Possibly nightmare of those who frequent risk their money in stock markets is coming to an end as according to a new study conducted by finance professor from the University of Florida shows that ChatGPT is capable of accurately predicting stock market movements.
According to Benzinga, the study, over 50,000 news headlines about companies dating back to October 2021 were fed to the chatbot, which evaluated whether the news was good, bad or irrelevant to the company’s stock prices. Using sentiment analysis, the chatbot generated a “ChatGPT score”, which was then analyzed to determine whether it was predictive of the companies’ stock market performance the following day.
The study found a significant positive correlation between the ChatGPT scores and the next-day stock performance for the analyzed companies. Companies with higher scores tended to have better returns than those with lower scores. ChatGPT outperformed traditional sentiment analysis methods that also used data from headlines and social media to predict stock movements.
The researchers concluded that incorporating advanced language models such as ChatGPT into investment decision-making processes can lead to more accurate predictions and enhance the performance of quantitative trading strategies. The study demonstrated that traditional models did not provide any additional predictive power over ChatGPT-derived sentiment scores. These findings suggest that ChatGPT may hold promise for investors seeking to anticipate future stock market movements.
While the potential use case for ChatGPT and other advanced language models in predicting stock market returns is promising, there are apprehensions in the market regarding the risks it could pose if it does not provide the expected accuracy and assistance. Despite this caution, Bloomberg recently released a new GPT-based language model called BloombergGPT, which is trained on a dataset consisting of English-language financial documents, news, filings, press releases and social media. The company claims that this new model will improve existing natural language processing tasks such as sentiment analysis, news classification, headline generation, question-answering and other query-related tasks.
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