Dr. Ernie Chan, CEO. Ernie’s career since 1994 has been focusing on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. He has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. He is also the founder and managing member of a quantitative investment management firm, QTS Capital Management, LLC..
Ernie was quoted by the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program, Technical Analysis of Stocks and Commodities magazine, Securities Industry News, Automated Trader magazine, and the CFA Institute Magazine on topics related to quantitative trading. In recognition of his expertise in statistical data mining, he was invited to serve on the Program Committees of the International Conference of Knowledge Discovery and Data Mining in 1998. He is the author of “Quantitative Trading: How to Build Your Own Algorithmic Trading Business“, “Algorithmic Trading: Winning Strategies and Their Rationale“, and “Machine Trading: Deploying Computer Algorithms to Conquer the Markets“, all published by John Wiley & Sons. He also writes the popular Quantitative Trading blog and conducts workshops on quantitative investment strategies and machine learning in London, UK. He was an Adjunct Associate Professor of Finance at Nanyang Technological University in Singapore, and an Industry Fellow of the NTU-SGX Centre for Financial Education, which is jointly set up by NTU and the Singapore Exchange. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there.
Ernie holds a Bachelor of Science degree from University of Toronto in 1988, and a Doctor of Philosophy (1994) degree in theoretical physics from Cornell University.
How Features Selection can help improve trading strategies
One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? If you don’t understand the underlying mechanisms of a predictive model, you may not trust its predictions. Feature importance ranking goes a long way towards providing better interpretability to ML models, and feature selection improves out-of-sample performance of ML models.