Description: Russ Goyenko, Associate Professor of Finance at McGill University discussed with Dr. Hossein Kazemi how large language models can, and soon they will produce more accurate analyst forecasts. Using textual information from a complete history of regular quarterly and annual (10-Q and 10-K) filings by U.S. corporations, we train machine learning algorithms and large language models, LLMs, to predict future earnings surprises.

Description: Alik Sokolov and Kathryn Wilkens discussed the revolution of natural language processing in recent years, and how it applies to various areas of investment management. Our ability to work with unstructured text data, which is abundant in investment management, has undergone several evolutions from the late 2010's: from sequence-to-sequence models for machine translation, to the advent of transformers and transfer learning, to the recent breakthroughs achieved by Large Language Models popularized by Chat GPT.

Description: In a world where artificial intelligence is becoming complex and gaining influence, creating and using a machine learning model is not only a technical endeavor but also a personal journey of exploration, challenges, and growth. Tom Pickel shared his journey of building a neural network from the ground up. Tom shared his experience in creating a neural network using Python’s basic data science packages (Numpy and Pandas) for trying to predict movements in the stock market.