Ernest Chan

Dr Ernest Chan talks about many aspects of quantitative trading, including how market crises impact momentum strategies and how to manage the impacts, when to use stop-losses and when they don't make sense, automating trading, managing funds in a portfolio of strategies and a simple money management approach which aims to limit drawdowns while maximising returns. Topics discussed Where to find trading ideas The first aspect of a market to identify before building a strategy for it Momentum crashes and the performance of momentum strategies after a financial crisis How to manage the times when momentum strategies aren’t working When stop losses should be used and when they don’t make sense How to limit drawdowns while maximising growth Factors to consider when automating your trading How independent traders can avoid competing with the big trading firms When you need to worry about market microstructure and when it doesn’t matter Managing funds for a multi-strategy portfolio The hardest part of trading   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

Join Ernest P. Chan, a leading figure in quantitative finance and founder of PredictNow.ai, as he shares his insights on the intersection of AI and trading. He reveals when machine learning models thrive and the pitfalls of data sparsity in financial markets. Discover how generative AI enhances risk management and portfolio optimization. Ernie also discusses alternatives to find alpha, the evolution of his quant strategies, and the vital traits for success in this evolving field. A must-listen for aspiring quants!

Dr. Ernest Chan, founder of QTS Capital Management, talks about his use of machine learning as a risk management layer on QTS's Tail Reaper program, a tail hedge strategy. He shares the success and unique approach of the tail reaper program, discusses the limitations of deep learning, and explores the challenges of adopting machine learning in the process.