Early Bird Offer : Register before September 15, 2022 to get a 15% off on our Programs

Dr. Ernest P. Chan

Faculty, QuantInsti

Managing Member of QTS Capital Management LLC, founder and CEO of PredictNow.ai, author of three books on Quantitative Trading
Dr. Chan was an Adjunct Associate Professor of Finance at Nanyang Technological University in Singapore, and an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises students theses there. Besides being a faculty in QuantInsti, his academic distributions are available on Quantra and on major web portals. He is an industry expert in algorithmic trading and machine learning. He lectures on Quantitative Momentum Strategies, which probes the origins of momentum in futures and equities markets and finds enduring arbitrage opportunities.
Dr. Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor that focuses on crisis alpha strategy. He is also the Founder and CEO of PredictNow.ai, a financial machine learning SaaS.
Since 1994, he 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.
Dr. Chan has written three books: Quantitative Trading: How to Build Your Own Algorithmic Trading Business Algorithmic Trading: Winning Strategies and Their Rationale Machine Trading: Deploying Computer Algorithms To Conquer the Markets


Apply Now

Fill in these details to begin your application for the program of your choice

OTP Sent
Invalid OTP
OTP Verified
OTP not sent. Please retry.
Field will not be visible to web visitor
Field will not be visible to web visitor
Field will not be visible to web visitor

By submitting the form, you agree with our
Terms and Conditions and Private Policy