Asset allocation: From theory to practice

Monte Carlo simulation is one of the best tools to help investors come up with appropriate portfolios, Dr. Paul Kaplan says.

Paul Kaplan 22 August, 2012 | 1:00PM Christian Charest
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Christian Charest: I’m here with Dr. Paul Kaplan, Morningstar Canada’s Director of Research. Now Paul, in a previous video we discussed some of the elements that investors and their advisors need to consider when coming up with an asset allocation. This time around I’d like to focus more on the practical side of the equation. How can investors use this information to translate it into an actual portfolio, what's the next step?

Paul Kaplan: Well, the next step after the advisor has gathered all the necessary information about the client from the client - basic information, their age, their goals, number of children they have, their income, when they are thinking about retiring, how much they’ve already saved, how much they’re contributing to their retirement plans and so on - then it's time to put all this information into practice and there are a few tools that really help out with that. One is the very famous idea of the efficient frontier or mean-variance optimization or Markowitz optimization. This idea of doing asset allocation so that as we combine stocks, bonds and other asset classes into diversified portfolios, we’re getting the highest level of expected return per level of risk that we choose.

Now what’s really important here is that there is a lot of misunderstanding these days, unfortunately, where many investors believe simply because something’s more risky it’s going to have higher potential returns. That in general is not true according to Markowitz. What is true is that for any given level of risk that you're willing to take, we can ask the question "what's the highest level of return we might expect over the long run to obtain?" Or we can reverse the question and say "if I'm after a particular level of return, how much risk do I need to take?" So, that's a great tool for helping us really map out some potential asset allocations. What I would recommend, however, is not using the tool simply to come up with one, but come with several that can be discussed between the client and advisor; one more conservative, one more aggressive, one in the middle, and then to use the next tool, which is Monte Carlo Simulation.

Charest: And what exactly is that?

Kaplan: Monte Carlo Simulation is a technique of really using a computer to generate lots and lots of random numbers to bombard our models with lots of different possibilities. The name comes from Monte Carlo, the famous…

Charest: Casino place.

Kaplan: The famous casino place because the method was originally developed to solve problems like what's the probability of getting a winning hand in a card game? Because there are fifty-two cards in the deck and there’s just so many possible ways of shuffling that deck, it’s too many to list them all. So, the idea was let's use the power of computers and let’s just pick a thousand hands at random, and from that we can infer mathematically, statistically, what might work - what a good hand might be. So it's exactly the same idea we’re doing here in asset allocation. What we’re doing is we’re saying, okay, let's try this particular asset allocation. It’s got certain amount of stocks, bonds, cash, real estate, commodities and so on, and let's now bombard this model with various possibilities.

So, what we do is year-in and year-out you pick a completely random set of returns based on some model of how those returns might behave statistically and then what you do is you say, okay, now let’s calculate, given that randomly chosen set of returns, what would have happened to my assets? And then in years going in, in the model, you have money coming in, then later you have money coming out - and to see, ask questions - and by repeating that process repeatedly, not only you have all the years in which we want to model, but we then repeat the whole process 1,000 times, 2,000 times, 5,000 times and then we use basic statistical techniques to answer questions like: asset allocation A gives me 73% chance so I’ll be able to meet all my financial goals; asset allocation B gives me an 80% chance, so B looks like it will be a better choice.

Charest: Sounds like a very data-intensive process. Fortunately with today’s computer technology, it must be lot easier than it would have been, say, ten or twenty years ago.

Kaplan: It’s become much easier. There really have been great technological breakthroughs; both in software and hardware that really let an advisor run literally thousands upon thousands of these trials on their desktop. They will get the results back some times within seconds, possibly minutes depending upon how long the calculation is. What I also like about it is that not only can we vary the asset allocation, we can vary some of the other assumptions. If the person hasn’t retired yet, for example, we can say what about he saves a little bit more. We know that’s going to increase the probability of having a successful retirement. What about instead of retiring at 65, he works for another two years? It’s a really interesting exercise because what people are often surprised by how much better they are going to have a retirement just by working an additional few years.

Charest: And Morningstar has a tool in our Advisor Workstation software that does Monte Carlo Simulation.

Kaplan: We certainly do. We do have a module of our Advisor Workstation software, a financial planning module. It is powered by an internal piece of software called the Ibbotson Wealth Forecasting Engine, which was developed by the asset allocation expert, Ibbotson Associates, which Morningstar acquired in 2006, and its’ a very, very powerful model and can really be useful to help in financial planning.

Charest: Interesting stuff. Thank you very much, Paul.

Kaplan: Thank you.

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Paul Kaplan

Paul Kaplan  Paul Kaplan is Director of Research for Morningstar Canada.

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