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Since the introduction of ChatGPT, numerous quantitative traders and portfolio managers have been intrigued by the potential of utilizing it to develop quantitative and algorithmic trading systems. The field of quantitative finance has been abuzz with excitement and speculation about the capabilities of ChatGPT. However, amidst the hype, it is crucial to critically examine its proficiency in programming and designing quantitative trading systems. While ChatGPT exhibits impressive language generation abilities, it is important to assess its practical applicability and effectiveness in the complex and quantitative-driven world of algorithmic trading.
Reference [1] conducted an examination of AI-powered programming tools and highlighted a fundamental problem associated with using AI as a programming tool. The article shed light on potential challenges and limitations that arise when relying solely on AI for programming tasks. The authors pointed out,
Despite the many benefits of AI-powered programming, the use of AI here raises significant concerns, many of which have been pointed out recently by researchers and even by the providers of these AI-based tools themselves. Fundamentally, the problem is this: AI programmers are necessarily limited by the data they were trained on, which includes plenty of bad code along with the good. So the code these systems produce may well have problems, too.
In our experience experimenting with ChatGPT, we have found that, in its current state, AI primarily functions as a language-based model and cannot effectively perform complex tasks. For example, we requested ChatGPT to generate code for pricing a convertible bond, and it provided an entirely incorrect answer. What is concerning is that ChatGPT presented its response with a high level of confidence, potentially misleading users into assuming the accuracy of its results. This highlights the critical importance of human expertise and domain knowledge. If a programmer lacking knowledge of convertible bond pricing were to accept ChatGPT’s response without verification, it could lead to serious consequences. It underscores the need for caution and human oversight when relying on AI models for complex financial tasks.
In summary, while AI can offer valuable assistance and facilitate certain aspects of programming, it often lacks the comprehensive understanding and contextual knowledge that human programmers possess. This fundamental gap raises concerns about the reliability, adaptability, and precision of AI-powered programming tools, emphasizing the need for human expertise and oversight in the programming process.
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References
[1] Jaideep Vaidya, Hafiz Asif, A Critical Look at AI-Generated Software, SPECTRUM.IEEE.ORG 35, 2023
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