Dancing with the investment stars

Ashleay: Welcome to the “In Your Interest!” podcast. My name is Ashleay and as usual, I'm joined by my colleague Sébastien Mc Mahon. And this week we welcome Tej Rai, Managing Director, Head of Asset Allocation, to talk about investment approaches. So, hi, Tej! Hi, Sébastien!

Tej: Hi, Ashleay.

Sébastien : Hi, Tej. Great to have you here. So, people don't know you. I think you're kind of one of the best kept secrets that we have here at iA, but we've been working together since… what? 2020?

Tej: Yeah, late 2020 actually.

Sébastien: Yeah. In the middle of the pandemic. So, the asset allocation team took a turn there and you joined us. And since then, the team has grown by a lot. And the way that we do investments has changed quite a bit. So, it's fun to have you here to count you as a partner. So, what people don't know about you is that you're a great dancer.

Ashleay: Mm-hmm….

Tej: That's right. Argentine tango and partner dancing in general is a big passion of mine.

Sébastien: Okay. All right. But you're even better in investments. So here, you know, we're going to be talking about investment approaches, value investment, quantitative investments. So, all of these rolled into one.

Tej: Sounds good. Let's do it.

Ashleay: So, Tej, who is the greatest investor of all time…. other than yourself?

Tej: I wouldn't certainly count myself there, but I think, you know, it's a tough question, but it's a very interesting one, and I think my first reflex is to say, what's the definition of “greatest”? Is it the annualized return you've delivered to clients, the assets under management or just the overall P&L generated? I'm not really sure there's a single answer, but for me personally, the two that stand out the most are Warren Buffett and Jim Simons. Now, of the two, Warren Buffett's probably the most well-known because he's quoted regularly in the press, and his investor meetings in Omaha are well attended, covered extensively. But what I find interesting about Buffett is that he's stuck to a single investment style – that's value investing – for over 60 years. And so, what he does is he analyzes each investment in detail. He tries to find businesses that have solid fundamentals. Things like competitive advantages that will endure, strong management teams, predictable cash flows, things like that. And then when he finds such businesses that are also trading at a big discount in the market for whatever reason, he then commits in a big way to those investments, and he builds up a concentrated portfolio of stocks that he holds for the long term. And it's this disciplined approach that's resulted in something like a 20 plus percent annualized return for his company, for Berkshire Hathaway shareholders, since 1965, which is incredibly consistent and incredibly successful.

But on the other hand, if you look at Jim Simons and his investing accomplishments, he's not as well known in mainstream circles, but he's a legend in the quantitative investing world. Originally, you know, Simons was trained as a mathematician, and he took a radically different approach to financial markets than Warren Buffett in that, rather than analyzing individual companies in depth, Simons built a team of PhDs, and they perfected the art and science of building statistical models of programming computers to quickly and efficiently spot patterns to profit from opportunities across the globe. And although not much is known precisely about what they did at Renaissance Technologies, it's estimated that Simons’ flagship Medallion fund returned 39% annualized for investors. That's over 35 years, and that's after charging very hefty 5% fixed and 44% performance fees. We've never seen anything like it in the quantitative world since.

Ashleay: So our listeners can't see me, but I literally have my jaw dropped.

Sébastien: Yeah, and this is stuff of legends. We are talking about these two people here because they're the exception to the rule. And if you’re readers out there – Ashleay, I know you love to read –there's been lots written about Warren Buffett. So, there's this biography of him called Snowball, which is interesting, but my favourite would be The Essays of Warren Buffett, in which they just edited part of his annual shareholder letters, organized by topics. And you learn so much reading this!

And for Jim Simons, on the quantitative side, there's this biography of him which is called The Man Who Solved the Markets. And it's very interesting. Again, they don't give the recipe. You know, you can't read that and say: “Now, I'm off to being the new Warren Buffett or Jim Simons.” But it's very fascinating stuff if you love the world of investing. So, I recommend it here.

Ashleay: And I think that if one reads the financial press, we'll often hear that Buffett is described as a discretionary investor, while Simons was clearly a quant or a quantitative investor. Tej, could you maybe describe the two approaches in more detail?

Tej: Yeah, it's a good question. You know, I think there's a lot in common that these managers have. But the one big distinction is that discretionary managers, you know, they tend to spend a lot of time learning about a small set of companies or assets that they know well. So they'll do things like they'll look at the company's financials in detail. They'll speak to management regularly, analyze the competitors of the company, the suppliers, etc., all to develop a best ideas approach among the subset of companies that you follow. And from that they'll build concentrated portfolios, a small number of holdings, you know, usually 50 to 100 stocks. And because the portfolio is concentrated, you know, a success for a discretionary manager happens if they're right on a high fraction of the bets they make. And so, to use a sports analogy, successful discretionary managers, they have a high batting percentage but relatively few at-bats. Now if you contrast that with a quantitative manager. Quant managers tend to rely more heavily on bigger, larger data sets, statistical analysis and computing power to simultaneously analyze thousands or even tens of thousands of securities at the same time. And so what that does is for a quant manager, they're able to deploy their capital across a wide range of uncorrelated bets. And each of these bets is not really expected to make or lose a lot of money. But when you multiply this process over time, over thousands of securities, you end up in a highly profitable investment process of the type that Jim Simons was able to build. And so, again, to go back to our sports analogy, the quant manager here is looking to maximize the number of at-bats rather than necessarily the batting percentage and relies on diversification to add a lot of value.

Sébastien: Yeah, and maybe just a stat here, just so that everyone understands how difficult the game of investing is. In the Jim Simons book, at some point, they quote that very precisely they know that their strategies are right 50.75% of the time. So it's just more than a coin toss. But as they say in the book, when they're right, they were very right. So, it's just about, you know, making the most out of the times that you're right. And also, the flip side of that is that when you're wrong, and you know that you're going to be wrong a bunch, you want to make sure that you position the size of your trades in a way that no mistake will sink the fund.

It's pretty much the same thing also for discretionary investors. The average is a bit better, but you know, there have been some records of legendary stock pickers in history whose batting average is even a bit below 50%. So, they were right less than 50% of the time. But it's about what you do, how you behave when the trade is in. So, making sure that your mistakes don't sink you. And when you have a trade that works and you know you were right, then make sure that, even, you increase your exposure through time to these ideas so that in the end, you know you benefit the most from where you are right, and you make sure that none of your wrong calls will just sink you. So, it's very much a behavioural approach to be a discretionary investor.

Ashleay: Right. And differences aside, what would you guys say these two approaches have in common?

Tej: I think they have a lot more in common than people realize, actually. Because, you know, sometimes you'll see the investing world divided into the two camps, and I don't even see them as two different camps, but sort of different facets of a common, more encompassing investment philosophy that I personally like to call data driven investing.

Ashleay: And can you tell us more about what data driven investing is?

Tej: I never thought you'd ask, Ashleay!

Ashleay and Sébastien: Ha, ha, ha!

Tej: So yeah, this is something I'm passionate about. Data driven investing to me is the idea that, you know, you want to analyze all available and relevant data before making an investment.

Now, when I say it like that, it sounds obvious, but if you take a step back and you look at what most professional investors do, whether they're discretionary, quantitative, somewhere in between, is what? They'll do their due diligence. They'll do intensive research before they'll allocate capital to a particular trade or investment. And because this due diligence necessarily involves some kind of data, some kind of analysis, some kind of insight, every investor, I'd say, is by definition a data driven investor. And so then the only thing that's different across all of us is, you know, what kinds of data we access, how we transform it into insight, and how those insights are actually used to make investment decisions.

And so if you go back to the two ends of the spectrum, the discretionary/quantitative paradigm, you'll notice that in both cases there's a lot of data involved. As I mentioned earlier, the discretionary PM, you know, they'll go over the company's financials, they'll speak to management, analyze a bunch of different things, all to understand the business better. And while a lot of this data is non-numeric, you know, things like conversations, charts, descriptions, meetings with management, it's still data in some form. It's still insight nonetheless. It's just not tabular, necessarily. And it's doing this kind of analysis well that has made someone like Warren Buffett so immensely popular and successful.

Sébastien: Yeah and....

Tej: And historically… go ahead….

Sébastien: And you know, building this asset allocation team, you know, the two main decisions that we've made through time was: “Who are we going to hire? What kind of talent do we want to add to the team?” And after that: “What kind of data can we purchase that maybe others don't have, that we can have a very long history on and make sure that we understand, you know, the behaviour through time?” Because one thing that's constant through time is human nature, human behaviour. So you can infer from what happened in the 1800s, and it can have a decent impact on how you decide to invest now.

Tej: Absolutely. These kinds of long data sets have been the provenance of the quantitative investors. But, you know, with the advent of things like artificial intelligence, NLP, large language models, Gen AI, even now the quantitative investors are able to access non-numeric data. You know, look at what AI has been able to do for text extraction, for image recognition, things like that. So I'd say the worlds – these two extremes that I'm saying are facets of the same thing, of data driven investing – are becoming closer and closer. So ultimately, to me, it doesn't matter what you call yourself, discretionary, quantitative. To me, that's not the important distinction. It's more about do you have access to the right data? Are you transforming it the right way? Are you extracting every possible insight from it? Are you making good investment decisions using data? To me, that's what this game is all about.

Ashleay: And so, given your comments above, wouldn't it make sense to combine both styles into like a perfect blend? And how would this combination work within the AA team at iAGAM?

Tej: Yeah, I mean, Ashleay, you hit the nail on the head. This is in fact what our philosophy here is within the asset allocation team at iA Global Asset Management.

You know, we're using an optimized blend of what we call expert judgment, that's the wisdom of the portfolio manager – the human – and data driven analytics to essentially power our investment decisions. And so how does this work? Well, you know, what we do is the portfolio managers, us the humans, we use the quantitative models as a key input, a key building block into the overall decisions we make. And because we ourselves are the ones who are able to build and maintain these models, we can ensure that they're appropriately designed for our specific needs. They're optimized for the kinds of data we use and the kinds of decisions we want to make. And so in this way, we're combining the power of the data and the machine while keeping the human element in the investment process, keeping our portfolios explainable, easy to understand, firmly grounded in the macroeconomic and financial theory that we know has worked for decades and decades. It's something that we're very passionate about at iAGAM. It's something we're very excited about. And frankly, it's the investment philosophy we follow, and it's what we continue to invest in within asset allocation at iAGAM.

Ashleay: Well, that concludes this week's episode. Thank you for coming to enlighten us on investment approaches, Tej. And thanks also to you, Sébastien.

Tej: Thank you very much.

Sébastien: It's a pleasure to have you here, Tej, to take the time. And thank you, Ashleay, again for hosting this podcast.

Ashleay: And we'll say thank you to all our listeners as well, and we'll talk again next week.

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