Complete Recording
Check out the complete recording of the webinar here:
About the Session
This is the first webinar of our #10YearsOfQuantInsti series.
Mean reversion strategies have similar characteristics as short volatility strategies: they do well in calm and bullish markets, but suffer tail risks and can do very poorly during financial crises. In this session, Dr Chan will discuss how their fund QTS Capital Management benefited and suffered from trading a mean reversion strategy, and what they did to mitigate such tail risks.
Dr Ernest Chan is a globally renowned speaker on computerized trading. Dr Chan has conducted multiple educative sessions worldwide and has authored three books so far. His writings on Quantitative, Algorithmic and Machine Trading form primary literature for traders and aspiring quants.
Speaker Profile
Dr Ernest Chan
Founder & CEO of PredictNow.ai Inc
Dr Chan is the Founder & CEO of PredictNow.ai Inc and a Managing Member of QTS Capital Management, LLC. He has worked for various investment banks (Morgan Stanley, Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997.
Dr Chan received his PhD in physics from Cornell University and was a member of IBM’s Human Language Technologies group before joining the financial industry. He was a co-founder and principal of EXP Capital Management, LLC, a Chicago-based investment firm.
Dr Chan is also the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley), Algorithmic Trading: Winning Strategies and Their Rationale and his third and latest book is on Machine Trading: Deploying Computer Algorithms to Conquer the Markets.
#10YEARSOFQUANTINSTI
As they say, knowledge is the greatest gift in life. While celebrating our 10 years of existence, we’ve planned this series to thank our community. We are thankful to you from the bottom of our heart for showing the love & support in our journey over the years!
This webinar was conducted on:
TUESDAY, SEPTEMBER 15, 2020
10:00 AM ET | 7:30 PM IST | 10:00 PM SGT
