GPT-4 outsmarts Wall Street: AI predicts earnings better than human analysts

Researchers recently explored whether OpneAI’s GPT-4 could analyse financial statements as effectively as human analysts. Their findings were surprising: GPT-4 was not only capable of predicting changes in company earnings more accurately than human analysts, but it also matched the performance of advanced machine learning models, even when it was only given raw financial data without any additional context.

The researchers conducted their study by providing GPT-4 with standardised financial statements, carefully stripped of any company names or dates to prevent the model from using prior knowledge. To mimic the process human analysts typically follow, they used special prompts to guide GPT-4 through the analysis step-by-step. This approach ensured that GPT-4's analysis was as close to human reasoning as possible.

Using data from the Compustat database, covering the years 1968 to 2021, the researchers compared GPT-4's performance with human analysts' predictions from the IBES database. The results were telling. With the step-by-step prompts, GPT-4 achieved a prediction accuracy of 60.35 per cent, significantly higher than the 52.71 per cent accuracy of human analysts. Moreover, GPT-4’s F1-score, which balances the accuracy and relevance of predictions, also outperformed that of the human analysts.

One of the critical aspects of this study was testing GPT-4's capabilities without any textual information, such as the Management Discussion and Analysis (MD&A) that usually accompanies financial statements. This allowed the researchers to determine if GPT-4 could analyse and synthesise just the numerical data and still make accurate predictions. They found that it could, and the step-by-step prompts played a crucial role in guiding GPT-4 to analyse trends, compute financial ratios, and synthesise information much like a human would.

The study also highlighted situations where GPT-4 excelled. The AI model showed particular strength in scenarios where human analysts typically struggle, such as with small companies or those with volatile earnings. This suggests that GPT-4’s general knowledge and reasoning abilities give it an advantage in complex situations. Despite this, the researchers noted that human analysts still add value, especially when they have more time to process information. Combining the insights from both GPT-4 and human analysts led to even better predictions.

When compared to advanced machine learning models, like artificial neural networks (ANNs), GPT-4's performance was comparable. In some aspects, GPT-4 even slightly outperformed these specialised models. 

The practical implications of these findings are significant. GPT-4 could assist analysts in making quicker and more accurate predictions about company performance. Its ability to handle large amounts of data and provide insights could streamline the financial analysis process, making it faster and potentially more reliable than relying solely on human judgment.