ChatGPT-3.5 predicts stock market movements using the positive news ratio (NRG).
Investors react slowly to positive news, while negative news is priced in immediately.
Prediction accuracy improves during economic uncertainty.
DeepSeek and BERT perform worse due to training limitations.
ChatGPT’s effectiveness depends on prompt design and data period.
Opinion
This study suggests that LLMs like ChatGPT can serve as useful tools for financial forecasting, particularly by identifying inefficiencies in investor sentiment processing.
Delayed reactions to positive news align with behavioral finance theories like loss aversion and information overload.
During economic uncertainty, ChatGPT’s insights become even more valuable, as traditional investors struggle with information complexity.
The underperformance of DeepSeek and BERT highlights the importance of AI training data, reinforcing ChatGPT’s advantage in financial news analysis.
Core Sell Point
ChatGPT effectively captures delayed investor reactions to positive news, making it a valuable tool for stock market prediction—especially in times of economic uncertainty.
A recent study, ChatGPT and DeepSeek: Can They Predict the Stock Market and Macroeconomy?, investigates whether large language models (LLMs) such as ChatGPT-3.5 and DeepSeek-R1 can predict stock market returns and macroeconomic trends by analyzing Wall Street Journal (WSJ) headlines.
Key findings reveal that ChatGPT-3.5 demonstrates predictive power by capturing delayed investor reactions to positive news, whereas DeepSeek and smaller models like BERT show weaker forecasting ability due to their training limitations.
Methodology
Data Collection: Headlines from WSJ front-page articles (1996–2022).
Sentiment Analysis: ChatGPT-3.5 and DeepSeek-R1 classify headlines as positive, neutral, or negative.
Regression Analysis:
Monthly positive news ratio (NRG) and negative news ratio (NRB) are used as independent variables.
Stock market returns and macroeconomic indicators are dependent variables.
Robustness Testing: Uses ChatGPT-4, fine-tuning, and alternative prompts to validate results.
Key Findings
1. ChatGPT-3.5’s Market Predictive Ability
Positive News Ratio (NRG) as a Predictor:
Higher NRG predicts higher stock returns in the following months.
Regression analysis shows NRG has a 0.53% coefficient on next-month stock returns (statistically significant at 5% level).
R-squared increases to 8.52% for 12-month predictions, indicating stronger long-term effects.
Delayed Market Reaction to Positive News:
Investors fail to immediately incorporate positive news, leading to a gradual price adjustment.
Limited Impact of Negative News (NRB):
Negative news is priced in immediately, showing no predictive power for future returns.
2. Economic Conditions & News Impact
NRG is More Predictive During Recessions:
During economic downturns, positive news is often undervalued, making it a stronger signal for future stock movements.
Novelty Effect:
ChatGPT identifies more unique, impactful news—reinforcing its ability to detect high-value information.
Economic Policy Uncertainty (EPU) Interaction:
In times of high policy uncertainty, ChatGPT’s predictions improve as investors struggle to interpret complex economic signals.
3. Comparing ChatGPT to Other AI Models
DeepSeek’s Limitations:
DeepSeek, despite being structurally similar to ChatGPT, performs worse due to less training on English financial news.
BERT’s Weak Performance:
Traditional models like BERT fail to capture nuanced financial signals, making them less effective in market prediction.
4. Methodological Considerations
Prompt Engineering Effects:
Different prompts for sentiment classification can affect results.
Data Period Dependency:
Findings are based on 1996–2022 data, meaning different periods could yield different results.
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