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# ETFs for a Modern Portfolio Theory

10/01/2014 10:00 am EST

**Focus:** ETFs

*iSectors uses computer algorithms to develop ETF-based asset allocation models to enhance the returns expected from traditional strategies based on modern portfolio theory. CEO Vern Sumnicht discusses the firm’s strategy and highlights the model’s latest ETF allocations.*

**Steven Halpern**: Our guest today is Vern Sumnicht, CEO and Founder of iSectors. How are you doing today, Vern?

**Vern Sumnicht**: Very good. Thank you, Steve.

**Steven Halpern**: Well, thank you for joining us. iSectors develops a variety of ETF-based asset allocation models, which, in turn, can be used to help develop portfolios meeting specific investment needs. Can you give our listeners a brief overview of how these models are created and the goals they address?

**Vern Sumnicht**: Sure. Let me start with a little bit of history. Modern portfolio theory, that's what the MPT in the name of our iSector's host MPT model references. Modern portfolio theory began and was developed at the University of Chicago back in the late 1950s.

These gentlemen that were the developers of modern portfolio theory received Nobel Prizes in 1990s for that work. In the years after that, a field of study, I guess, in finance developed called post-modern portfolio theory.

It looks at certain principals in certain ways that advisors have historically—or investors have historically—implemented some of the principals of modern portfolio theory. The principals that advisors have typically used to determine asset allocation are one of the focuses that post-modern portfolio theory has discussed.

As an example, historically, investors have looked at risk using standard deviation. Standard deviation tells us that upside risk and downside risk are equally bad. In other words, it says volatility is bad and, if you have an investment that is volatile, that's a bad thing relative to an investment that has less volatility.

Well, post-modern portfolio theory has identified the fact that, you know, upside volatility is really not a problem. Most investors aren’t complaining when expected return was 6% for some particular investment and—because of the volatility—they had a return of 12% in a particular year. They're not going to go, “Oh, I'm very upset with that. I was expecting a 6% return and, gosh, I made 12%.”

We know that investors simply don’t like to lose money. If you would identify risk or talk to any investor, just logically, I guess this is true too, but research has actually also been done on that, I don’t know why, but, you know, the fact is, to most investors losing money is risk, going down is risk, and that's a simple kind of thing.

There's a lot of mathematics in allocating investments and that's one of the points, one of the main key factors of that and it’s a very poor factor. With modern portfolio theory, our model—our post-modern portfolio theory investment model—we took that issue and a few other issues very much like that, and we said, we need to correct it.

If we correct these things, we will be able to show a return that is better than average, let's say, and a risk that's lower than average as compared to some benchmark, for example, the **S&P 500** (SPX)?

We did a lot of research on this—probably four years and probably too much research—and we developed—in our business—it's called an algorithm or a process of steps including mathematical steps that we use to determine asset allocation.

Obviously, it's slightly different than the way it's been done historically. That's really the key to the iSectors post MTP model, it’s a better approach to the mathematical—or better mathematical approach, I guess—to determining asset allocation.

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We're also using asset classes that different from which has been historically, I guess, traditionally used by the investment world. Typically, the investment world likes to split the universe of investments up into large growth/large value, small growth/small value, mid-cap growth/mid-cap value, and then maybe a large blend, small blend/mid-blend.

They call it a nine box, you know, allocation. A nine box division of the market. The problem with that is, over the last 20 years, those different asset classes, large growth/large value, small growth, etc., have become extremely highly correlated.

Their correlations are approaching 1, which means perfectly correlated. In other words, when one of those asset classes goes up, the other one goes up. When one goes down, the other one goes down.

As a matter of fact, in down markets when you would really hope that your diversification among various asset classes would help to reduce your losses. Unfortunately, the correlation among these different asset classes goes up dramatically, and, in effect, you have no diversification. They all go down, they all go up, so your diversification really isn’t reducing risk.

We started another important thing with our post-modern—our iSector’s post-modern portfolio theory allocation—is that we allocate the assets among nine asset classes that are different than each other that have low correlation to each other.

And we're taking the entire market, in effect, the market universe and we divided it into nine—we call them asset classes, some advisors like to tell me that they're prime sectors—but they're basic materials. For example, basic materials, energy, technology, financials, utilities, gold equities, 20-year treasuries, healthcare, those are an example.

That makes up a lot of the difference. Our algorithm looks every month at the changes in 15 different economic and capital market factors, like inflation and money supply, unemployment, capacity, utilization.

We look at the change in those factors and we put those numbers in effect into the equations, and we ask the computer—which has our mathematical process—ask the computer to give us the best, the optimal, asset allocation among those nine asset classes without going below zero.

Give us the highest possible return with the proviso, or the threshold of, don’t go below zero. That's what we do. That's what our post-modern portfolio theory allocation actually, I guess, is and kind of the long version there, which I appreciate your patience.

**Steven Halpern**: I want our listeners to be able to understand the specific ETF Investments that they could consider or follow what your asset allocation models are suggesting as the best strategy for the current environment. If you could tell us just a little about the specific ETFs that make up these nine categories now, so that somebody could see where they should be investing their money today.

**Vern Sumnicht**: Sure. Now realize we're getting closer and closer to the end of the month and we will tweak these at the end of the month again, as things change, but currently, our largest allocation, which has been for most of the year, is healthcare.

We choose to use the **iShares Healthcare ETF** (IYH) and then probably the next largest allocation we have in the portfolio is technology. We've used the **iShares North American Technology ETF** (IGM).

We also have an allocation in energy—**iShares US Energy** (IYE) and in the financials—and in the financials we're using the **ProShares Ultra Financial ETF** (UYG).

Those are the largest allocations. We have from smaller allocations, a couple of % in real estate, through the **iShares US Real Estate ETF** (IYR), a couple of % in the 20-year Treasuries via the **iShares 2-+ Year Treasury Bond ETF** (TLT).

**Steven Halpern**: Well, we really appreciate you taking the time, today. Thank you for joining us.

**Vern Sumnicht**: Thank you, Steven. I appreciate it.

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