Interview by Sramana Mitra
Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Bristol Gate.
Richard Hamm: I’m the CEO at Bristol Gate Capital Partners. We’re an asset management company in Toronto focused on high-dividend growth investing so we can provide our customers with more income each and every year. That’s the objective.
We believe entirely in the man-and-machine approach in an industry that is virtually bereft of any artificial intelligence. We are lucky enough to have lived with it as long as we’ve been in business over the last 10 years.
We’re also lucky enough to live in Toronto, which is one of the AI centers in the world. We have lots of different people who we can speak to in the FinTech space and beyond. Fortunately, adding artificial intelligence into our business strongly supports the idea of why we use AI based on producing great results for clients in their income and capital growth.
Sramana Mitra: Let’s double-click down a little bit on how you do what you do. Are you running a consumer-facing investment platform, or are you providing enterprise software to investment managers and banks who provide that, in turn, to their customers?
Richard Hamm: We would be a product manufacturer. We would incorporate the use of data into our process of building that product. We do it through a series of predictive analytics that can try and look at what types of companies can produce the highest dividend growth a year ahead with the least amount of measured risk.
We use AI entirely and some NLP applications on the front end of what we do when we’re building a product. Our products are distributed by banks, trust companies, and people who consume fund products.
Sramana Mitra: So your customers are the banks and fund managers?
Richard Hamm: The banks are not independent fund managers like Fidelity. They would never use us. They view themselves as competing with us. We do banks, trust companies, family offices, pensions, endowments, and foundations. Those would be our customers.
Sramana Mitra: Let’s take a few use cases across your different customer segments. Let’s take three different customer segments and do some use cases.
Richard Hamm: Let’s talk about banks. Banks in Canada are quite different than banks in the United States. They’re very narrow and protective of themselves. They’re a very small oligopoly. They control a lot of shelf space and they control the products that go on that shelf space for their customers.
We work with banks to be a part of their offering. To find us, you’d have to be Sherlock Holmes because we’re usually buried way down in the fine print. Their marketing is of their own brand and their own products. They would hire us to manage a part of a product. That’s a bank use case.
Sramana Mitra: When a bank does hire you to do this kind of work, double-click down to exactly how the technology works and what it is that it’s doing on behalf of the bank.
Richard Hamm: What the banks want us to do is to satisfy their investment-oriented customers by giving them a good return.
Sramana Mitra: I’m trying to understand what’s happening in the guts.
Richard Hamm: They don’t come in and have a very deep look at what we do in applying predictive analytics. We tell them what we do and we take them through the evolution of the system.
Sramana Mitra: What I’m trying to extract is what is the thought leadership that’s going on and how that applies.
Richard Hamm: If a bank was to look at what we did on the face of it and say, “What we do is what they can’t do.” We simply take them through the process of applying data and the records that we generate from the data into predictive models for dividend growth.
Sramana Mitra: I’m trying to go a bit further down in the process. We understand that at a 30,000-foot level. Give examples of what kind of data are you using to predict what.
Richard Hamm: We use data from Bloomberg and Fred. We prepare all these records. We run models based on about a thousand features of the data going back 20 years.
We train the model through machine learning to take what we have done in the past that has worked to find the same basic premise of prediction that we would have done by linear regression in the old days. Then that data is sorted into a training model.
The training model is then applied to the set of securities that we drive down to the number of features that we believe are key predictive features out of the thousand features we start with.
Generally speaking, we work it down to about 200 features. Then in that 200 features, there are 20 dominant features. As you can imagine, one of the most basic features is history. The history of what has happened is important in order to predict the future. Where we’re different is people don’t predict the future very well.
Our business is based on doing that. Some of the features are anything that you can imagine financially. That would go into our gradient boosting machine that would take what we have known in the past to be reasonably good and better than what we could do on our own.
We just keep that model running constantly. We always have a model running on top of an actual. If we find something that’s significant or that can change it, we’ll be that much better off.
Sramana Mitra: The end goal of all this is to maximize the dividend-based income for investors and public stocks.
Richard Hamm: Yes, these are S&P 500 companies only. That’s the most difficult market in the world to apply this to because it’s the most over-invested market in the world. We set out with the goal of doing it so that we can say, “Can we predict dividend growth better than analyst estimates?”
If we can lower our error rate in predicting dividends below that of analyst estimates, then we’re doing the right thing. We measure that kind of accuracy improvement. We are about 1.5 times better than analysts.
Sramana Mitra: How is this information consumed by your customers? Is there a human in between the bank’s automated system and where the consumer touches the bank?
Richard Hamm: At the front-end, we use predictive analytics. We also add our own human influence on this to verify the accuracy of the models by good old-fashioned fundamental analysis to make sure that the 65 names that are generated are worthy of investment. We drive that number down to 22.
The bank buys from us the finished product of 22 stocks that are driven by the process I discussed. In the final stages, we do correlation analysis and risk analysis on the 22 names that finally make it into the portfolio that we sell to the banks. The portfolio is sold to the banks.
Sramana Mitra: The bank then, in turn, sells that portfolio to their customers.
Richard Hamm: Yes. The reason they buy it is they don’t do this themselves.
Sramana Mitra: This workflow applies to all your customers then?
Richard Hamm: Yes.
Sramana Mitra: This is what you do. You come up with a portfolio that is optimized for dividend growth on a regular basis. How often would a bank want a recommendation like this? Is it monthly, quarterly, or annual?
Richard Hamm: Banks tend to be consumers of this kind of product over time. We would be hired on an annual basis. These relationships tend to last a long time as long as we’re delivering what we say we’re going to deliver.
We’ve got long-term relationships with most of our customers, although we’ve only been in business for 10 years. We’ve known these people for a long time and we tend to be involved long-term with their companies.
Sramana Mitra: At any given time when you’re giving a recommendation, it’s the same portfolio that you’re recommending to all the banks?
Richard Hamm: It would be no different.
Sramana Mitra: Basically, you’re coming up with a portfolio recommendation which is then sold to all the banks or all the investment companies who are your customers.
Richard Hamm: In the United States, we deal with UBS and Citibank. What they would do is the same thing as Canadian banks. They would use our intelligence on building a portfolio, and they would buy the finished goods from us.
Sramana Mitra: You said at the outset that companies like Fidelity are your competitors. Elaborate.
Richard Hamm: Most of the big companies in the mutual fund business believe everybody is a competitor, because we’re selling to the same distribution systems they’re selling to. When you’re fighting for someone’s dollar of investment, they would obviously believe that they were a competitor to us.
Fidelity is a big powerful company. They have recently added some artificial intelligence into their investment world, which is no different than a lot of other people who have come to the game quite late.
In a recent study, 85% of investment managers in the United States said that they would like to investigate using artificial intelligence in their work. 15% have actually adopted it. Of that 15%, most have done so in the last year.
I don’t know if they’re responding to the demand in the trend of customers, which I believe is probably true. It doesn’t mean that they have mastered the process at all.
Sramana Mitra: Let’s switch to the other question which is open problems in FinTech using the kinds of approaches you’re talking about. Where do you see the possibilities of applying these kinds of techniques? And to do what?
Richard Hamm: In the FinTech world, which is alive and well in Toronto, my biggest concern, particularly for FinTech that sells itself into banks, is that it’s no different than a lot of technologies. They’re built to solve a particular problem. They’re not an integrated solution.
Let me fix how quickly I can analyze the ability of someone to borrow money. Let me figure out if I can take their driver’s license and lend them leasing money. They’re not answering the big questions. They’re solving questions that often bothers them individually like all technologies.
I don’t see it moving the dial very much in the world of banking, to tell you the truth. Banks are filled with big legacy systems that are very difficult to redo. The costs are enormous. They don’t even have the luxury of starting fresh and building the foundation that they should build. FinTech does a lot of patchwork.
Sramana Mitra: You started your current company 10 years back. If you were starting a company today in FinTech, what kind of a company would you start?
Richard Hamm: If I was going to be in the financial space, I would look at companies like Digit in Canada. Their view is much more holistic. They would like to integrate all of the aspects of a client from understanding the client and their needs to educating them about how money actually works, how different returns work, and building better allocation models. All of these are done in parts by people, but very few have an integrated offering.
This is where the world of robo investing and direct investing are trying to get to. Most are not very successful. If I would take a bigger view of the problem, I would just try and solve an issue with the problem.
Sramana Mitra: What do you think is hindering the robo advisory category?
Richard Hamm: The biggest issue for them is the money still resides in people who are 55 and over. The millennials may be driving the need for robo. Here’s the interesting thing. People are quite willing to try and self-serve, but when it comes to financial decisions, they’d like to have someone confirm with them that what they’re doing is right.
At the end of the day, robo companies are finding that their cost structure is not as neat and tidy as they thought. People want to get confirmation that what they’re doing is the right thing for them.
Sramana Mitra: That can be managed. If the industry is coming to terms with the fact that when it comes to managing finance, people don’t want to do automated advisors only and they want a human being, you can have technology assisting people. They can be technology-assisted users.
Richard Hamm: We’ve developed with some people in Oxford a chat-with program. It can answer a lot of questions but at the end of the day, talking to a chatbot is not the same as a person.
Sramana Mitra: I’m not talking about a chatbot; I’m talking about a real person. This person doesn’t have to have a Ph.D. in Finance.
Richard Hamm: They don’t have to have a Ph.D. I don’t know what it is.