Jim Creighton, Head of Manifold Cluster Analysis and Chief Investment Officer at Manifold Partners, sat down with our team to discuss the growing trend of using machine learning and large-scale data analysis to better control risk and predict outcomes in the investment world and what he learned using Via Science’s approach.
By Via Science Marketing
Give us a little background on yourself.
I grew up in a small village in Nova Scotia (Tatamagouche), graduated in mathematics from Dalhousie University, and then quite by accident became an actuary. While working for a large insurance company in Halifax I discovered that assets were more interesting than liabilities. That eventually led to moving to Toronto and starting, with several other people, a firm that became one of the larger quant firms in Canada in the late 1980s. Following this, I headed up Barclays Global Investors (BGI) in Canada, and then took on the role of Global Chief Investment Officer for BGI in San Francisco.
I was also a Global Chief Investment Officer for Deutsche Asset Management in New York and later, the same role for Northern Trust in Chicago. After being the Chief Investment Officer for three of the largest and best quant firms in the business globally, I was ready to go back to investment research. I left Northern to start a firm applying machine learning methods and large-scale data analysis to the investment world.
You have served as Chief Investment Officer to three of the world’s largest banks and managed over $1 trillion in assets throughout your finance career. What first attracted you to this industry and what has kept you going?
I have always tackled jobs that fascinated me, so I was always motivated by the challenge.
The investment world is filled with very bright, interesting people, which makes it difficult to view this as “work”. I get to work each day with great people on difficult problems. What could be better?
The fact that machine learning techniques we have been applying for some years suddenly have become something of a rage has made my work life more rewarding than ever.
Quantitative trading has been applied to finance for more than 40 years with its share of successes and failures. How have you seen it evolve throughout your career and what’s different now?
What we did 20 years ago was very simplistic by today’s standards. Quantitative methods in investment management evolved very slowly and were well behind the methods being used in many other areas of business and science. For example, for many years we have understood that markets are not linear in nature and financial distributions are certainly not Gaussian in character. Yet quants have persisted in using ever more refined methods based on assumptions that are fundamentally flawed.
That is all starting to change and it’s changing rapidly. Quants in finance are starting to understand machine learning and other data analysis techniques that have been developing over the past 20 years can indeed be applied to financial markets. That means better predictions, better risk control and ultimately, better outcomes for clients.
It turns out that if you have the right machine learning techniques, data and computing power, you do not have to make simplifying assumptions like linearity and normality. The answers are in the data, if you can just apply the right machine learning techniques to determine the relationships between the data and outcomes.
When most people think of quantitative trading, they think of very high frequency trading. That is not true for Manifold Cluster. Can you share more about your approach?
Quantitative approaches, including machine learning techniques, can be used in a variety of ways. It is certainly the case that high frequency trading is necessarily highly quantitative and automated. And high frequency trading has had a lot of press coverage so it is relatively well known. But from the point of view of prediction complexity, making a prediction for the next second is a simper problem than making a prediction for the next month or more. That is in part because many more things can happen over the next month that will influence outcomes than is the case for the next second. It is like trying to predict the weather the next minute versus the next month.
At Manifold, we are interested in making predictions over longer time periods. We consider a number of factors that influence investor behavior and potential outcomes over weeks or months. There is a lot of data and trillions of calculations are required. So we use high-end computing, data analysis and machine learning to examine how investors reacted in the past in specified circumstances. We use past investor behavior in highly similar circumstances to today to make a prediction about how investors will treat a stock going forward from today.
Do you see artificial intelligence and machine learning as a growing trend in the industry? What interests you most about the potential and promise of this technology?
When we started applying machine learning to financial markets, the subject was never mentioned in the financial press and we were not taken seriously by other quants, who generally were of the view that if you could not write down the equations linking factor values to returns, you could not trust the “model”. Even in other branches of business and science the mention of machine learning was rare. Now you cannot pick up an industry publication without seeing a reference to some form of artificial intelligence or machine learning.
The growth in interest and understanding has been exponential over the past couple of years. What excites me is that the investing world is starting to wake up to the potential to make better predictions in one of the most challenging and complex systems humans deal with – financial markets. Better predictions mean better outcomes, and that helps everyone.
Manifold and Via Science both have very specific but different approaches to solving problems. What interests you most about using them in combination to improve investment strategies? What value does Via Science’s Bayesian networks approach offer Manifold?
There are many forms of machine learning with different strengths and weaknesses. In a complex problem like predicting future stock prices, it is quite likely that two different types of machine learning will have some overlap. But it is also the case that each system will likely pick up some information in the data missed by the other approach. That is exactly what we find when we use both our approach to machine learning and that of Via Science. Using our factors, Via Science picks up information that we do not pick up and vice versa. We find that combining the signals gives a nice improvement in results over using just one approach, even though both machine learning approaches are using exactly the same data set to make predictions.
What is the next big thing for investing, and Manifold in particular?
We strongly believe that the “next big thing” in quantitative investing is the rapid growth in application of machine learning to financial markets. I would argue that over the next number of years machine learning will become the primary method for making predictions in financial markets.
As for Manifold, we are continuing to extend and refine our application of machine learning to markets. One area we are looking at presently is “feedback loops,” where results are analyzed rapidly and used to adjust predictions in light of experienced prediction accuracy. This looks very promising and is a way to better incorporate changes in markets structure and investor behavior.
Another area we are thinking about is “layering” machine learning techniques, where one layer would oversee and refine some inputs for the next layer. This again is a way to make sure the machine learning systems are evolving along with the world it is attempting to predict. Generally, layered levels of machine learning should evolve faster and make better predictions than a single level and approach to machine learning. We want our forms of artificial intelligence to evolve, much as human brains do with experience, in their ability to understand and predict the world around them.