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Risk management
Machine learning
Artificial intelligence
The views and opinions expressed in this article are the author’s, and do not reflect the views or positions of BNY Mellon.
This year, in an experiment not unlike the chess challenge between grandmaster Garry Kasparov and IBM’s Deep Blue, I agreed to take a prestigious university’s final-year maths exam question on financial models alongside an AI rival: GPT-4, the latest iteration of generative pre-trained transformers.
The question was an optimal control problem based on the Bellman – or Dynamic Programming – equation. You may be pleased to hear that GPT did not give the complete solution in a single prompt. It took expert prompting with a lot of back-and-forth to arrive at the answer. But the model’s response was immediate: it gave detailed explanations of the underlying concepts and methodologies – and, like Deep Blue, it got there in the end.
I make that a score for the AI. But don’t worry, we’re not into Arthur C Clarke territory yet – HAL 9000 won’t be setting your exams.’s guide to the world’s leading quant master’s programmes, with the top 25 schools ranked
To fully harness GPT’s capabilities, users must understand how it works. Relying on it for single prompts may not yield the best results. Still, we can obtain more nuanced and accurate responses by engaging in a dynamic, interactive conversation with the AI model. This approach can be particularly beneficial for students in mathematical finance, who often tackle complex problems that require a deep understanding of various concepts. It’s like having a chat with a robot mathematician pal. Just don’t expect it to crack jokes – not good ones, anyway.
Education is just one sector where the emergence of GPT – a large language model (LLM) based on probabilistic modelling – has had significant impact. And this year’s Quant Finance Master’s Guide has landed amid much debate over AI’s influence on education and on students in mathematical finance in particular, especially in the context of open-book exams.
These became a function of the transition to remote learning that educational institutions adopted as Covid-19 took hold. The approach provides students with access to various resources, including textbooks and online materials, and has altered the testing landscape, turning it into a playground for resourceful AI such as GPT.
Accordingly, GPT has become an indispensable tool for many students in such exams. Its sophisticated language-generation capabilities allow users to receive accurate and detailed responses to complex queries. And with the growing prevalence of open-book exams, GPT’s potential to assist students during assessments has become a hot topic – not just because it makes Clarke’s sci-fi visions feel closer to reality.
It also raises critical ethical questions about relying too heavily on AI for academic success. Leveraging GPT can be seen as an unfair advantage, potentially undermining the integrity of the assessment process.
Research by OpenAI, which developed GPT, has demonstrated that it can pass medical and bar exams and perform well in MBA tests. In one study, ChatGPT achieved a 60% accuracy rate across various parts of the United States Medical Licensing Exam – within the passing range – highlighting the potential of GPT in supporting students across multiple disciplines, including mathematical finance.
Another recent study shows that GPT-4 performs well in answering open-ended questions that require reasoning, describing and explaining concepts or algorithms. On the flipside, it needs help with calculations and solving actual problems, which highlights the importance of designing take-home exams that test students’ problem-solving abilities and understanding of concepts, rather than focusing solely on theoretical knowledge.
And grade distributions have remained largely unaffected by the switch to take-home exams, it finds – suggesting that the change in examination format has yet to lead to a significant advantage or disadvantage for students in terms of their academic performance. But it also reports higher fail rates for open-book exam course instances.
So, while GPT might be an AI genius, it isn’t turning bad students into good ones overnight. Educators therefore need to design exams emphasising problem-solving and calculations to counteract GPT’s ability to answer open-ended questions. This will help ensure that students are tested on their understanding and application of concepts, rather than their ability to regurgitate information provided by GPT. In other words, we need to keep GPT on its digital toes.
The main objective of exams is to evaluate a student’s knowledge and understanding of course material. By relying on GPT, students may be bypassing the learning process that helps develop the necessary skills for success in their field of study. This could lead to a generation of professionals ill-prepared for real-world challenges, ultimately weakening the workforce in various sectors, including quantitative finance.
The potential risks here are self-evident: from the trading floor to the back office, nobody wants a future run by AI-assisted slackers.
Additionally, using GPT during open-book exams raises concerns about academic integrity and fairness. Educators must find a balance between using the benefits of GPT and ensuring that students are still tested on their understanding and application of concepts, rather than their ability to regurgitate GPT’s information. As with any new technology, it is essential to proceed cautiously and consider its potential consequences.
The day-to-day practice of quantitative finance may take some time – but it may not. The widespread availability of these models can lead to rapid, significant shifts in the future. Bloomberg has already announced its version of GPT.
Integrating LLM with quant models could streamline the interaction of models with traders, doing things such as trade entry and capturing sentiments within trader chats much more efficiently.
As a final word of caution, it is worth noting that for all the impressive achievements of these models, Yejin Choi, a computer scientist, pointed out their incredible intelligence and shocking shortcomings. She revealed critical issues with these LLMs failing at basic common-sense reasoning. This phenomenon is so common that it has a name – hallucination, which is when the AI goes bonkers, spitting out fancy-sounding nonsense.
This happens because the model trains on information and then learns patterns and associations, but doesn’t deeply understand the content. Consequently, it may generate responses based on superficial connections, leading to seemingly coherent but inaccurate or irrelevant outputs.
Machine learning and AI have already left a substantial mark on the quant landscape, particularly in mathematical finance. As technology evolves, its potential applications in education and the professional world will expand.
By understanding and harnessing the power of GPT, students and professionals alike can adapt to the changing landscape of quantitative finance and thrive in this new era of AI-driven solutions – provided the AIs don’t go rogue.
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