Demystifying Investment Information


Preconference presented by the
BRASS Education Committee
Friday, June 24, 1994, Miami, Florida

Making Order Out of Chaos in the Stock Market

Thor Bruce, Ph.D.

Transcribed by Katherine Shelfer
Carol Womack, Chief Editor

Thank you, Carol. Good morning, ladies and gentlemen. You have no idea what an extreme pleasure it is to do an investments lecture before a group of librarians, because librarians already know all about the complex issues involved in investments. Or, if they don't, in just a few moments and a couple of keystrokes they will have it for you. So, with this wonderful audience, I'm going to try to teach the entire investments class in less than one hour!

We will start with a colorful slide taken of the cover of the book by M. R. Schroeder, Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise (NY: W. H. Freeman, 1991). It shows a [rainbow-like] swirl overlaid by a random track and represents the title of this talk--that is, making order out of chaos in the stock market. Chaos is a random black line that zig zags unpredictably across the background of the swirl. The swirl itself represents the concept of order. Take any particular color line in that swirl. You can predict pretty accurately the shape of the next line in the swirl. That's what order is.

Order is the ability to predict things based on things you already know. Not only can you predict the shape of a line in the swirl, but you can probably also predict the color of it by simply presuming that the color of the line next to it in the swirl will be exactly the same as the color of the one we just saw. As you go around from place to place, your ability to predict the color may be the only part you're not going to do very well, but the shape is going to be absolutely certain.

Imagine a pendulum. It has an orderly, periodical swing. It's very predictable. You can observe it for awhile, and after a moment you can shut your eyes and pretty much say exactly where the pendulum is going to be. That's another example of order. But, if the pendulum were to wander without gravity in a really willy-nilly form, then it would be chaos.

The same thing is true with the sun rising every morning. We've learned to adjust about a minute and a half every day for sunrise/sunset time. We can predict that very accurately. However, if we have a light meter and we're trying to measure how much light is coming from the sun, there might be an intervening event such as clouds which would confuse that fact. So there's your chaos. The clouds are probably one of the best examples of chaos.

Now, in the stock market, how do we exemplify chaos? Well, I think we have several very good examples of chaos in the stock market. One of them is called the Random Walk Theory which says that stock prices follow a random walk, that stock prices cannot be predicted from one transaction to the next. That if you were simply to record pluses and minuses [consecutively as transactions take place], you'd find that you can't predict the next movement from the previous, and that's the random walk aspect. A whole book was written on that by Burton Gordon Malkiel. It was called A Random Walk Down Wall Street (NY: Norton, 1990). That, of course, is one of the key books in a good business library.

To give an idea of whether the stock market is order or chaos, let's look at the termite analogy. In Africa, there are these incredible termite mounds that stand six to eight feet tall. They are of particular interest to scientists because they are one of the world's largest producers of methane gas. There's a lot of activity in these termite mounds. A scientist observing it put a blade of grass on top of this thing, and the termites very vigorously would pull it off the top of the termite mound. The researchers filmed this and tried to observe how the termites knew from their genetic code to get that blade of grass off the top of that termite mound.

It turned out that the termites were programmed very simply. That is, if you see a blade of grass, grab it with your teeth and back up as hard as you can. Other termites, noticing the turmoil, will join force. There will be termites on all sides of the grass, and they will be pulling against each other, and the blade of grass will follow a path very similar to what you see in stock. It will go this way and that until it finally ends up going in the direction where there are the most termites and ends up off the nest. There's no blade of grass now violating the sanctity of the termite nest, so they go about their business.

The stock market is the same way. We have all these bleary-eyed people staring at red, blue, green, orange and white terminals looking for blades of grass. When they see them, they start pulling on them. When they find a blade of grass that's overpriced, they shortcut the stock and find another blade of grass that is under-priced and they buy that. Then they look for relationships so that they can sell one and buy the other.

One of the relationships that my students always find most curious is the very strong relationship between beans and gas. That is something we all know about. You can make money on this correlation, because it just so happens that one of the most expensive components in raising soy beans is fertilizer, and one of the most predictable elements of fertilizer prices is energy, and energy is measured in natural gas contracts, so there is your correlation between beans and gas. If they get out of line with each other, you want to buy one and sell the other.

Now, one of the things I'd like to discuss is why the stock market go down when the economy improves. What happens is that the economy starts to heat up, unemployment goes away, manufacturing output goes up, the GNP increases, and the Federal Reserve starts to think, "Uh oh, we're going to have inflation." So the Federal Reserve thinks it should put on the brakes now and do something about this while it's not too painful, rather than waiting until it gets out of hand and we have a hard time stopping it from turning into galloping inflation. So the Federal Reserve increases interest rates just a little bit.

Well, that's fine, that'll slow down the economy a little bit, but the economy is still better than the ones before; even when it slowed down the stock market should go up, but the stock market goes down. We need to see why this is. We have a model called the Dividend Discount Model.

Let me back up a little bit. In the definition of chaos, I've taken that slide with the colorful swirl and used another version of it which is also from Schroeder's Fractals, Chaos and Power Laws, a very complex book.

So what we have here are images of two squiggly lines, one at the top, and one at the bottom of the slide. In the top one, the random walk pattern appears to be a squiggly line moving northeast. This is an example of Brownian Motion. What is Brownian Motion?

A biologist or botanist by the name of Robert Brown looked in a microscope in 1827 and saw a particle in a liquid suspension rooting around like it was alive. This caused a really major stir. What it did was to offend a lot of religious feelings at that time because people actually believed that this was a living molecule. So Brown boiled the water. He froze the water. He put chemicals in the water to kill anything that was in there. But, no matter what he did to the water, there was still this motion. They call it the Brown Movement, and it has a truly random effect.

If you've ever seen them, every once in a while when you look in a microscope, if you look for about five minutes, all of a sudden one of those tiny little particles will get up and take off and run out of the field of the microscope as if it knows what it's doing. It seems like it's alive and it has motor to it. In this upper graph here we have a segment marked AD. Purely by chance, all of a sudden, something took off and moved in a northeast direction like it knew it was doing.

If we were to increase our sample and sample 100 times more often, we would find that line segment AD in the lower chart looks like the bottom segment. It also is chaotic and moves all over the place, but it appears to move horizontally from left to right rather than northeast, and it moves in a much more random, jerky fashion, with wide swings from its lows to its highs. It's just that we sampled infrequently enough that when it was observed it appeared as a straight line. Well that Brown Movement is part of the stock market and we now call that Brownian Motion.

Now we go to slide number two. In slide number two, we have the dividend discount model which is very simple:

P 0 = D 1 / (K - G)

It simply says that the price of the stock times zero right now is equal to its dividend next year. It's anticipated dividend divided D 1 is divided by K - G where K is the discount rate appropriate for that risk class and G is the book rate, and both of those are a decimal fraction.

So let's just say that K is .12 and G is 5 percent--that's .05-- so you'd have a .07 denominator. Now with this dividend discount model you can explain very simply why the stock market goes down whenever the Federal Reserve raises interest rates. When they raise interest rates from 3 1/4 to 3 1/2, which is hardly any move at all, the stock market takes a major dive. What happens here is that when the economy does better D 1 goes up, and then the Federal Reserve comes along and raises K.

Now it so happens that raising K has more mathematical impact on this equation then raising D 1 does. Let's just say for example the K goes from 12 percent to 13 percent. That means that the denominator K - G will go from .07 to .08. That's a change in one out of seven. That's a very big change compared to what D 1 is going to change. And so we have K going up, and G going up, and that helps explain some of the peculiarity in the stock market.

The stock market does not like higher interest rates. The higher interest rates are caused by inflation, so we have people searching all over the world for things that will cause inflation. For example we have people who watch the oil cartel--how well the cartel holds together, how much meat we serve, because all prices affect inflation. We have people studying productivity in Japan, Germany and the United States--whether we can increase our output for man hour which will help cut inflation. We have people studying this very carefully because inflation is the most important thing to try and forecast in order to forecast any increasing interest rates, which has a definite impact on stock prices. So this dividend discount model is an excellent example of how to go about understanding the impact of interest rates on the stock market.

Now, what kind of information are you going to need to supply as a librarian? People are going to want to have data of future earnings and dividends. They're going to want to have information about future inflation rates and things that are related to inflation. They're going to want to know about future interest rates which are tied into inflation rates, and not only future rates but also long term interest rates and short term interest rates. So you're going to need to have some kind of database on what interest rates have been and what forecasts for interest rates going to be in the future. All of these things are part of the information data.

The markets themselves are thought to be efficient. We define efficiency as a state in which the price of the asset reflects all the information that is available; that is, all the public information is that is available. To give you an idea of how efficient markets are let's look at commodities. There are actually commodity experts who have people out in the fields in the morning with radios looking to see if the crop has survived the night when the temperature drops to freezing. If the crop freezes in enough locations around the world, the price of soy beans or any other corn or other commodity are going to be affected. This is the degree of effort to which the experts in the major markets are going around seeking information. Under the circumstance, we can say that the markets are efficient; that is, they fully reflect all the information that is publicly available, including information that is privately derived by placing agents in the fields, and by having analysts doing mathematical models to try and figure out things before the public figures them out and so forth. But not all markets are efficient. And this is the situation that librarians have to work with. If the markets are efficient then the price should always reflect all of the information that is available, but there are markets that aren't efficient.

There are, for examples, open counter markets that don't trade for days on end. There, the stock price--the last most recently quoted stock price--clearly does not reflect the newest changes in interest rates or the newest change in work environment. These markets are not efficient. And there are other markets outside financial markets, like automobiles. Used cars--believe it or not--used cars markets are extremely efficient! They are networked. There are prices--blue book, black book, red book, orange book, and so forth, on used cars. But there are other items that aren't so efficiently priced. So some markets are very efficient and some markets aren't.

Slide number three [not included--a chart which shows the relative stock market performance up to and following October 19, 1987]. One always wonders how the stock market can be so efficient when, on October 19, 1987, in one day, the market fell by 25 percent. How can the market have a value on one day and the next day it's worth 25 percent less if all the information that's publicly available is priced on the market?

Well, it's very simple. There was a sudden change in the mix of information that was available. The stock market was in a free fall. Nobody wanted to buy stock. It would be like stepping in front of a speeding locomotive. And, while it was in a freefall, it would continue to drop in price until people started saying "Enough's enough!" and corporations started buying back their own stock, actually one of the best investments they could make. Individuals started saying, "well, it's starting to slow down so I'll start to buy."

Now, there is a new piece of data in the equation. Mainly, it is the dramatic new bit of information that the stock market had fallen, and that the stock market degree of risk is something that had increased dramatically and, given this new risk, now the stock market is for days at about 25 percent of what it was before. Now what had happened in the stock market is that we had had a period of time over a year where the stock market had grown from 1800 to 2700. It was cruising for a bruising, as they say. But then in one day a whole year's worth of profits was erased, perhaps that was excessive profits. But it then took another two years to earn back what was lost in that one day.

We had customers in Miami, and I'm aware of this elsewhere, coming in with a gun and shooting up the brokerage office. We had people jumping out of windows. We had people on highly leveraged portfolios, and operating with margin accounts, who not only lost all their money, but in some cases owed their broker four million dollars (which comes out of anything else they had). It was a true nightmare, a very, very serious problem. And we talk about order and efficient markets and yes, they are efficient! New information now existed.

And we had then, of course, computer trading. As soon as the computers sensed certain things, all the stops were pulled out and all these sell orders came out and that started the market down, which signaled even more computer trading which triggered the market into going down even more.

The next element that I'd like to talk about is when a company is put in play suddenly. Its price jumps by 25 percent. How can the market be orderly if the stock that is in play, that is, taken over, is suddenly worth 25 percent more? There's a very simple explanation for that. That is, it is now a company where you have controlling interest, where you can install yourself as a director, and bring in other corporate officers. It's worth more to you than a minority interest in the company where you don't have any pull, you don't have majority pull.

Typically speaking, securities that are minority interest sell for up to 30 percent less than securities where you have control of the company. In a takeover situation, you are now pricing that stock on the basis of majority control, not minority interest at a 30 percent discount, so that up to 30 percent discount goes away. The stock is worth more when acquiring the company. It's worth more because of the control.

It's also worth more for other reasons which could lead to another reason stock prices double. Sometimes you get efficiencies when you merge companies and you can eliminate some duplicate effort. Maybe one company can serve as a supplier to the other company, or maybe the other company has obtained discounts which you can just automatically apply to yourself. And so the synergisms of the merger also cause the stock to be worth a lot more.

Now I'd like to look at slide number four, which tracks the combined level of risk and return based on the number of stocks in a portfolio. My students took portfolios which consisted of Dow Jones industrials stocks, and tracked their performance during the chaotic period of the Gulf War. On the vertical axis is the standard deviation of the risk of the portfolios. On the horizontal axis are the number of stocks in each portfolio. As you add more and more stocks to the portfolio, the risk of the portfolio declines. And, finally, it declines at 30 stocks which is pretty much fully diversified to a level that is quite a bit below the risk of the one-stock portfolio. Remember, we're talking about giant Dow Jones industrials companies who are themselves fairly well diversified; they're international conglomerates in many instances. We'll take them, and buying their stock reduces the risk.

Now there are some things that we can observe. First of all diversification pays; it works. The second thing is, it doesn't work completely. As you can see, we've gotten rid of less than half the risk rate. There is a very persistent serious amount of risk remaining. The only way you can get rid of that risk is to buy treasury bills. You see you can get rid of this risk further by buying more and more treasury bills, which of course would be ultimately 100 percent treasury bills, which would have no risk. So diversification pays. One of the things that happens with diversification, though, is you can actually get something for nothing. What you do is to take two stocks that have a certain amount of return which is caused by their riskiness, and put them together in a portfolio. That gets rid of some of their riskiness, but now you have the same amount of return without as much risk. Or, you go get two stocks that are a little more risky and have a little more return and you combine them together to provide the same amount of existing risk of the previous two stocks held separately. So, you have a portfolio that has the same risk as what you had before, but one portfolio has more return. So, diversification is probably one of the most teachable important things in building a portfolio.

Now, when we had all those portfolios that the students simulated for their experiment, we had quite a few of them. We don't have them all on slide five, because if we include them all, the little black squares which represent level of risk would be so numerous that we'd end up with a black spot. So, you sample a fair number of them and you put plot them.

Each one of those little black squares on the plot represents a combination of risk and return. The horizontal axis is risk. The more risk you have, presumably the more return you need to make it worth your while. On the vertical axis we have return. So we throw off all these points and you have a cloud of risk return points. Then we draw a boundary around the northwest area of all these points, and we call that the efficient frontier, and that's the black line which borders the risk return points to the left.

Let's define efficiency in terms of risk and return. All of us are rational. We would rather have more return than less return. So, for a given level of risk, we want to be as far north, or high up, as we can on this graph. As rational people we would like as much return (to be as high) as we can. But we would also like to be as conservative as possible. As rational people, we would like to limit our risk (be as far to the left as possible), because that would mean exchanging a higher level of return for a lower level of risk. Now, ideally, we like to be northwest. That is, we go north and west at the same time, and we get more return and less risk. And so the boundary line which reflects all of the portfolios that are the farthest to the north (greatest return) and to the west (least risk), or really to the northwest, is known as the efficient frontier. And we don't want to buy any portfolio that isn't on the efficient frontier.

All the rest of those are non-efficient. We won't buy those. They exist, they were there, they were available, but we don't want them. And so in the concept of diversification we have this idea of efficient frontier. We want to only buy those portfolios that lie on the efficient frontier. There won't be any to the left of the efficient frontier because if there were, we would redefine our efficient frontier.

Now I'd like to introduce the lending line. On the far left side of that line we have the risk- free rate. These are daily return of treasury bills--something in the neighborhood of .0022 is the daily treasury bill rate during that period of time. That's an available investment, but it's not on our efficient frontier. So, we need to incorporate treasury bills into our efficient frontier, and the way we do that is by drawing that lending line in such a way that the left side, the risk-free rate, connects to the highest possible point (the rate of return) on the efficient frontier line. And now we've created a new efficient frontier, which is the efficient frontier in the presence of lending to the United States government in the form of buying treasury bills. We call that the lending line. And you can have any combination of risk and return that you want anywhere on that line.

For example you can buy all treasury bills with no risk at a lower return. You can buy the portfolio, whatever that portfolio happens to be--Philip Morris, Kodak, Exxon, and IBM, or whatever it happens to be--where the new efficient frontier line is drawn, or you can have anything you want on the line, let's say half way down the line of blending half treasury bills and half the portfolio so now we have a new efficient frontier. That gives us the combination of risk and return that's available to us as an investor. Now you can see that diversification has really done its work. You take all of these points that are on the newly created efficient frontier and you scientifically select the portfolio that creates scientific diversification, and under that set of circumstances you get a very higher, much higher performance portfolio.

Now I'd like to jump ahead here with the last slide. One of the things that we did with this set of data is we took one of the portfolios that was selected by a student, a four stock portfolio and it happened to be a portfolio that had what's known as a high beta, that is, the definition of beta is the relative volatility of a stock. If a beta of 1.0 is provided to you, that says this stock moves up and down the same way as the stock index does. It has average volatility. It goes up and down up and down with the S&P 500, or it goes up and down with the Dow Jones. If you have a beta of 1.5, then that stock will go up 50 percent more when the stock market goes up or it will go down 50 percent more when the stock market goes down.

So, since we kind of like the ability to observe real data using one of our Dow Jones portfolios, we took a high beta portfolio and we plotted the daily returns the daily returns on the market, and we plotted the daily returns on the portfolio, and--lo and behold!--when the stock market fell a little bit, our portfolio fell more. And when the stock market went up a little bit, our portfolio went up more. We have an illustration here of what diversification is. And that is very good idea of how beta actually works on a daily basis. If you buy a higher beta portfolio, you'll have more volatility it will go up more when the market goes up. If you know the market is going to up, then you want to hold a high beta portfolio. If you know the market is going to go down, you want sell, and get out of the high beta portfolio.

Now I'd like to talk a little bit about the arbitrage pricing law. What we've been talking here is a very simple world where all you would have is risk and return and that's been pretty much the way that academics have been looking at the stock market. They've been looking only in terms of risk and return. But there are other things that we might want to look at. So now we have a new theory called the arbitrage pricing law and it says that the stock market takes into consideration more factors then just risk and return. It's like having a drawer and you open up this drawer and inside this drawer is a magic combination of variables, hundreds and hundreds of variables, some of which play on this stock and some of which play on that stock. We don't know what they are, we can't even define what they are.

We don't need to know what they are, we have a theory. The theory says that there are a myriad of things that influence the stock market and make stock prices the way they are other than just risk and return, now let me give you an illustration.

The University of Miami has a multi-million dollar electric bill every year. And of course, OPEC changes oil prices depending on demand. When oil prices went up in 1973, the University of Miami had a very difficult time balancing its budget. We had not anticipated this, we had not set aside a reserve. We were turning off the air conditioning and that made the students perspire. We turned off the air conditioning in the library and that made the books decay. It was a very serious problem. Ultimately, we decided that we simply had to pay the price to have an air conditioner. But we took for granted a very big energy risk.

Now it so happens that certain stocks do very, very well in such situation; for example, Exxon does very, very well when energy prices go up. So, if the University of Miami wanted to hedge energy prices (that is one way to make money somewhere else when we have a budget shortfall), what we should do is put a whole lot of Exxon stock into our endowment. Exxon's a good investment. If it sits there as a good investment, and then rises when OPEC raises prices, our Exxon stock will make enough money that we can at least pay the budget deficit the first year, and we can make arrangements to take care of it the next year. Well that would mean that Exxon stock ought to be worth more than their stock because it has a special use as a hedge against increases in the cost of energy and it is in fact worth while. And the arbitrage pricing model explains that.

There are many things that might effect the stock prices. One might be the timing of cash flows for the stockholder's need for money. If a stock fits somebody's need for money, it will be worth more, so that's a timing issue. Other stocks may be worth more to somebody who thinks they're going to come into play. Well, where in the risk of return model do you have any discussion about stocks that are potentially coming into play? So analysts chase around looking for stocks that might potentially come into play. Now any of these other things can cause a stock to sell for higher amounts and now its explained by arbitrage pricing so we don't know what all those factors are. They change daily, they're different for different stocks but we now have a theory to explain it.

In the line, however, of efficient capital markets, there are some situations where the stock market appears to provide excess return without a blistering amount of risk. One of the most interesting examples is the Value Line market index. Now this index is probably one of the most popular reference tools in the business section of the library. It's very widely followed. It has a huge budget and it has excellent data. It has a track record which is absolutely unexplainable by academic theory.

Mainly, the stocks that are ranked "1" for timeliness have out-performed the market for many, many years. In almost every year, the stocks that are ranked "2." outperform the stocks in rank "3" and "4", and the lowest, "5" performs the worst. And over a 20 year period of time you may get a l0,000 percent return through one stock. Now one would think that if Value Line could produce a model, purely a technical model, purely based on historic data, no judgment at all, that is no future forecast, they get those models that out performs the stock market consistently then the stock market must not be efficient. And so we study this very carefully because we're interested in knowing if the stock market is not efficient. If Value Line can pick it before it happens then the market is not efficient.

There is a second aspect to the Value Line. That is, whenever there is a rank change, for example, when the stock is elevated from rank "2" to rank "1," you get an abnormal return on the stock market of a one to one and a half percent. And when you get a truly abnormal return rank change as from a "3" to a "1," you can get upwards to a three percent excess amount of return on that stock over the next two days and that's enough to pay the brokerage commissions. So attempts are being made to try and explain this Value Line denominator.

The fact is, there's another anomaly called the surprise earnings forecast anomaly which is when earnings are announced by corporations which are higher then what the analysts expected out of this crop. Whenever there's a surprise, the stock market takes a real jump. Theoretically, if the market is truly efficient, it should have had all of that information already priced into the stock, so we've got these two anomalies--the Value Line anomaly and the surprise earnings anomaly. And now we've found that they are one and the same. That is, if you try and explain the Value Line anomaly, you'll find that it is caused by the earnings surprise anomaly. Therefore, the Value Line anomaly is set aside.

My recent work at the University of Miami has shown, however, that there still are two separate anomalies. The surprise earnings anomaly happens about a week to 10 days prior to the Value Line anomaly. And then the Value Line anomaly comes along later. It is an additional anomaly on top of the earnings surprise anomaly. If you do your model correctly, and build up a daily analysis of this, you can find that both anomalies exist. Not only do they exist, but this "3" to "1" anomaly, even after controlling for [unclear tape] brings a three percent excess return, an opportunity to make money in the stock market. So what should you do? You should look around for all of those stocks that are going to change to rank "1" and discover that the weekend before Value Line publishes it.

[Possible transcription gap due to need to change side of tape.]

You can, in fact, anticipate "3" to "1" changing. What you do is look at all the stocks that are ranked "3" and you find all the ones that have extraordinary earnings changes, because that's really what causes a "3" to "1" change, and you can find it out in advance. There are hedge funds, believe it or not! There is a professor at the University of Texas who advises a hedge fund where they try to get the upper half of the hundred stocks that are "1" and they buy those, and they get the lower half of the stocks that are "5." They're writing their own model, and they "short" those, and they've been making a lot of money on this. That is, until one year when something unusual happened and the group five stocks took off and went crazy and they practically got wiped out. So you can in fact participate in that anomaly and you can make money, and we think that people are, in fact, doing that.

Statistical analysis that we see shows that this surprise effect hits immediately when the earning are announced, and then during several days prior to Value Line coming out with their report. Others have computed and figured out what Value Line is going to do, either an illegal leak at the printer has let this out, or somebody's analyzing it and figured it out that there's statistical evidence that's starting to come out. Then, when it actually hits the street, Value Line isn't real popular.

Now there are other anomalies that make the efficient capital market seem a little strange. For example, the month of the year effect. January is the best performing month. Now there's a perfectly good reason for this; that is, there's a lot of sell off at the end of the year in December for people who are doing tax returns, and these stocks end up slightly depressed at the end of the year and they pop back in January. Not only do they pop back in January but the first week of January. So really it's not a January effect at all but the first week of the year effect. That's a proven anomaly and it exists probably eight out of 10 years. And just about the time that you're going to buy a stock on December 31 you're going to have a serious problem.

We have the Friday the 13th effect. Three of the finance professors at the University of Miami published an article on Friday the 13th. It turns out that that on Friday the 13th, the stock market goes down. This happened for every Friday the 13th for numerous Friday the 13ths. So one of them decided they would release this to the local radio stations. It was announced on the morning of Friday the 13th that three professors at the University of Miami, and they named them, said that the stock market is going to crash today, and it went up. So Friday the 13th may exist and it may not. So there's another anomaly.

These same professors looking around for traditional anomalies, and also tongue-in-cheek, did airline crashes, this is a really sick one, because it turns out that whenever an airline has an airplane crash the stock price goes up. Now that's scary because we already had the Tylenol scare, the rumor that Tylenol that was poisoned. And that's how they found out--Johnson & Johnson, is that who it was? They found the person that poisoned it. Airline crashes, however, a basic [unclear tape] study, have an economic reason for that. It turns out that the airplane is worth more for insurance purposes than it is truly worth. And the wealth of the airline actually goes up when the plane crashes. Now they have all this bad press and all the liability problems but that's covered by insurance, so airline crashes is another anomaly, a really strange one. We have a day of the week anomaly.

Now some people try to explain the day of the week, but different days of the week have different performances. These anomalies, some people say the exceptions make the rule, because there's only a few exceptions but they bother academics because they shouldn't exist. You shouldn't be able to make more money on a Thursday but you can. Of course, we were saying before, the very Thursday that you decide to put all of your eggs in one basket and go for it, you get wiped out!

The stock market is a dangerous place. There have been years where you can lose 15 percent. Sometimes you can have a year when you can lose 15 percent in all or another year when you lose 25 percent. But you can have years when you make 40 percent. The stock market has, on an average, about a 12 percent return. But with a standard deviation of 20 percent, which means that, and that's really remarkable, the standard deviation is higher than the average. That means that two thirds of the time the market is going to deviate plus or minus 20 percent. Plus 20 percent is fine, that would be a 32 percent return but minus 20 percent is an eight percent loss. And so two thirds of the time the market is going to go between eight percent and 32 percent. That's a wide swing. You have to be prepared to stay the course. You probably should have 60 percent of your portfolio in stock but you better not look at it on a daily basis if it'll bother you. My investment professor had a lot of truisms, and one of the ones that he had which I especially enjoy is "you sell off the stock if you can't sleep, never let stock keep you awake at night." If the stock keeps you awake at night, you get rid of it the very next day. The sleep is much more important than worrying about your portfolio. And if you can't tolerate a 15 percent loss followed by another 25 percent loss over two years you're going to miss out on a very good return but you shouldn't be in the stock market.

Now some people think the stock market is the most risky place in the world but let me assure you that the bond market is much more risky than the stock market. We have a wonderful new vehicle called the zero coupon bond. A 30-year zero coupon bond has a volatility index which we call duration of 30. That means if interest rates go up by one percent , or six to seven percent, then that bond will drop by 30 percent. Now United States government bonds, zero coupon strip bond are the safest, default-free instrument available.

They turn out to be the best speculative vehicle for interest rate changes, because you can, generally speaking, get a lot of leverage in buying those bonds. You can borrow up to 90 percent of the money to buy such a bond, and, if you do that, then you multiply that 30 percent volatility by 10, and you now have a 300 percent volatility which means you can triple your money if interest rates go down and you can lose all of your money and owe the bank money if the interest rates go up . So the volatility that you get in treasury bonds, especially from strips, is phenomenal.

Now, a 30-year strip is bad enough, but just recently Disney, in order to generate interest in the financial markets, came out with a 50 year zero coupon bond. Well that has a duration of 50 which the means a one percent change in interest rates cause a 50 percent change in value, truly remarkable.

It's kind of an interesting thing that happens when you're an investments professor, when things are going well you sit in your office and read your Wall Street Journal but when things go bad all kinds of interesting people come to see you. And some of these people that come to see you have had terrible things happen to them like in the crash, they now owe four million dollars.

I now know more than a dozen people that has happened to and that's really sad. They end up talking to themselves and they all swear that if their broker hadn't cut them off they would have made it, but the worst story of all I think was a man who ran an air conditioning contract business. He was doing high rises and he made a lot of money and all the money that he made last year they finally paid him, because there was litigation involved. All the money that he made this year came in and a lot of the money that he was going to get next year was on a cash basis. Some major up front money that came in this year, and he had a million dollar income tax bill to pay. He had all of this money he had invested in these projects and he didn't have the million dollars to pay the income tax bill. So he went searching around.

He called every broker, every advisor, every lawyer that was involved in this, looking for some kind of tax shelter. They checked out cattle breeding. They checked at royalty schemes. It was hard to shelter a million dollars over a two month remaining period of the year, but there was one, and it was a very interesting scheme. Apparently what you do is you buy treasury bond futures and then you short treasury bond futures, equal to the amount you bought and at the end of the year one of them will be up and the other one will be down and you liquidate the one that's down and take a giant loss and then you carryover the one that's up to the next year and you take a giant gain next year and you move all of this money in the next year's taxable year.

Well, it's a great idea except that shortly after he did this the governor came along and said you can't do this, but even before the governor came along and said you can't do this, he had trouble because in order to get enough future contracts to shelter that kind of money in that short a period of time he needed a thousand contracts. So he went into the market and he bought a thousand contracts for June and then he went into the market to buy short a thousand contracts for June and the sharks in New York said there's some fool in Miami who just bought one fourth of the entire availability of those bonds through futures and we're going to fry him and they wouldn't let him have any shorts at a fair price. Those that had kept them.

So he had to go out to the near months; he went to May and July and he shorted four hundred in May and four hundred in July and now he had what is known as a butterfly spread. And the body and butterfly spreads usually work well because if you think of our first slide how we could predict those swirls. May tends to go like June tends to go like July, except during this period of time there was some major announcements that the Federal Reserve did something, the President did something, and the next thing you know they moved adverse to each other. And the first day he got a margin call for a half million dollars. The second day he got a margin call for a million and a half dollars. The third day the broker sold them out. But what happened was that he sued the broker saying that nobody told me that a thousand contracts was a billion dollars worth of market exposure. Nobody told me that each contract was a million dollars. If I had had any idea how terrible this was I wouldn't have done it. And what ended up was he lost the 300,000 dollars he delivered to the broker and the broker ate the rest and he went away forever.

So if you think the stock market is bad, watch out for the bond market.

Thank you very much.

bullet Demystifying Investment Information: A Preconference
BRASS Education Committee
Friday, June 24, 1994, Miami

Disclaimer : This publication has been placed on the web for the convenience of BRASS members. Information and links will not be updated. Posted 12 December 1997.