In the last few years, an important step-change in the development of AI has occurred. The most powerful Large Language Models (LLMs) of today possess complex 'world models' within their learned weights. These world models allow LLMs to capture concepts in our day-to-day lives, and in business, and the intricate relationships between them. This capability allows LLMs to make logical inferences beyond their training data, mirroring the reasoning process vital in today's knowledge-intensive fields. This trend may, in the near future, drastically change the processes of many types of analytical activities, including data science and quantitative finance. Our recent paper, titled "Towards Automating Causal Discovery in Financial Markets and Beyond", illustrates some of the opportunities this trend presents. In it, we explore the applications of Large Language Models (LLMs) for causal discovery, the process through which researchers create scientific hypotheses they can then empirically test. As practitioners are aware, this process is critical in data science to reduce the risk of finding spurious and otherwise non-generalizable relationships, and therefore the risk of finding strategies that may perform well on during training (even out of sample) but not in a live scenario. In our paper, we show how LLMs can be used to create hypotheses (formulated as causal graphs) that align well with empirical evidence, and demonstrate practical approaches to generating such hypotheses with current state-of-the-art models and the complexities involved in doing so.

About Speaker:

Alik’s professional background is in AI consulting as a machine learning and AI product team leader, and venture capital investor in one of Peter Thiel's funds. Alik is also leading researcher and educator in the machine learning field, having taught and developed the machine learning course at the University of Toronto Master's of Mathematical Finance program, as well as many workshops and classes around the world. Alik is also a PhD candidate and Vanier Scholar at the University of Toronto, studying applications of machine learning in quantitative finance and he has published papers at the intersection of quantitative finance, AI, and responsible investing in leading journals. Currently, Alik serves as the co-founder and CEO of Sibli, a technology company at the cutting-edge of Generative AI in investment management.