I bet you’re real excited about this concept of the science of investing, right? We’re going to get out our test tubes, and I don’t know what we’ll do on this, but the place I’d like to start is a question that I use with almost all of my clients when I first start talking to them, and that is, Are you an investor or are you a speculator? Because what we find is that if they don’t know the difference between an investor and a speculator, it’s very difficult for them to understand the importance of what I’m going to share with you.

To go back a little bit, when I first got into the insurance business way back right out of college, I really wanted to do financial planning as well as risk management. The problem was, I couldn’t really find an investment program that I felt comfortable with that I could represent to my clients and that they would feel like this was something that would make some sense for them, because it didn’t make any sense to me. And so I just kind of put that on the shelf. I got a Series 7 license, and I just maintained it over a period of years.

Then in the early 1990s, I got introduced to a seminar that was based on evidence-based investing, and it was predicated on five Nobel Prizes. I went to this seminar reluctantly. I didn’t feel like it was really going to be anything that I needed to do. It was eight hours that went by in 10 minutes. I mean, it blew my mind. So what I’m going to show you in this session is what I learned in that seminar. It’s very condensed because it’s not going to be eight hours, but I think you’ll find that the condensation is at least enough, maybe, to stimulate your intellectual curiosity.

Why do we call this the “science of investing”? Well, the reason we call it the science of investing is because it’s evidence-based. It’s based on predictable probabilities, hypothesis testing, all of the things that would go into any type of a scientific analysis. You look at a slide like this, and you’ve all seen these in one way or another, but those different lines have different results over long periods of time. [visual] The question is, are any of them predictable to the extent that they pass a scientific measure? And how can you combine those and use those effectively for clients?

Returns, if you will, are the result of earnings, and this is a risk in the capital markets, obviously. But not all the risk is the same, because there are different volatilities, different amounts of risk, standard deviations, depending on which one of those curves you’re on. You can either chase the market or you can be the market. It’s that simple. Those are your two choices, and I always felt like I didn’t want to chase the market, which was what I was pretty much taught in the early stages of my career.

What I learned in this seminar was that you can be the market, and the advantage to being the market is that you don’t have to constantly be defending the outcomes of your portfolios because you are the benchmark, if you will. So not all risks are the same. If you look at the four dimensions of risk, one is the market itself. We know from all of the evidence, dating back to Harry Markowitz’s Nobel Prize in 1950 — well, 1990, but his research was started in 1950 — that bonds do not beat stocks. Stocks over the long run outperform bonds, and so that’s one dimension.

Another dimension is the size of a company, and we’re going to look specifically at this in a little bit, how the size of a company impacts return over time. The third is relative price, book-to-market ratio. Are companies that have a significant net worth compared to their overall market value a different type of performing asset than a company that has very little in the way of assets? And then the final one is profitability. Intuitively, we know that with profitability, companies that have higher profitability can outperform companies with lower profitability. The problem is, you don’t always know which one or in which order.

If we kind of look at the history of this research, the single-factor model, which was bonds versus stocks, dates back to 1963. That’s really when it was finalized in the academic paper. The second was 1981 when they determined that the size of a company really mattered. It was 1991 when the book-to-market ratio became a factor. And then it’s just been recently that profitability has played a major role in this. What I’m going to do in the time that we’ve got is to show you how to identify these and how you can integrate them together, and the impact that understanding the roles of these various asset classes within the market can have on return over a long period of time. I think you will see why my head exploded after this eight-hour seminar that I was telling you about.

If we look at the market as a whole, it might look like that. [visual] Lots of stocks. If we want to reshape the market to take into consideration capitalization, the price of a stock times the number of shares outstanding, it might settle down like this, with sediment at the bottom. [visual] If we brought in book-to-value ratio, you would have a further segregation of the marketplace. And then if you integrate in profitability, you have a further way of being able to screen to see which parts of the market are outperforming other parts of the market.

So what are the measures of risk? Volatility, expected return, market factors. I don’t know if any of you have ever seen this illustration, but if I had a jar of jelly beans, and I let all of you guess how many jelly beans were in that jar — I don’t know how many we’ve got in this area, but let’s say we’ve got 50 — we would get 50 different answers, probably. They did this with a study of about 5,000, as I recall, and what happened was, when they tabulated the votes, it became a Gaussian distribution. That’s another way of saying standard deviation. It was a normal bell-shaped curve. I learned this in the Ph.D. program at the American College; otherwise, I wouldn’t have known this.

The lowest vote, the lowest tabulation, was 409, so somebody looked at that jar of jelly beans and said, “There are 409 jelly beans in there.” I don’t know why he didn’t say 410 or 405, but he said 409. On the other side was a high of 5,365, so you had a range in all the voters between 5,365 and 409. But it turned out that the average of all the votes was 1,653. So how many jelly beans do you think were in the jar? It was 1,639, so that’s how standard deviation works. That’s how normal distributions work. Normal distributions are the result of predictable probabilities over a long period of time, and you can apply that same logic, that same outcome, to stock prices, for either individual stocks or the market as a whole.

And, of course, if you do it with individual stocks, there are a lot of factors that could come into play, such as management, obsolescence, technology, all those types of things. But when you do it as a market as a whole, that tends to disperse that risk, and markets tend, over a long period of time, to perform the same way they’ve always performed. And the more data points you have, 50, 60, 70, 90 years in some cases, the higher the probability is that you’re going to get the historical predicted rate of return for that asset class.

In standard deviation, this chart kind of gives you an overview of how it works. [visual] You’ve got the mean. The standard deviation is roughly where two-thirds, if you add both sides together, of all the events fall. The next standard deviation, the second standard deviation, could be like 95. The third standard deviation would be 99. So you could have a one or a two or a three event, and, of course, black swans are threes. Anytime you get a black swan event, like we did for instance in 2008, you’re out very far on the scale.

One of the things that’s really important to understand is that markets integrate the combined knowledge of all components. It takes everything that’s going on into the market, and it combines all of those into price. There’s competition, then, between buyers and sellers, supply and demand. And then you have trading that’s based on vast amounts of information that’s flowing all of a sudden, and then you have individual investors who are trying to assess this information and make decisions on how they’re going to invest.

Now, who do you think is falling behind the curve? It’s the investors, because by the time they get the information, it’s already been assimilated into the process. In fact, every day there’s $407 billion (this was at the end of 2017) that’s trading daily, so as an individual investor, who could hope to keep up with that? This goes back to what I said before. Are you an investor or are you a speculator? See, in order to be successful in our business, you have to have a philosophy, and if you have a philosophy, then what that philosophy does is it helps you answer that question, because if you believe as an investor that you can anticipate where the markets are going and outguess the stocks, well then you have a much different attitude than if you’re going to be the market and allow the market to just move.

Now, we trust the markets every day. Let’s take fish, for instance. One day the price is $6.50, another day it’s $7.10, another day it’s $6.85, and the daily market will vary based on supply and demand, market forces, the number that have been caught, the storms or whatever. The price estimates that current value, so if you’re going to buy a pound of fish, one day it’s $6.50 and another it’s $6.85, you’re going to have some variability in your cost. Stock prices work the same way. They reflect the known value, and the faster all that information is assimilated, the faster that price gets set, and then that price becomes the point by which you’ll get variation during a given period of time.

All available information flows through this $407 billion that’s being traded every day and plays out into prices. The market effectively integrates all of this information, and the world equity markets then are processing all of this data. Take orange juice. Reuters had an article in January 2012, when prices adjusted unexpectedly, that the price went up from Friday to Tuesday. So the markets react. Here’s an example of where Berkshire Hathaway bought Heinz, and you can see that the volume in the stock was very low. [visual] All of a sudden, boom, it went up.

Another great story, if you’ve ever done the research, is on the Challenger. When the Challenger exploded, within minutes four stocks plummeted. And when those four stocks plummeted, they turned out to be the four stocks that were integral in the construction of the Challenger, but one of them fell dramatically, and it was the one with the O-ring. Well, how did the market know that so quickly? Investors just can’t keep up with that, so when we, as advisors, are suggesting, “Well now’s the time to move into this, or now is the time to move into that, or we’re hearing about Brexit like she was talking about,” or whatever, are we speculators or are we investors?

Most of the people I talk to, I’ll ask them the question “Are you a speculator or an investor?” And what do you think they say? Well, they almost always say the same thing. They say “investor.” But then we’ll talk about behaviors like — she mentioned the term. I can’t think of it right now, but where they put all the money into the economy in 2008. And then you come up to the end of the year, and they’ve got the budget crisis. I’ll have investors call me and say, “Well, should we be getting out?” and that type of thing. I’ll ask them if they are speculators or investors. Because if they’re investors, they just push through that because it’s not going to matter. If you heard Tim Holland, he was talking about Brinker’s attitude and how Brinker knows that the markets over long periods of time don’t really react to the politics, other than for a short time.

So if somebody is in this for a gain for two or three months, are they the right type of client for you? Is this serious money or is this mad money, as somebody said in another session? You have to look beyond the noise, the “retire rich, sell stocks now.” I’ve seen presentations where they take the headlines of “Money” magazine and some of the other magazines, and it’s a riot. “Buy these 10; sell these four; this is the fastest-growing segment,” and whatever. That’s all confusing, and it’s all noise, and our job is to bring some form of reason to the chaos. If we bring reason to the chaos, then we can establish ourselves as long-term analysts and asset managers for our clients. Like I say, these messages just stir up anxiety, and they chase the latest fad. We have to have a philosophy, and if your philosophy is long-term markets, you’re going to do a lot better.

This is an illustration, if you will, of all the mutual funds in the United States at the end of 2017, and if you broke them up, you would have found that there were 1,500 of fixed income, 1,050 of international, and 2,183 of U.S. equities. [visual] How have these done? These are all the funds, and so it would include ETFs; it would include index-based and actively managed. There were 2,867 as of 2012, so that’s a five-year period, and 82 percent survived that five-year period. So in other words, 18 percent of them disappeared. They were no longer around. And of those, 26 percent actually beat their benchmark or matched their benchmark. So you had a one out of four chance of picking the right fund by throwing the dart at the dartboard.

If you look over a 10-year period, 2007 to 2017, there were 3,229 in that cohort. It shows that 58 percent were survivors, so here we lost 42 percent, and only 22 percent beat their benchmark. And if we go out over 15 years, you had 2,828. You had 51 percent as survivors, and 14 percent were winners. So the further out you get, the harder it is to match the market. And then you take into consideration the fees for trying to match the market, and you have to question whether or not this is the best way to go. Was it Holland? Somebody who was talking about the Fidelity study, where Lynch had gotten a 20 percent compounded return over the 20+ years that it was managing it, and they looked to see what the investors got. The investors got a negative return. Well, why is that? It’s because they jump in and out, but it’s also because you can pick the wrong horse in the race.

Here’s another way to look at it. You’ve got funds in that same period of time, looking at three consecutive years, after they were in the top 25. From 2004 to 2008, 33 percent were still in the top 25, then 28, 21, 25. So you can see over this long period of time, the average of 26 were in the top 25 percent of the Morningstar five-star rating, the rating system. Again, my head was exploding as I was getting this information, and I’m thinking, What is the answer? How do you try to beat that? Picking the fastest lane is a stressful guessing game, and that’s not what we want to be doing.

You really basically have two choices. You can try to outwit the market, or you can try to be the market. The financial markets have always rewarded long-term investors. People expect the positive return on the capital that they give you, and so historically, equity and bond markets have grown over time. This is one of my favorite illustrations. If you Google “U.S. bears and bulls,” I think you can get this chart on the internet very easily. [visual] What we’re going to see here is a comparison of bears and bulls for 90 years. In section A here, this was right after the Depression. You can see the bear market was a pretty intense red there, but you can see the market that followed that was pretty substantial. And in fact, over that period of time, the average return was like 17 percent, and it lasted something like 13 years, almost 14 years.

If we go to the next section, after the next downturn, the next recession/depression, it went 15 years in B. [visual] If you look at C, it went over 20 years, and it was over a 1,000 percent growth over that period of time. If you go to D, which was between 1985 probably and 2000, before the tech burst, it was 12 years, and that was a 800 and something percent return. Now we’re in one that I think looks like this, and so the question is, what’s the green going to be? [visual] If you look at that pattern of 800, 900, 1,000 percent compounded returns, the market that we’re in right now is only up about 500 percent since the downturn of the tech bubble. Is there another 300 or 400 percent in it? You’d have to think, looking at history, that there probably is, but who knows?

A major role in this is volatility. I think Tim put up the VIX Index, as I recall. What does the market look like overlaid on that? You’ve got reverse correlations going on here, where oftentimes the more volatile it is, the market falls. But how do you build that into your portfolio? I like to use this. [visual] I use this in the workshop, so some of you may have seen it, but if you’ve got Portfolio A that earned 10 percent compounded every year for five years, you have a 10 percent return with a 10 percent IRR. In other words, this is the best it can be. It can never be any better than the IRR.

If we take a portfolio that bounced around a little bit, up 20, down 5, down 10, but got the same 10 percent, the same exact 10 percent, what was the IRR? Because when it goes down, it’s got to go up more to get back to even. So if it goes down 50 percent, it’s got to go up 100 percent to get back to even. That impact in volatility is what impacts IRR. In this case, the IRR was 1 percent less. Same returns, same average return over that period of time, but the internal rate of return was much different.

Well, how did that play out with the money? If I put in $100,000, a 10 percent IRR with a 10 percent average went to 161. But with the 9 percent IRR, it went to 153. So you lost 8 percent from volatility. Now, what that told me, and I’m sure it tells you the same thing, is that how you put your portfolios together and manage the standard deviation of risk is just as important as the asset allocation and diversification. You have to manage both of those components at the same time, and the degree to which you can manage the standard deviation, the higher the probability is you’re going to deliver a better return over time.

Here’s the S&P Index. [visual] These are all the downs. These are all the ups. It’s again that three out of four over that period of time, 75 percent were up if you put them together. Now, you look at that, and you really can’t tell much. It’s just a bunch of bars going in different directions. But it certainly dominates with the green. That was a 10.2 percent compounded return over a 90-year period.

Here’s that same chart, only doing it on compounding, with $1 growing over that period of time. [visual] That’s 10.2 percent compounded growth, taking into consideration the ups and downs along the way. Now, what’s the dotted line? The dotted line is the expected return. The expected return is the terminology they use in all the academic literature to explain the performance of an asset class, a group of events, a group of returns, over a long period. Understanding expected return becomes the core, if you will, of the science of investing.

Now, what’s not intuitively obvious is that there are sublines along the way. In other words, what happens is, when the market goes down and stabilizes, it gets back on the expected return, and when the market goes up and stabilizes, it gets back on the expected return. When it goes back down, it gets back on the expected return. So you’re always on that 10.2 percent compounding, once whatever the disruption event was that took place. When people say, “Well, when should I invest?” the answer is now. And they say, “Should I do dollar cost averaging, or should I go in in a lump sum?” Well, it kind of depends on your philosophy over the long run. We’ll do both, but generally speaking, I prefer to just put it in because you’re going to get on the compounded return, on the expected return.

So ignore the noise. Eugene Fama, who got the Nobel Prize in 2013, says there is no information whatsoever in three- to five-year returns. Now, compare that to the way most people think whom we know, whom we work with. They want to talk about, well what did the market do in October? And is this a trend? Is the market going to start going down after that? Are we in the beginning of a recession? Are we in the beginning of the end of the bull market? I mean, you hear all sorts of things when you have a disruption like this. What Fama says is that you can’t tell anything from a three- to five-year period of time. You’ve got to look out 30, 40, 50 years to see what the trend is over a period of time. And again, with all the scientific testing, with all the hypothesis testing and probabilities, each of the asset classes that I’m going to show you has a predictable return to within almost the second standard deviation. That’s a very high probability. Its 95 percent confidence level is huge when it comes to this type of measurements.

With all that in mind, what does it take to invest successfully? I mean, how do you take and integrate all that information in and put together a successful program? I think Warren Buffett had the answer. He says you need two things. He says you don’t have to be the sharpest knife in the drawer. He says you don’t have to have unusual business insights or insider information. You need two things. The first thing that you need is a sound intellectual framework. Now, is there only one sound intellectual framework? Probably not. You know, I told you about the four dimensions that we’re going to look at here. There are probably 350 dimensions, but what’s interesting is, if you really break down all the 350 dimensions, they essentially break down to four. And if you understand those four and can manage those four, you’re really understanding all of those dimensions.

Could we put a portfolio together on momentum? Sure, but would it have the same scientific probabilities and outcomes over a long period of time as the methodology I’m going to show you? Not according to the literature. Not according to any of the research that’s been done. So the first thing you have to do is to have a sound intellectual framework. The second thing you have to do, which is really the behavioral side of our business, is you have to provide them with guardrails in order to protect them from destroying their intellectual framework when chaos comes into the marketplace.

You have an October, and people start to get nervous, and they want to rethink what they’re doing. Now, we have about 500 clients whom we manage money for. In October I got two calls, and I knew both of them would call because they always call. No matter what I say to them, no matter how I calm them down, no matter how many times they have said I am right and they should have listened to me, they call. We have a good laugh over it.

To understand markets, then, we have to understand this expected return. We have to be able to calculate what that expected return is, and we have to be able to have a mathematical way of being able to blend expected returns so that our expected return line for our portfolio makes sense from a volatility and a diversification perspective. When we look at the basic drivers within this model: I told you about markets, I told you about size, I told you about book-to-market ratio and profitability. In the bonds, there are two of them. There’s term and then there’s the credit of the bond. We’re not going to look at bonds here.

What is smart diversification? Is smart diversification the S&P 500, where you’re diversified over 500 stocks, or is smart diversification 15,000 stocks, spread out internationally, domestically, in emerging markets where you’re taking advantage of the fact that over 50 percent of the world’s wealth is outside of the United States? When I first started doing this, that number was 70 percent of the world’s wealth and it was in the United States. So has the United States gotten less wealthy over the last 50 years, 40 years? No, what’s happened is that the U.S. has had huge economic growth, but so has the world. If you want a well-rounded, diversified portfolio with uncorrelated assets that can give you an expected return over a long period of time, it’s smart to have international in there.

The CRSP database, the Center for Research in Security Prices, is a database that Fama and French put together a number of years ago that gives the price history of every publicly traded stock going back to 1926. This is a huge asset for research because it gives you all of this data. You can go into that and slice and dice any way you want. There are 32,000 stocks that are tabulated within this database. If we look at the CRSP Index, based on 1 to 10, so the very largest stocks are in the decile 1, the very smallest stocks are in decile 10, and then you’ve got the eight in between, $1 invested in that index, which is the total market, grew to $5,977 over the last 90 years.

If we look at the S&P and compare it, which is considered to be a proxy for the market as a whole, it went to $7,339, so it is pretty close — 10.2 percent for the S&P, 9.9 percent for the CRSP. The only thing we did with the S&P was that we got 500 stocks instead of 7,800, which is what’s in this CRSP Index. I don’t know how many of you have had this conversation with a client recently. The client says, “What’s happening with my portfolio? The market’s so up.” And I say, “Well, how are you defining up?” He says, “Well, the Dow was up 200 points, 300 points. How did that play into my deal?” I say, “How many stocks are in the Dow?” I mean, it’s an entirely different approach than the approach that we’re using. If you want to bet on the top 30 industrial companies in the United States, well that’s fine. But that’s not what we do. That’s not being an investor, in my mind.

We’ve heard a lot about inflation and what was said about the value of money was that it was 3 percent, which is the historical bond rate over the last umpteen years. You add inflation to it, and that’s where you get the inflation-adjusted return for money. If you take the inflation-adjusted return for money on the S&P, it went to $7,339. If you took the inflation out, what do you think it went to? Write down a number that you think it went to, or commit it in your head. Now, I get all sorts of answers to that — 3,000, 4,000, 2,000. Very few people ever would guess it’s 543. What’s really interesting about 543 is that that’s a 7.6 compounded return over 90 years, so you can see if 10.2 is $7,339, and 7.6 is 543. A quarter of a percent, 10 basis points improvement in return, can have a huge impact over a long period of time.

Managing fees, managing volatility, having the right diversification, these are all tools that you can use to build a portfolio that has a higher probability with an expected return of giving you a better result than if you have an unmanaged market where you’re not taking or paying attention to any of those issues.

We talked about company size. Let’s look at that a little bit more. This is a picture of the large growth market in the United States. [visual] This is all of the publicly traded companies that would be considered large growth. This would be a picture of all the companies for large value. Here’s a picture of small growth and small value. You can see, on a capitalization basis, the boxes are based on their fair market value, the price times the number of shares. There’s a huge tilt in the marketplace toward large companies, and it’s pretty evenly divided between large growth and large value. So how do you determine who these are? Well, you take their capitalization, so you take and multiply the current price times the number of shares, and rank all of the companies from the very largest to the very smallest. And if you take a median, the one that’s right in the middle, everything above that’s large, and everything below that is small.

That’s pretty much what was done here, except that we added the book-to-market aspect to it, which I’ll explain to you in a little bit. But you can see that the smalls are way down here at the bottom, and the larges are all up here because this is being done on a capitalization basis, not on the number of stocks. OK, this is all of the publicly traded stocks in the United States. [visual] We’re going to draw that line in the median that I just told you about. So we’ve got large cap and small cap.

Let’s look at the evidence. If we look at the returns of the large cap, and we look at the returns of the small cap over a 90-year period of time, and we look at it on a one-year rolling basis, January to January, February to February, there are 1,045 periods in that time frame. And 57 percent of the time, small beats large. So what does that mean? That means 43 percent of the time, large beats small, so you’ve got to realize that there’s going to be a flow in this, but over the long period of time, 57 percent of the smalls are going to beat it.

But that’s on a one-year basis. Let’s look at a five-year basis. Now it goes to 64 percent, so if we go January to January, 26 to 31, February to February, 26 to 31, and so on. There are a fewer number at this point in time. I think there are 967 periods, or something like that. Now we go to 64 percent. If we look at 10 years, it’s 72 percent. If we look at 15-year rolls, it’s 82 percent. So 82 percent of the time, small is going to beat large over long periods of time. So if you’re going to tilt your portfolio, should you tilt it toward the large-cap stocks, or should you tilt it more toward the small-cap stocks in order to be able to take advantage of it? And why would you do that? Well, I’m going to show you some dollar numbers here in a little bit that show you the magnitude of the difference of return over that period of time because, in fact, it’s over 1 percent.

Here’s a chart that just shows on the 10-year premiums, if you subtract small from large, how often small beats. [visual] The green is small beating large; the red is large beating small. You can see the sine curve that exists there.

OK, let’s look at book value. Book value is pretty simple. Book value is the net worth of the company or the liquidation value of the company divided by its capitalization. We just figured out what its capitalization was, so we’re going to divide that into its book value. We can now take another median in each of those boxes. We now have large-cap growth, large-cap value. We’ve got small-cap growth and small-cap value. Again, if we go back to the one-year look, on a one-year basis, it’s 61 percent over that same 1,045 rolling periods, value beats growth. If we look on a five-year basis, it’s 77 percent of time that value beats growth. Well, what about 10 years? For 10 years, it’s now 88 percent, and it’s almost 100 percent on 15 years. So there is a value premium. There is a historically measurable, highly predictable value premium over a long period of time.

Where do most, especially in the wire house world, typically go? They typically go to large growth and some large value. We do an analysis of portfolios in our shop. We’ll take somebody’s portfolio, we’ll deconstruct it, we’ll break out, using Morningstar, what percentage of the portfolio is large, small, growth, value. I would say 95 to 98 percent of the time, the small-growth, small-value segment is less than 1 to 2 percent of the total portfolio. There may be some slop-over into the mid-caps, but it’s almost always that 60, 70 or 80 percent of the portfolio is in large cap.

Here we see that there is a premium, on a probability basis, for value and for small caps. [visual] Here’s another chart, so you could see the delta between the small caps versus the large caps, or the value versus the growth. This is another chart that basically does the same thing.

What about profitability? We look at the same data. We look at the same computations. We look at the same probabilities. And 71 percent of the time, companies with high probability beat companies with low probability. And again, I mean this isn’t Einstein stuff. You would expect that, because the market is pricing, if you will, the future value of earnings. So you would expect if companies have higher earnings, they’re going to have a higher price at any given point in time. If we look at a five-year roll, it’s 82 percent. If we look at 10 years and 15 years, they’re both 100 percent. So why would you not tilt toward high profitability in your portfolio if you’re dealing with an investor who has a longer than 10-year time frame?

I know you all could articulate this next point very easily. If you’re talking to 65- or 70-year-olds, and you ask them what their time frame is, they’re going to usually tell you what? When they’re going to retire. That’s their time frame. You know, one year, two years, five years, whatever that is. But what’s their real time frame? Their real time frame is five plus life expectancy. So most of the people whom we deal with in that age range have a 10- or a 15-year life expectancy as their time horizon. It would not be unusual to be looking out at the probabilities of 10-, 15- or 20-year rolling periods to be able to capture these premiums.

Here’s a chart that just shows high versus low. [visual] We’ve seen the Callan chart, and we know with the Callan chart, one of the things that’s really interesting is following one stock to see how it’s done over a period of time. And the reason for diversification is that no one asset class is a winner over a long period of time. So you want to have diversification; you don’t want to put all your eggs into one basket.

Now the question is, what is the best way to allocate? If we know all of this, how do we actually build a portfolio that takes into consideration large cap, small cap and profitability? We could just put 25 percent in each box. That would be one way to do it. We could try to pick one and be a speculator. And, of course, this is the way the stock market has done it for years. But here’s a kind of an interesting one if we look at this. Here’s large growth, if you do it on an equal amount in every stock. Instead of putting your money and allocating it based on capitalization, we’re going to take the total portfolio and say, “OK, Amazon’s 2.3 percent of the total market, so we’ll put 2.3 percent of our portfolio in Amazon and 3.5 percent into Microsoft,” or whatever all those percentages are. We’re going to take all the number of stocks, 7,800, and we’re going to divide that into our portfolio, and then we’re going to take 1/7,800th, and we’re going to put it in each and every stock.

If you did that, $1 and the total market grew to what? Remember, $5,977. So, $1 in that box on an equal distribution basis grew to $3,076. In large value, it grew to $16,000, so that just shows you over long periods of time how much of a tilt there is between that. The large growth grew at around 9 percent. That’s not bad. We’d probably all take 9 percent. The large growth grew at 11 percent. Small growth grew to $2,257, so small growth even underperformed large growth over that same period of time. That was like an 8.9 percent compounded IRR.

What do you think small value went to? $1? Write down what you would think would be the answer to that, based on these numbers that you’re seeing here and what I’ve told you. [visual] Everybody got a number? I’m hearing 50,000, so I got 50,000, going once.

Audience: 35.

35? OK, we got 50, 35, 18, 84,000, so 14 percent compounded IRR over 90 years. I’ve had wholesalers of different asset classes come in who have gone through a presentation. What I’m showing you here is pretty much what I show all clients before I’ll let them invest. [visual] It’s sort of a test. If they can pass the test, if they can sit through this and understand it, then we’ll deal with them. But I’ve had wholesalers look at this and not understand this. I mean, it blows them away. They’ve never seen these statistics before.

Let’s just take the United States, and let’s take five funds that capture what I’m talking about. One fund captures everything. Another fund captures large growth, large value, small growth, small value. We’re going to build a portfolio based on the expected returns of each one of those asset classes because large value has its own expected return, so we’ve got expected return vectors then, if you will, for each of these asset classes. Does that make sense? So how do we combine those into one so that we know we’ve got the optimum return for the minimum amount of risk? We’ve got to do that calculation.

We put together an algorithm in our office to be able to do this. We could do 100 percent in the total market, so that’s what core is. Or we could put 95 percent in the total market and put 5 percent into U.S. vector, so that’s a combination of large cap and mid-cap. We could put 95 and 5 into just large value, the vector, which is, like I said, more mid-cap, and the core. Or we could go 85, 5 and 5. Or we could go 80, 5, 5, 5 and 5. So we could keep doing that all day long. How many combinations are there if you’ve got five funds on 5 percent increments? Well, there are 3,125 combinations.

I’ll ask the client, “Which one’s the best one?” And of course, he’ll say, “I don’t know; that’s what I pay you for. That’s why I want you to do this.” How would you determine them? Well, you could take each one of those asset classes and figure out how much percentage to put in on those based on this plot of the average and the risk. We introduce large value into this, and we bring in our bonds if we want to use an integrated portfolio. So we might get moderate with these percentages. Or we might get aggressive, all equity, with these percentages. But you’ll notice the red line, and the red line is an actual line that comes off of the data that shows the optimum return at every level of risk. [visual] This is called the “efficient frontier.” If you take our data and plot it, it looks like that.

So we’ll create these plots. [visual] I can create this for from 1986 to today, from 1970 to today, from 2000 to today. This is all 3,125. You can see where the edge is. That’s the efficient frontier. And this yellow dot that I just brought in could be one specific portfolio that we’re looking at because we could take that portfolio and do the same analysis and see where they are on the efficient frontier. Now, if I’m sitting and talking to you as a client, and I say, “Would you like to be on the line, or do you want to be where your dot is?” what do you think she’s going to say once she has a fair comprehension of this? I mean, she’s going to want to be on that line.

Here’s a scatter plot for 1970, and you can see here what the mean return would have been for that group. Here it is going back to 1986, and here it is in 2000. It changes a little bit, but pretty much the curve stays the same. I mean, you’re going to get some modest changes over time, but because of the stability of these returns, going more than three to five years like Fama says, using an intellectual framework, you can find the efficient frontier. And you can pick the right spot on that line, if you will, in order to be able to identify the optimum portfolio.

This is a chart that we create for our clients and our expected return analysis. [visual] In this particular case, we calculated their efficient frontier with the diamonds. The yellow was where they were. We’ll ask them, “If you want to, you can get a higher return for the same amount of risk, or you can keep the same return and have less risk in your portfolio. Which makes more sense to you?” We’ll walk them through those choices.

If we take that and we back test it, we look at these asset classes using the expected return, and we go back to 1990, and we remember that the market as a whole went to $5,977, what do you think the efficient frontier did over that period of time, optimizing the value in each portfolio, in each one of the asset classes, with the risk? What I’ll say is that we’re going to look at the same data, the same exact stocks. We’re not going to change the stocks. It’s the same markets, the same risk, the same time frame. What do you think using this science and building a portfolio would have done? Write down what you think that number would be. OK, any guesses?

Audience: 150,000.

150,000? I want to invest with you. Anybody?

Audience: 25,014.

25,014? Hmm, that’s a good guess. 18,000? So what does it turn out to be? 25,123. So you can see that there’s a four times multiple by just applying some science. There’s no voodoo in this at all, is there? I mean, all we’ve done is just take and combine the expected returns over a long period of time. Now, is there any guarantee that in the future the markets are going to perform exactly like they have in the past? Absolutely not, but on a scientifically hypothesis-tested methodology using 90 data points, you’ve got a pretty good argument that this is much more likely to happen than if you ignored it and didn’t pay any attention to it at all.

Clients will say, “What’s your role in this? How do you work with us?” Then I’ll say, “Well, do you have a favorite restaurant?” And they’ll say, “Yeah.” I’ll say, “OK, so why do you go to that restaurant?” And they say, “Well, I like the ambience. I like the food. I like the menu, the prices, all of those types of things.” I say, “Well, we’re the same way. We use the best recipes, so that’s the Nobel Prizes, the five Nobel Prizes that are behind this. We use the best ingredients, which are the fund family that we use. And we’re the chef, and we put those together to create the portfolio.”

As advisors, what we tell clients that they need to do, and we’ll help them do it, is to create an investment plan that fits their needs and risk tolerance, that we’ll help structure a portfolio along the dimensions of expected return, that we’ll diversify over 15,000 stocks globally, that we’ll manage expenses, turnover and taxes for them. And more important than anything else, we’ll stay disciplined while the market plays its games.

A big role that we have with clients is to help manage emotions, and of course, there’s a whole range of emotions like optimism and elation. We’ve had elation for quite a while. Now we’re getting nervousness, maybe fear, and then it swings back to optimism. You’ve seen studies, I’m sure, showing that if people invest based on their emotions, they underperform the market significantly over time. In the behavioral world, Daniel Kahneman has a book called “Thinking, Fast and Slow,” and in it he talks about how fear has a magnitude of two to three times greater than the elation over a good market. So it’s no wonder that when people see themselves losing money, they become nervous.

Part of our whole process has to be to help them manage that fear, to help them understand that markets go up and markets go down. In fact, that’s something that I say all the time: Markets go up; markets go down. So avoid market timing. Market timing doesn’t work. I can’t remember who it was who was talking about the four ways: buy-and-hold, strategic, market timing, and I can’t remember what the other one was. The point being that there’s a number of different ways to be able to approach the market. What is your philosophy? What is the best way to do that?

We are an efficient market, evidence-based money manager who believes in the efficient frontier. That’s the philosophy we sell. If people like it, they come with us; they stay with us. If they don’t like it, they’re better off someplace else because I don’t want a book of business that is counter to my philosophy, because all that does is take my time, and it causes us to not do a very good job for them.

This has always been an interesting chart. [visual] It says that if you invested in the market from 1990 to 2017, over that period of time, $1,000 would have grown to $13,000, but then it dropped to 12,000, 9,000, 5,000, 3,000 and the one-month treasuries got 2,100. So if you missed the 25 best days in the market between 1990 and 2017 because you went out and didn’t get back in time, and if you look at the data, almost always the biggest bump is right after the down. When the Dow turns, and nobody’s in then because they’re going to wait to see what the market’s going to do, is this a false, what do they call it? A dead cat flop kind of thing, a bounce?

Audience: You said you use a certain family of funds. What do you use?

We use a company called Dimensional Funds. It’s the fifth or sixth largest fund company in the world. But you can do this with Vanguard. You can do this with any of the companies.

Audience: And are they index funds?

No, they’re evidence-based funds, market-based funds. Index funds, by the way — if you’re not familiar with the definition — follows something else. It’s slavishly related to whatever that is, and so they have to follow the top 500 stocks, for instance, if it’s the S&P Index. The Russell 3,000, it’s always those 3,000 stocks. That takes into consideration no human ability to evaluate or look at what’s going on in those markets, so you can’t get rid of a company that’s about to have a huge lawsuit, for instance, if you’re in an index.

Audience: Based on the numbers you showed and the research, it appears to me that if some 25-year-old came in, the best place for him or her to be would be 100 percent small value. Why wouldn’t you just dump them all in small value 100 percent, based on what you’ve shown?

You definitely could do that, but if you look at the Callan chart, over a long period of time, since the market goes up and down in those various things, if somebody would be more comfortable with diversification, then you might go that way as well. Yeah, but there’s no reason to not do that, if you can afford to take the risk. If you’re a 25-year-old, and you’re not planning on touching that money for 40 years or 45 years, you could do that, but eventually I think you’d want to move out of that.

Audience: Yes. Guy, I love your research, and I could relate to that research. I’ve seen it before. The only issue would be that the research timing has most of those charts going about 90 years. That’s not human because if you want to look at 90 years of things, I have to invest when I’m a baby, day one, and then when I die at age 90, that would be a good timing. Most of the people invest 10, 20 or 30 years, even young people can invest the most at 30 or 40 years. Nobody can relate to a timing of 90 years. I would think research in smaller segments of time would be meaningful because if I show a client 90 years return, he or she will say, “I’m not going to live 90 years.”

Baker: OK, I think the point you’re making is, is the data valid for somebody who’s 65 and who’s only going to have 15 or 20 years compared to somebody like your 25-year-old, right?

Audience: Even if a 40-year-old is going to live to age 90, there are only 50 years of investing. Nobody has 90 years of investing time frame.

Baker: OK. With all kindness, the point of this was not that this is a 90-year time horizon. The point of this is that over 90 years, you’ve got all these data points, which gives you a high probability that it’s not going to fluctuate a whole lot. It doesn’t matter whether it was 90 years, 50 years, 20 years or 30 years, but that you can count on the expected return over a long period of time. This chart up here goes back to 1970, using exactly the same data. [visual] You actually get a better return using the same data with our algorithms than you do using the 90-year one. The 90-year time is more conservative than using 2000.

The question would be, am I comfortable using data from 2000, which is much more current, but is only 17 years, compared to 40 years or 50 years going back to 1970 or going back to 1926? This really has nothing to do with the time frame of returns back then versus here. What that basically is saying is that over the ups and downs of the market, two wars, all of the oil crises, everything that we’ve had over that period of time, look at the consistency of that return, and it hasn’t changed that much if you only look over 20 years, 30 years or 40 years.

Audience: Just for the record, there was an article in the “Journal of Financial Planning” about three to four years ago looking at asset classes, and the article said that the best combined tested two asset classes was small cap and government securities, and that nobody in his right mind would go to his client and say only use these two asset classes for the rest of your life. Then he went into about six other asset classes that could be slipped in as proxies, but it’s a wonderful article in the “Journal of Financial Planning,” the CFP magazine.

Baker: You know, one of the nice things about this academic journey I was on for the last few years was that I got to read a lot of those types of articles. And yeah, there’s no doubt that small cap has done well, but what I showed you was small cap broken into small growth and small value. So when you look at $1 in the large-cap market, I’m trying to think, because of the large-cap value, it goes to like 9,000. And in the small cap, it goes to about 25. But that’s taking into effect the small value and the small growth and combining those together over that period of time. When you take the small growth out, you’ve got small value at 84,000. So while that article is correct about small cap, it didn’t really dig deep enough into the subject. And you’re right, but remember, if you combine government with small cap, you’ve got a balanced portfolio, and I think we all would agree that it’s nice to have some large value in that portfolio and be much more diversified than those two classes.

Audience: Thank you so much. You show that value is tremendously helpful. My question is, in your actual portfolios that we’re doing, how much and how heavily do you actually tilt toward small value versus growth? How heavy do you go large value versus growth? Do you go a little bit tilted? More like two-thirds to value, one-third to growth? Do you go very heavy to small cap, or do you say you know what? Small growth, more risk and no reward versus large growth, so maybe we use very little in the small growth place, but use a ton of value, but how much and how heavy do you tilt value large cap to small cap?

Baker: Sure, that’s a great question. The answer is, we put 100 percent in small value. No, not really. OK, that chart that I showed you with the 3,125. What that chart’s designed to do is to optimize in the 3,125 combinations to answer to your question. And the answer to your question is, same market, so we’re not changing the market. The thing that I like about the process that we use is you’re not going to find Microsoft in every one of those funds. You’re not going to find Amazon in every one of those funds. You’re not going to find Twitter in every one of those funds. And you’re not going to be so heavily weighted toward those high-tech whatever they are today, whatever you want to call them, that you’re going to get trapped in the downstroke that we just saw recently in some of those, or what happened in 2000.

Our portfolios in 2000 — well, I shouldn’t say that. The market in 2000 went down 40, 50, almost 60 percent in some cases, depending on how you built your portfolio. Our all-equity portfolio went down 9 percent from 2000 to 2003, so when the upturn hit in 2004, it went through the roof because it was up like 43 percent. We were going from 9 percent down to 43 percent up, where it took the S&P six years to recover from those three years. Think about that. It was a six-year recovery before 100,000 in the S&P equaled 100,000 in the S&P. It was a long time frame.

The same thing happened after 2008. In 2008 it took six years for the S&P; that’s why they call it the lost decade — 2010, the lost decade. So anyway, the answer to your question is I don’t know. I have to go by the algorithms and rebalancing and what have you, but I would say in the U.S. market, we use six funds, because I don’t have profitability built into here, but I would say we’re around 16, 17 percent probably in small value in exactly the small value fund, but then the other funds pick up small value. But I don’t know the exact percentage. I read the algorithms; I don’t write them.

Guy E. Baker, MSFS, CFP is a 48-year MDRT member with 41 Top of the Table qualifications. He served as MDRT President 2010.