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Major and niche financial services and investment management companies are shifting their investment and strategic focus toward AI and machine learning applications to optimise their market and portfolio returns.
Machine learning and trading experts, Dr Ernest Chan and Eric MacDonald discussed:
• The importance of continuous improvement models and more effective trading signals using a vast array of predictors;
• Adaptable parameters for ever-changing trading strategies through “Conditional Parameter Optimization” (CPO);
• The CPO technique’s potential applications in the context of other industry verticals.
Note – this webinar took place on Thursday 18 November 2021.
Ernest is the CEO of this financial machine learning SaaS company.
He is also the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor, specializing in crisis alpha and machine learning. He began his career as a machine learning researcher at IBM’s T. J. Watson Research Center in the storied Human Language Technologies Group, which produced some of the best known quantitative hedge fund managers. He later joined Morgan Stanley’s Data Mining and Artificial Intelligence Group in New York. He was also a quantitative researcher and trader for Credit Suisse and other hedge funds.
Ernie is also the author of “Quantitative Trading”, “Algorithmic Trading“, and “Machine Trading”, all published by Wiley. He writes a popular blog at epchan.blogspot.com. Ernie earned his Ph.D. in theoretical physics from Cornell University and his BSc from the University of Toronto.
Erik is an MBA candidate at DeGroote School of Business specializing in Business Analytics. Previously, he received his degree in Mechanical Engineering from Queen’s University in Kingston, Ontario.
