative sentiment score (DC) by comparing C to its value over the last 20 trading days (extending the overall lookback interval to 40 days).
• If C is positive (negative), take a long (short) position in the NASDAQ index with a 2-day lag to ensure executability and a debit of 0.2% trading frictions for position changes. Repeat this evolution for DC.

They separately examine cumulative performances of the long, short and overall returns of the C and DC variations of this strategy, focusing on Sharpe, Sortino and Calmar ratios as key performance metrics. Their benchmark is buying and holding the NASDAQ index. Using the specified daily financial news sources and daily NASDAQ index returns during 2010 through 2023, they find that:

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