Which one will make you richer: a human or a machine?

Ashleay: Welcome to the “In Your Interest!” podcast from iA Financial Group, where we discuss your need-to-know economic news and how it affects your finances. All this in under 10 minutes. We often hear about management with a human and a machine, how do the big asset managers of this world go about creating portfolios and choosing the stocks that will be part of them? Is it a computer that's controlling this or management by one or even a committee of seasoned investors? My name is Ashleay. I'm here with my colleague Sébastien McMahon, our Chief Strategist and Senior Economist here at iA Financial Group. Hi, Sébastien.

Sébastien: Hey, Ashleay.

Ashleay: So, Sébastien, in 2023, can you explain to us who is the one that's taking care of our investment funds?

Sébastien: Well, computer management, committees of seasoned investors, all these answers are good, and each approach has its advantages and disadvantages. So, maybe let's dig first into management by a human. So, other ways to phrase it would be fundamental management or traditional management. Many management firms entrust the management of their funds to seasoned investors who have built up their own “secret recipes” over the years. So, this relies on the managers’ experience and ability to simultaneously read changes in economic conditions, market valuations, ups and downs in investor sentiment. Or maybe they have a very deep understanding of one or multiple specific industries. The human brain is a very powerful tool, and a manager supported by analytical framework honed over time can make informed decisions while managing risk.

Ashleay: Yes, but Sébastien, the brain has its limits.

Sébastien: Of course, and the brain limits the amount of information that a human can process in a short amount of time. So, we're very good at making links between concepts, but it's the quantity of information that we can, that we can process in a limited time frame. That's one issue. Other issues are what we call cognitive biases. This is applied psychology, and there is a lot of research has been done over the last decades on that. So, just to name a few. So, those are kind of errors in human judgment that we all make. And they can be important in finance; like confirmation bias, which is the tendency to give more importance to data or to events that confirms our thinking. So, if we're thinking one thing and then some news come out that confirms our thinking, we'll think this is more important than something that infirms [AM1] our thinking, for example. Representativeness bias, meaning making a judgment based on the information that someone already has, although that information probably does not paint a complete picture of an issue. Availability bias would be giving more importance to information that comes quickly to mind and less to new information that we will get in doing some more research. Or maybe lastly, the bandwagon effect. So, the tendency is to want to stay close to the consensus, to get involved in crowd movements for fear of staying behind. So, the human brain is a very powerful tool, but of course it's not immune to pitfalls.

Ashleay: Absolutely. So, I guess if the brain has its limits, then the computer takes over. Is this a new concept?

Sébastien: It's not a new concept. I just learned recently that the pioneer of quantitative investment strategies was a French mathematician named Louis Bachelier, whose doctoral thesis was published back in 1900. So, computer-based management—other terms would be quantitative, systematic or multifactorial strategies. So, these strategies are based on an advanced mathematical model developed by industry professionals. So, they're made by humans, including programmers, mathematicians, statisticians, economists, financial specialists. They build models and these models deconstruct large amounts of data to arrive at investment decisions. Today, the advance of the advent of networked communications and powerful, inexpensive computers has paved the way for complex computerized mathematical models to guide investment decisions. So, the objectives of these quantitative strategies are to reduce the human bias in the investment process, to use data and technology to overcome human limitations and to allow for an evolving strategy. So, the analysis of thousands of assets and also transpose ability. So, to take a strategy that works in one universe or one asset class and transpose it to other asset classes.

Ashleay: I see. And so, what is the best option?

Sébastien: Well, at iAIM, iA Investment Management, we believe that the combination of both approaches is optimal. So, maybe I'll use the analogy of a car here. So, I don't know if you've been in the market for a car recently, but you have all of this technology now surrounding us. You've got cameras, you've got sensors, you've got cruise control, All of these things are there to help the driver, you know, just focus on the road and let go of the small details. So, the way that we see the combination of the two is just like, for example, if there is a snow storm while you're driving your car, the human intuition would be to remove all of the computer aids that we have, to put both hands squarely on the steering wheel, take control of the car again. And if the road clears up at some point, then you can put again all of the little helps that the technology gives you in the car. So, it's pretty much the same thing that we believe in, in investment. We think that there's room for quantitative strategies to help us. If at some point there is some volatility, maybe we give less weight to these tools until the road clears up again. And then after that we have again a mix of the human and the machine working together in the investment process.

Ashleay: Fantastic. Well, Sébastien, thanks again and I'm looking forward to learning some more on our next podcast.

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