Financial Analysis using GPT-4
The landscape of financial analysis is undergoing a seismic shift. Recent research from the University of Chicago demonstrates that large language models (LLMs), specifically OpenAI’s GPT-4, can conduct financial statement analysis with an accuracy that rivals – and often surpasses – that of professional human analysts. This breakthrough has profound implications for the future of financial decision-making.
The Chicago Study: LLMs Versus Human Analysts
Researchers at the University of Chicago in a groundbreaking study titled “Financial Statement Analysis with Large Language Models,” wanted to determine if a large language model (LLM) can perform financial statement analysis similar to a professional human analyst – a task that demands critical thinking, reasoning, and judgment.
In the study, GPT-4 was provided with standardised, anonymous financial statements and instructed to analyse them to predict the direction of future earnings. A significant innovation was also the use of “chain-of-thought” prompts, which guided GPT-4 to emulate the analytical process of a financial analyst. According to the researchers, this approach helped the model perform intuitive reasoning and pattern recognition, capabilities that stem from its vast knowledge base and understanding of business concepts. These carefully-curated prompts were expected to help the AI identify trends, compute ratios, and synthesise information to form sharper predictions.
Remarkably, even without any narrative or industry-specific information, the LLM successfully outperformed financial analysts in predicting earnings changes and did better even in situations where analysts typically struggle – such as in areas where they may tend to show bias or disagreement. Surprisingly, GPT-4’s performance matches or exceeds that of specialised machine learning models, such as ANNs (Artificial Neural Networks) trained for earnings predictions and considerably higher than the typical 53-57% accuracy range of human analysts.
Perhaps as importantly, the LLM’s predictions did not stem from its training memory; instead, it synthesised, based on the thoughtful prompts, useful narrative insights about a company’s future performance as well. Trading strategies based on GPT-4’s predictions also yielded a higher Sharpe ratio and alphas – used to analyse company performance in trading strategies – than those based on other models.
Overcoming Challenges in Numerical Analysis and the Path Forward
Despite these impressive results, the study also highlighted the traditional challenges LLMs face with numerical analysis. “One of the most challenging domains for a language model is the numerical domain,” said Alex Kim, one of the study’s co-authors. “While LLMs are effective at textual tasks, their understanding of numbers typically comes from the narrative context, and they lack the deep numerical reasoning or the flexibility of a human mind.”
Some experts have expressed caution, noting that the ‘ANN’ model used as a benchmark in the study may not represent the cutting edge of quantitative finance, noted VentureBeat.The remarkable capacity of a general-purpose language model to rival and even outperform specialised machine learning models,and human financial experts as well, underscores their disruptive transformative potential within the financesector.
The implications of this research are vast. As AI technology continues to advance, the role of the financial analyst is poised to evolve. While human expertise and judgment remain invaluable, powerful tools like GPT-4 can significantly augment and streamline the work of analysts. While the study’s results are certainly indicative of LLMs’ potential to democratise financial information processing and assist in decision-making, the full extent of their impact on human decision-making in financial markets remains to be explored.
As we move forward, the integration of AI in financial analysis will likely grow, offering new opportunities and challenges. The future promises interplay between human expertise and artificial intelligence, heralding a new era in financial analysis that leverages the strengths of both to drive better decision-making.
References:
- Read “Financial Statement Analysis with Large Language Models” (University of Chicago) here.
- The University of Chicago researchers have also developed an interactive web application to showcase GPT-4’s capabilities, inviting curious readers to explore its potential. However, they caution that the model’s accuracy should be independently verified before widespread adoption. Find it here.