Ernest Chan, Founder at PredictNow.ai, is also the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. QTS manages a hedge fund as well as individual accounts. More information about his services can be found at
predictnow.ai and
www.qtscm.com. 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", all published by John Wiley & Sons. He maintains a popular blog "Quantitative Trading" at
epchan.blogspot.com. He is an adjunct faculty at Northwestern University's Master's in Data Science program. His courses and publications on finance and machine learning can be found at
www.epchan.com.
The top challenge in financial machine learning, in general, is to create enough features that keep up with the times.
MEDIA 7: Could you please take us through your professional journey?
ERNEST CHAN: Six long years of Ph.D. in theoretical physics at Cornell convinced me that I am no physicist. Fortunately, I developed a strong interest in machine learning during that time, and convinced IBM Watson Lab and later on Morgan Stanley’s Data Mining and AI group to hire me as a researcher. There began my decades-long journey of making AI work in investment management, finally succeeding a few years ago at my hedge fund, QTS Capital. We think our discoveries have implications far beyond investment management and finance. So I spun off a tech firm Predictnow.ai to democratize that.
M7: The second edition of your book ‘Quantitative Trading’ is now out. Could you please tell us a little bit about it?
EC: When the book first came out in 2009, it made a splash in introducing quantitative trading to many individual traders. I talked about many techniques, strategies, and provided lots of resources on where to find trading ideas. Since then, I have published 2 more books (Algorithmic Trading and Machine Trading), in increasing order of (hopefully) sophistication, but I still regard Quantitative Trading as the best introduction to readers new to this field. For those who have already read the first edition, they will find some significant additions such as how to make a PCA program run 10x faster, how to optimize a trading strategy’s parameters under different market regimes via machine learning, and a look at the out-of-sample performances for some of the strategies I described more than 10 years ago.
Different libraries require different versions of Python, bugs in different libraries caused various delays and issues, and engineering the input features properly took a long time. With Predictnow.ai, all these tedious chores are dealt with by our financial data scientists, machine learning experts, and software engineers.
M7: What are the new pre-engineered stock fundamental features that PredictNow.ai Inc. launched recently?
EC: Stock fundamental features fall into 2 types: cross-sectional vs time series. Our cross-sectional features are specific to each stock traded in the US (>6,000 listed companies, and >10,000 delisted ones to avoid survivorship bias), and they are things like earnings yield, book-to-market ratio (B/M), etc. They are computed from the companies’ quarterly financial statements and are useful if you want to predict the forward risks of trading individual stocks. Time series features are the twins of the cross-sectional features (see https://www.predictnow.ai/blog/metalabeling-and-the-duality-between-cross-sectional-and-time-series-factors/).
For example, we would rank all the stocks based on their B/M ratio and form a portfolio that buys the top quartile of high B/M and short the bottom quartile of low B/M stocks, and use the daily returns of this long-short portfolio as the time series factor. What this factor measures, is whether the market favors value stocks over growth stocks each day, and it can be used as the input to a machine learning process called “meta-labeling” – computing the probability of profit for a portfolio or a strategy, rather than individual positions within a portfolio.
M7: How does PredictNow.ai Inc. make machine learning more accessible with a no-code interface?
EC: When we were developing the ML-based risk management layer in our hedge fund, we thought that it would be easy to implement using open-source software. But it wasn’t. Different libraries require different versions of Python, bugs in different libraries caused various delays and issues, and engineering the input features properly took a long time. With Predictnow.ai, all these tedious chores are dealt with by our financial data scientists, machine learning experts, and software engineers. We even have two highly regarded mathematicians working on the team to deliver the most advanced features and optimization algorithms to our users.
In life as in business, one should not be afraid to pivot quickly when reality doesn’t meet expectations.
M7: What are the top challenges you see for the industry in general and PredictNow.ai this year?
EC: The top challenge in financial machine learning, in general, is to create enough features that keep up with the times. For example, who knew that we need sentiment features on r/wallstreetbets a year ago?
M7: Knowing what you know now, what is your advice to your younger self?
EC: In life as in business, one should not be afraid to pivot quickly when reality doesn’t meet expectations.