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Alan Cring Productions in association with the Emergent Light Studio presents

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the Illinois State Collegiate Compendium, Academic Lectures in Business and Economics.

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This is Business Finance, FIL 240 for spring semester 2024.

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Today, the capital asset pricing model and just as a heads up, I will have a surprise quiz for you on Wednesday.

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So please be surprised. Make sure you have that Excel template for bond yields and bond prices.

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That will make that go a lot more efficiently for you if you have it and you know how to use it.

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But on a happier note, for today, let's have a look at the numbers.

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And as you can see, this is a very quiet bear day.

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And it's kind of an unusual one because the least risky of the portfolios, the Dow 30 is down the most, more than a third of a percent.

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The S&P 500 is down about 0.2 percent and the NASDAQ is down barely, just 0.06 percent.

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It's just one of those days, it's kind of hard to explain. This day started out on a drop.

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The opening was down and then it's tried to find its way back except for the Dow.

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The Dow just keeps sliding. But as you can see, with both the NASDAQ and the S&P 500, they came up off the opening low.

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But they've just never gotten a lot of steam in their whistle.

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So it's kind of hard to say what's going to happen, although it looks like there's not a whole lot to move the markets around right now.

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Again, just kind of a wait and see attitude.

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Crude, it's in a trading range from about 81 to something like about 89, 88.

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But it came down off, it broke down below 81 a barrel, but it kind of got excited and pushed it back up.

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But it's on the low side of that new trading range. But of course that means higher gas prices because overall the price of the oil is higher.

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So that's why you're seeing, as I said would happen, the 379 a gallon along that line.

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Now gold, the gold bugs are excited about something.

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It's got a new neckline up there at about $2,200 an ounce.

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And it's trying to get up there, but it doesn't have really a lot of will to punch through it.

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So it's just kind of floating up there at 21.75. Nothing big about that.

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At least it's not. It swooped up earlier in the day, but then it's kind of quieted down from there.

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Now the bond yields. The bond yields popped up and they're still up, but they lost their speed upward and sort of leveled off.

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So we're now at about 3.1 basis points up.

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Which of course that means that if the yields are up, the prices are down, that means that there's a mild sell-off of bonds, just like there's a mild sell-off of equities.

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And the beneficiary seems to be a little bit of gold is being bought. Kind of worrisome there, but other than that not really much to write home about.

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Tokyo, it dropped and then it leveled off and right at the closing, in the last 20 minutes before the closing, it started to fall off a cliff.

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And fortunately the bell rang and that ended the bear attack, but it was down 1.16% for the day.

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London started out plowing downward, but it has recovered somewhat.

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And we're still trading over, London I think is still open. It's in the last hour or so.

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But as you can see it's beginning to lose a little bit of speed here again. Right now it's down just 0.17%, so that's nothing much to write home about.

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But anyway, overall there's a kind of a little bit of a sour mood right now.

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Nothing horrible, but probably I would hope that they're just taking a breather before we have our next upsurge.

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One way or the other though, let me take this off the board here for a while.

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Now there is some math in what I'm about to do, and it's not tragically hard math.

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How did I get a marker that is not a good marker?

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Okay, good. There's definitions and terminology in here, and the math is not robust. It's not calculus or anything like that.

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It's just knowing what is what, what this term means, what this term means, that term means and all that.

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Start off just a little bit with the idea of risk.

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Now some of this I've already said, say it again, just to get a running start here.

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Now risk, remember, is the spread of possible outcomes.

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If there's only one possible outcome, then there's no risk. More than one? Yeah, there's risk.

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How much risk depends upon first how many different outcomes there could be and how widely they are distributed.

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The more wide the distribution of risks, the greater the risk, of outcomes I should say, the greater the risk.

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Now our favorite measure for risk in most subjects is the standard deviation.

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And for the sample, that's just 1 over n minus 1 times the sum from i equals 1 to n of each data point minus the mean squared.

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And then you take the square root of that mass right there to get your standard deviation of the sample.

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If it were a population, you would use an n, but we hardly ever have a population of data, so this is good for us.

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Now one quick side mention. We square it and then take the square root. Well, why would we do that?

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The reason is rather simple. It's because you can have data points below the average and above the average.

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If you just added up those differences, a lot of canceling out would happen.

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So we square it so that everything's a positive. Whether the x sub i is below or above, it will be squared to be a positive.

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That way we are not getting any canceling of low points and high points.

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But anyway, that's kind of our normal one.

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And then of course the coefficient of variation is a big one in a lot of subjects, especially, well, in management accounting

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or management finance, we look at coefficient variation. I'll explain that in a minute.

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But that's a standard deviation over the average.

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You will see this. If you were to take one of my higher level courses, and I think they do this in some management courses too,

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you might have lines of year by year or month by month or quarter by quarter numbers for your variable costs, your fixed costs, your revenue,

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and all those kinds of numbers. And what we like to do is we like to see how those numbers on a given line are varying over time.

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But because they would necessarily be of different sizes, fixed costs might be a lot larger than variable costs or vice versa,

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it's hard to compare their standard deviations because those are taking the actual numbers.

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The coefficient of variation takes out the scale, the size of the numbers, and so we can look at these different lines and compare them.

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Now, our variable costs are highly volatile, but our fixed costs seem to be very calm and quiet or something like that.

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Our revenues are variable quite a bit, but our net profit doesn't seem to move that much.

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So the coefficient of variation does have a lot of good uses to it.

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One that I kind of push off to the side sometimes, but it's actually kind of useful.

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And I'll tell you why it's not a good idea.

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Excuse me.

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The Sharpe ratio.

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The Sharpe, as a matter of fact, you'll see a template, I'll pull that out on Wednesday.

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You'll see a template where you can do all these kinds of calculations, but the Sharpe ratio is one that will...

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Yeah, I think I have that in the template.

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You take the average returns minus the risk-free rate, and then you divide that by the standard deviation.

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The criticism of that one is that it uses the wrong measure of risk, sigma.

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The Sharpe ratio, it should be either beta or it should be the coefficient of variation.

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Because the Sharpe ratio, I don't know, it's just out there.

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It's very popular as a measure of return for mutual funds.

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You'll see, as a matter of fact, it's harder to find the beta than it is for the beta of a mutual fund.

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The Sharpe ratio is reported very clearly.

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I brought it up, so now beta.

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Beta is the finance measure of risk.

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Beta is the non-diversifiable risk...

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The non-diversifiable risk of a security in a well-diversified portfolio.

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In other words, beta doesn't mean anything if you're just going to buy a stock.

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The total risk is the diversifiable risk plus the non-diversifiable risk.

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Make sure you get these down in your notes because I tend on a quiz and an exam to give you a word or term

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and then I give you some definitions and you've got to nail the right one for it, risk.

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In other words, this whole mass is sigma.

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The variation of returns, the total standard deviation of returns is the sum of the diversifiable risk and the non-diversifiable risk.

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Something I need to mention here.

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Another term for the diversifiable risk is the non-systematic risk.

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And the non-diversifiable risk is called the systematic risk.

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Now these look kind of backwards.

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Diversifiable is non-systematic, non-diversifiable is systematic.

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The systematic risk is the risk in the system.

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It can't be removed.

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The non-systematic risk can be diversified away.

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You can make it disappear if you just put more stocks in your portfolio.

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There are standalone risk or there risk, internal risk will just cancel each other out.

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So those two, there you go, right there.

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Okay, now as I said, this is kind of definitions right now.

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Another definition.

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The return to the market portfolio.

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Now when I say the market portfolio, that would be a portfolio of all of the stocks and bonds in the world.

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That would be a market portfolio.

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It's a theoretical portfolio.

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We can't actually buy all of them.

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But this is what it would be if we bought all of them.

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What's the expected return to the market portfolio is?

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We call this the market return over the risk-free rate.

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Let me explain this to you.

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Let's see if I can do this.

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Let's see if it will work.

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I got here a $10 bill.

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Now, you sir, I shall give you $10 and you have your choice of two things you could do.

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You could either walk out the back door here from the first floor and you'd earn a $10.

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Or you could go up to the top floor here, and I'll show you how you can get outside from the top, and jump off from there and earn your $10.

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Now which one would you rather take?

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The first one.

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Yeah, I mean, what is the chance, well, okay, yes, you might land on a fat squirrel and survive the drop from that fifth floor here, but most likely not.

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You see, the walk out the door is risk-free.

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You would want something more than the risk-free rate to do it from someplace where you might actually get hurt.

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You might be a hero, you know, become the next TikTok sensation.

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He jumped off the fifth floor of the State Farm Hall of Business.

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You might, but, you know, the point I'm making here is that the risk-free rate would be like what you'd buy.

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I got $1,000, I'll put them into treasury bills.

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Well, you're not going to lose your money.

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So you're going to earn a little bit, but you're not going to earn a lot.

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This market premium, and I should put the word premium here.

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This is the additional amount that you expect to get above what you would expect to get by taking on the market risk above what you would get by taking on no risk.

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So in other words, if we expect the market return for the coming year to be 16% and right now the risk-free rate is 4%,

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then the market premium over risk-free is 12%.

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This is the additional reward you get for taking a risk.

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You, sir, could have graduated from high school and gone out and gotten a job.

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$12 an hour, okay, in your case, $8 an hour.

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That would have been risk-free.

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No risk.

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You take college, you go here to college, you're taking an expensive risk.

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There's no guarantee you're going to come out with a job because of this degree and all this great learning that you're getting.

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You're taking a risk, so you expect an extra return, maybe $50 an hour.

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So the premium for taking that risk is 50 minus 12 or $38 per hour.

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That's the point of what that market premium over risk-free means.

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If you are unwilling to take risk, you get R sub F, the $12.

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If you are willing to take an actual risk, then what the extra you get is the 50 minus 12 or the $38.

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So that's what that kind of means in a little bit more understandable terms.

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Now we're moving kind of forward here.

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Let me go back here just for a minute.

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When you're building a portfolio, putting more stocks into it, what you are actually trying to do is to narrow,

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get this diversifiable risk down because of that risk pooling effect that I was telling you about on Wednesday.

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Get the peculiar risks of each stock to cancel each other out.

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And the more stocks you put into your portfolio, the more that noise, that diversifiable risk will disappear.

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You're probably never going to get rid of all of it unless you had a world portfolio.

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Then it's all systematic risk, but you try to get as close as you can.

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Now, how many stocks would it take to actually shake out this diversifiable risk?

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Well, it works like this. Let me draw you a picture here.

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And we've done a lot of simulations on this.

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Let's say that on the horizontal axis is the number of stocks, number of stocks, different stocks in your portfolio.

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And on this axis is the total risk of the portfolio.

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And again, we've done a lot of these simulations and looked at real world data.

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At one stock, you've got pure total risk. Pure total risk.

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I mean, one stock's got both its diversifiable and its non-diversifiable.

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As you put in more and more stocks, it slides off.

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You might remember a term from math and algebra, the asymptote.

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Actually, in the first five to ten stocks, you squeeze out a lot of that diversifiable noise.

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And then it slides off more and more slowly.

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The asymptote, not surprisingly, is the non-diversifiable risk, the systematic risk.

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In other words, if you get enough stocks in there, all you'll have is non-diversifiable risk.

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You will have squeezed out all of that diversifiable non-systematic risk.

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How many stocks?

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To get rid of 95% of the diversifiable risk, about 35 stocks.

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That's actually a lot of stocks in a portfolio.

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I mean, you have to buy those. That takes a lot of money.

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And then you'd have to manage it to keep the portfolio in balance.

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If a stock price goes up a lot, then you shave off so that it comes back into line with the others.

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Or if a stock goes down, you put more of it in there to bring its value back up in the portfolio.

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But yeah, about 35.

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That's why we strongly recommend that you invest in mutual funds and ETFs.

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Because they have portfolio managers who have put hundreds of stocks or bonds or both in.

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And then they watch them and manage them every hour of every day.

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So that's, I mean, for example, let me show you real quick here.

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The SPYDR, I've mentioned this one before.

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This is the S&P 500.

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As a matter of fact, the S&P 500 is so well diversified that we use it oftentimes as a proxy for the market.

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It's got, I mean, somewhere between 65% and 70% of the value of the world securities are in the S&P 500.

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So it's hugely diversified.

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It's managed professionally by the top cats in the industry.

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And so it's, you know, I have a fun portfolio too.

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I just buy and sell a few, take a lot of risk in them and all that.

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But the responsible money is in these professionally managed ETFs that you can get.

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You just buy a share, you buy a share of this, you've got a perfectly well diversified portfolio.

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That's all there is to it.

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Now, however, let me take you, there's one more term that is useful for you now.

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Well, what the hell is beta?

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Beta is the measure of the non-diversifiable risk in a stock.

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So you can't really just look at beta and say, well, if I buy this stock, I'll have this much risk.

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Because this means something only in a well diversified portfolio.

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If you buy a stock on its own, you don't have the beta risk.

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You have sigma risk. You have the whole thing.

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Like my favorite example, I believe it's Microsoft.

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If you buy a share of Microsoft, 60% of its risk is diversifiable.

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Only 40%, the part that you will expect a return for, is non-diversifiable.

206
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Now, the actual formula for the beta is the, well, let me find some other place to put this.

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Beta of a security, a stock, whatever, it's the correlation coefficient of that stock with the market portfolio

208
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that times the standard deviation of the stock over the standard deviation of the market.

209
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That's the actual formula for beta.

210
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That's about all I just took.

211
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But now, so actually, when we calculate a beta for that standard deviation of the market portfolio,

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oftentimes we'll take the standard deviation of the S&P 500 or the standard deviation of that ETF.

213
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That is the standard deviation of the market.

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Notice a couple of things.

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If I talked about the beta of the market portfolio,

216
00:26:40,000 --> 00:26:50,000
the correlation coefficient of the market with itself times the standard deviation of the market

217
00:26:50,000 --> 00:26:55,000
over the standard deviation of the market.

218
00:26:55,000 --> 00:27:05,000
In other words, that would be the correlation of the market with itself is 1.00

219
00:27:05,000 --> 00:27:12,000
times the standard deviation of the market over the standard deviation of the market.

220
00:27:12,000 --> 00:27:17,000
So the beta of the market portfolio is 1.00.

221
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It's like our anchor.

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That's why I kept saying, okay, well, the beta is above 1 or below 1.

223
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That's why if it's above 1, then it's above the market risk.

224
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If it's below 1, then it's below the market risk.

225
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One more.

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The beta of the risk-free rate, like a treasury, something like that,

227
00:27:48,000 --> 00:27:55,000
the correlation of the risk-free with the market

228
00:27:55,000 --> 00:28:08,000
times the standard deviation of the risk-free over the standard deviation of the market.

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Well, that one's kind of easy.

230
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The correlation of the risk-free rate with anything is 0.

231
00:28:22,000 --> 00:28:27,000
The standard deviation of the risk-free rate is 0

232
00:28:27,000 --> 00:28:29,000
over the standard deviation of the market.

233
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So the standard deviation, the beta of the risk-free rate is 0.

234
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Okay, so we're there.

235
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Now let me step back from this.

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I started throwing this kind of algebra and arithmetic,

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and it can kind of put you off.

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But let me take you on something of a little bit more practical setting.

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Suppose that you want to build a portfolio.

240
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I'm going to start.

241
00:29:12,000 --> 00:29:17,000
I know it's 35 stocks, but I've got to start somewhere.

242
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So you put in your first stock.

243
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The next thing that you would want to do,

244
00:29:22,000 --> 00:29:27,000
don't just pick a stock that you like or that someone recommended.

245
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The first thing you want to do is keep that next stock

246
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from being too highly correlated with what you already have,

247
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because the only way that you shake out diversifiable risk

248
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is if the odd whips and saws are canceling each other out.

249
00:29:49,000 --> 00:29:54,000
That means that you want to find stocks that have low correlations

250
00:29:54,000 --> 00:29:56,000
against each other.

251
00:29:56,000 --> 00:29:59,000
You don't want stocks that are highly correlated

252
00:29:59,000 --> 00:30:01,000
because they'll fuel each other.

253
00:30:01,000 --> 00:30:03,000
They'll go in the same direction.

254
00:30:03,000 --> 00:30:07,000
You want stocks that don't run very well together.

255
00:30:07,000 --> 00:30:08,000
They don't play well.

256
00:30:08,000 --> 00:30:15,000
That way they're going to cancel out more of each other's diversifiable risk

257
00:30:15,000 --> 00:30:19,000
and retain the underlying risk of the market.

258
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Let me show you.

259
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There's a site that I...

260
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This has been around for a long, long time.

261
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It's called Portfolio Visualizer,

262
00:30:28,000 --> 00:30:32,000
and I'll put the URL, the address of this,

263
00:30:32,000 --> 00:30:36,000
in your Canvas files folder.

264
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This is a place where you can put in trading symbols of stocks,

265
00:30:42,000 --> 00:30:46,000
and it will show you how correlated they are.

266
00:30:46,000 --> 00:30:50,000
Now, you may remember that the correlation coefficient goes...

267
00:30:50,000 --> 00:30:53,000
If it's 1, then they're perfectly correlated.

268
00:30:53,000 --> 00:30:55,000
Oh, you don't want that.

269
00:30:55,000 --> 00:31:00,000
I'm going to just grab some stocks kind of out of thin air.

270
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Walmart.

271
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Let's take Ford.

272
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Apple.

273
00:31:10,000 --> 00:31:16,000
I don't know. Meta.

274
00:31:16,000 --> 00:31:18,000
I don't know.

275
00:31:18,000 --> 00:31:19,000
What would I want?

276
00:31:19,000 --> 00:31:22,000
Pfizer, let's try.

277
00:31:22,000 --> 00:31:27,000
Rochead Martin.

278
00:31:27,000 --> 00:31:30,000
Anyone got another one?

279
00:31:30,000 --> 00:31:32,000
Trying to...

280
00:31:32,000 --> 00:31:35,000
What? AT&T?

281
00:31:35,000 --> 00:31:37,000
Good.

282
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That's five.

283
00:31:39,000 --> 00:31:43,000
Now, what it's going to do is create a correlation matrix.

284
00:31:43,000 --> 00:31:47,000
Now, remember, correlation matrix is symmetric on the diagonal.

285
00:31:47,000 --> 00:31:51,000
Let's have a look at what these guys do.

286
00:31:51,000 --> 00:31:56,000
Here we go. Correlation matrix.

287
00:31:56,000 --> 00:32:01,000
Now, here's my rule.

288
00:32:01,000 --> 00:32:05,000
Point three, zero, or below.

289
00:32:05,000 --> 00:32:08,000
That's good for a portfolio.

290
00:32:08,000 --> 00:32:12,000
Above point three, and you're getting a little too much feeding.

291
00:32:12,000 --> 00:32:15,000
The stocks are feeding each other too much.

292
00:32:15,000 --> 00:32:18,000
They're correlations. They're too closely moving together

293
00:32:18,000 --> 00:32:23,000
and feeding each other's diversifiable risk.

294
00:32:23,000 --> 00:32:28,000
Now, let's look at Walmart.

295
00:32:28,000 --> 00:32:30,000
Walmart.

296
00:32:30,000 --> 00:32:33,000
Ford and Walmart would be good in a portfolio together.

297
00:32:33,000 --> 00:32:38,000
That correlation coefficient is a low.19.

298
00:32:38,000 --> 00:32:42,000
Apple, it's right on the boundary.

299
00:32:42,000 --> 00:32:44,000
Now, I have no clue.

300
00:32:44,000 --> 00:32:46,000
Sometimes it's obvious why they're highly correlated.

301
00:32:46,000 --> 00:32:48,000
But sometimes you've got to think,

302
00:32:48,000 --> 00:32:50,000
well, why would those be correlated?

303
00:32:50,000 --> 00:32:55,000
But Apple and Walmart are somewhat correlated.

304
00:32:55,000 --> 00:32:57,000
I might...there's plenty of others.

305
00:32:57,000 --> 00:32:59,000
Now, look at Walmart and Meta.

306
00:32:59,000 --> 00:33:02,000
Obviously, they're so different, what they do.

307
00:33:02,000 --> 00:33:07,000
And their correlation is commensurately low,.12.

308
00:33:07,000 --> 00:33:11,000
Walmart and Pfizer are okay together.

309
00:33:11,000 --> 00:33:14,000
Walmart and Lockheed Martin are good together.

310
00:33:14,000 --> 00:33:17,000
And Walmart and AT&T are good together.

311
00:33:17,000 --> 00:33:24,000
Walmart looks from this like what we might call a key stock.

312
00:33:24,000 --> 00:33:28,000
The one that would be in almost any good portfolio

313
00:33:28,000 --> 00:33:31,000
because it doesn't correlate well with others.

314
00:33:31,000 --> 00:33:35,000
In fact, if I build this matrix with a few more stocks,

315
00:33:35,000 --> 00:33:41,000
you'd see that it doesn't like anything very much.

316
00:33:41,000 --> 00:33:44,000
Now, let's look at Ford.

317
00:33:44,000 --> 00:33:47,000
Ford with itself is obviously 1.0.

318
00:33:47,000 --> 00:33:52,000
So Ford with Apple, now that is a little surprising.

319
00:33:52,000 --> 00:33:56,000
You would not want those in the same portfolio.

320
00:33:56,000 --> 00:34:01,000
Just a little too highly correlated for my taste.

321
00:34:01,000 --> 00:34:05,000
It's okay with Meta, and it's okay with Pfizer,

322
00:34:05,000 --> 00:34:10,000
Lockheed Martin, and Ford, and AT&T.

323
00:34:10,000 --> 00:34:13,000
The one that is a little bit disturbing there,

324
00:34:13,000 --> 00:34:17,000
Ford with Apple, and I can't figure that one out at all.

325
00:34:17,000 --> 00:34:19,000
Sometimes it's obvious, like I said,

326
00:34:19,000 --> 00:34:20,000
and sometimes you think,

327
00:34:20,000 --> 00:34:23,000
why would they be so correlated like that?

328
00:34:23,000 --> 00:34:26,000
But Apple, let's get across here.

329
00:34:26,000 --> 00:34:33,000
Apple with Meta, a little bit scary there with.27.

330
00:34:33,000 --> 00:34:35,000
With Pfizer, it's good.

331
00:34:35,000 --> 00:34:38,000
Lockheed Martin, it's okay.

332
00:34:38,000 --> 00:34:43,000
And with AT&T, very good again.

333
00:34:43,000 --> 00:34:48,000
Pfizer, if I go over here, Pfizer with itself,

334
00:34:48,000 --> 00:34:50,000
Pfizer with Lockheed Martin.

335
00:34:50,000 --> 00:34:55,000
Now, why in the heck would those two be correlated?

336
00:34:55,000 --> 00:34:57,000
Unless Pfizer, you know,

337
00:34:57,000 --> 00:35:01,000
Pfizer is a medical supplies, medical stuff kind of company.

338
00:35:01,000 --> 00:35:03,000
Lockheed Martin is a war company.

339
00:35:03,000 --> 00:35:06,000
Maybe something to do with that, I don't know.

340
00:35:06,000 --> 00:35:09,000
But with AT&T, it's okay.

341
00:35:09,000 --> 00:35:11,000
Lockheed Martin.

342
00:35:11,000 --> 00:35:17,000
Now, look over here at AT&T.

343
00:35:17,000 --> 00:35:21,000
It also looks like something of a key stock.

344
00:35:21,000 --> 00:35:23,000
It's nasty with Lockheed Martin,

345
00:35:23,000 --> 00:35:26,000
but with a lot of things, it plays pretty well.

346
00:35:26,000 --> 00:35:28,000
Matter of fact, with Meta,

347
00:35:28,000 --> 00:35:32,000
AT&T and Meta are almost uncorrelated.

348
00:35:32,000 --> 00:35:38,000
So you get a feel for what stocks play well with others,

349
00:35:38,000 --> 00:35:41,000
and what stocks just have everything you put in there

350
00:35:41,000 --> 00:35:45,000
has a.3 or something above or just closely below it.

351
00:35:45,000 --> 00:35:47,000
So this gives you an idea.

352
00:35:47,000 --> 00:35:52,000
This portfolio visualizer, it's something that does not hurt.

353
00:35:52,000 --> 00:35:55,000
If you've got a stock or two,

354
00:35:55,000 --> 00:35:56,000
first of all, look at those two.

355
00:35:56,000 --> 00:35:57,000
Are they highly correlated?

356
00:35:57,000 --> 00:35:59,000
Well, they're not helping you at all

357
00:35:59,000 --> 00:36:02,000
with getting rid of that diversifiable risk.

358
00:36:02,000 --> 00:36:05,000
But when you put in stocks, you just test,

359
00:36:05,000 --> 00:36:07,000
okay, I heard about this stock.

360
00:36:07,000 --> 00:36:09,000
Let me see how it would play with the stocks

361
00:36:09,000 --> 00:36:12,000
that I already have.

362
00:36:12,000 --> 00:36:14,000
You might find that it works very well,

363
00:36:14,000 --> 00:36:17,000
but you also might find that it does not work very well.

364
00:36:17,000 --> 00:36:20,000
Or the stocks that you have in your portfolio,

365
00:36:20,000 --> 00:36:22,000
they don't play well together,

366
00:36:22,000 --> 00:36:26,000
and you might want to consider moving out some of them

367
00:36:26,000 --> 00:36:28,000
and putting other things in

368
00:36:28,000 --> 00:36:33,000
that have less of that correlation with them.

369
00:36:33,000 --> 00:36:38,000
It's something to think about.

370
00:36:38,000 --> 00:36:42,000
Now, let me do something here.

371
00:36:42,000 --> 00:36:51,000
Bring you back a little bit here.

372
00:36:51,000 --> 00:36:58,000
Let me show you something.

373
00:36:58,000 --> 00:37:09,000
Let me show you a graphic.

374
00:37:09,000 --> 00:37:14,000
This graph with actual real-world data,

375
00:37:14,000 --> 00:37:16,000
simulations and all that,

376
00:37:16,000 --> 00:37:19,000
has been done many, many times.

377
00:37:19,000 --> 00:37:23,000
It was sort of like the fuel of PhD dissertations

378
00:37:23,000 --> 00:37:26,000
for a generation or two.

379
00:37:26,000 --> 00:37:29,000
As a matter of fact, Google used to have

380
00:37:29,000 --> 00:37:32,000
one of my graphs of this

381
00:37:32,000 --> 00:37:37,000
when you search for this term that I'm going to show you.

382
00:37:37,000 --> 00:37:49,000
But let me show you this for just a minute.

383
00:37:49,000 --> 00:37:53,000
This is total risk.

384
00:37:53,000 --> 00:37:58,000
And on this axis is the return to a portfolio.

385
00:37:58,000 --> 00:38:02,000
That could be one stock, one bond,

386
00:38:02,000 --> 00:38:05,000
a couple of stocks and bonds, or whatever.

387
00:38:05,000 --> 00:38:09,000
And this has been done many times.

388
00:38:09,000 --> 00:38:14,000
We look at a portfolio and we see its return.

389
00:38:14,000 --> 00:38:21,000
And whenever we do that, whenever we do that,

390
00:38:21,000 --> 00:38:25,000
we'll get data points all over the place.

391
00:38:25,000 --> 00:38:30,000
Just risk, return, plot another portfolio,

392
00:38:30,000 --> 00:38:33,000
risk and its return, plot another,

393
00:38:33,000 --> 00:38:36,000
and all over the place.

394
00:38:36,000 --> 00:38:44,000
We just keep plotting.

395
00:38:44,000 --> 00:38:47,000
And we get some portfolios that have high risk,

396
00:38:47,000 --> 00:38:49,000
very bad return.

397
00:38:49,000 --> 00:39:00,000
Some have low, high risk and high return.

398
00:39:00,000 --> 00:39:04,000
But no matter how many times we plot this,

399
00:39:04,000 --> 00:39:08,000
and believe me, there have been lots and lots of these done,

400
00:39:08,000 --> 00:39:14,000
like I said, there are now even undergrad research papers.

401
00:39:14,000 --> 00:39:17,000
If you're thinking about doing one,

402
00:39:17,000 --> 00:39:19,000
you can't beat this.

403
00:39:19,000 --> 00:39:21,000
You'll find portfolios all over the place

404
00:39:21,000 --> 00:39:26,000
with the risk and the return.

405
00:39:26,000 --> 00:39:31,000
But there's something strange going on.

406
00:39:31,000 --> 00:39:36,000
It's a phenomenon that we see in physics, kinesiology,

407
00:39:36,000 --> 00:39:39,000
astronomy, mechanical engineering,

408
00:39:39,000 --> 00:39:43,000
electrical engineering.

409
00:39:43,000 --> 00:39:50,000
We see that systems have a place

410
00:39:50,000 --> 00:39:55,000
where we no longer see any data points above it

411
00:39:55,000 --> 00:39:58,000
or below it, whatever it is.

412
00:39:58,000 --> 00:40:08,000
In mathematics, we call this an envelope.

413
00:40:08,000 --> 00:40:11,000
You decide that you're going to become a weightlifter.

414
00:40:11,000 --> 00:40:14,000
You're going to become one of those beefcakes

415
00:40:14,000 --> 00:40:16,000
with the balloon muscles.

416
00:40:16,000 --> 00:40:17,000
You start talking.

417
00:40:17,000 --> 00:40:20,000
Did you ever see the original movie Rocky?

418
00:40:20,000 --> 00:40:22,000
But you play that music anyway.

419
00:40:22,000 --> 00:40:25,000
You say, da-da-da-da-da-da-da-da-da.

420
00:40:25,000 --> 00:40:28,000
Okay, and I'm going to get my muscles,

421
00:40:28,000 --> 00:40:30,000
and I'm going to lift more and more weight,

422
00:40:30,000 --> 00:40:32,000
more and more weight.

423
00:40:32,000 --> 00:40:36,000
You will find a place where you cannot lift any more.

424
00:40:36,000 --> 00:40:38,000
You will not be able to.

425
00:40:38,000 --> 00:40:40,000
Now, you'll push the envelope,

426
00:40:40,000 --> 00:40:46,000
but you'll never get to that desired, let's say, 900 pounds.

427
00:40:46,000 --> 00:40:50,000
No matter what you do, you try to lift 900 pounds,

428
00:40:50,000 --> 00:40:53,000
but no matter what you do, you fart.

429
00:40:53,000 --> 00:40:54,000
That's right.

430
00:40:54,000 --> 00:40:56,000
Grrr.

431
00:40:56,000 --> 00:40:57,000
I won't make this sound.

432
00:40:57,000 --> 00:40:58,000
We all know what it is.

433
00:40:58,000 --> 00:41:01,000
God knows anyone who's been near you knows what it is.

434
00:41:01,000 --> 00:41:03,000
Okay, but here's the problem.

435
00:41:03,000 --> 00:41:07,000
You're breaking, you're trying to cross into an area

436
00:41:07,000 --> 00:41:09,000
that can't happen.

437
00:41:09,000 --> 00:41:18,000
The same is true with, for example, with astronomy.

438
00:41:18,000 --> 00:41:22,000
You find stars that are absolutely enormous,

439
00:41:22,000 --> 00:41:25,000
but we've never found a star that is larger

440
00:41:25,000 --> 00:41:29,000
than a specific boundary, the envelope.

441
00:41:29,000 --> 00:41:31,000
You can take your car.

442
00:41:31,000 --> 00:41:34,000
You can tweak the hell out of it,

443
00:41:34,000 --> 00:41:37,000
put in everything you need to get speed,

444
00:41:37,000 --> 00:41:42,000
but there's some speed you cannot cross.

445
00:41:42,000 --> 00:41:43,000
That's the envelope.

446
00:41:43,000 --> 00:41:45,000
You've come to the envelope.

447
00:41:45,000 --> 00:41:49,000
No matter what you tweak, it won't get past that.

448
00:41:49,000 --> 00:41:52,000
Your personal best running speed.

449
00:41:52,000 --> 00:41:55,000
There will be a speed you will never cross it

450
00:41:55,000 --> 00:41:57,000
no matter what you do.

451
00:41:57,000 --> 00:41:58,000
Well, I believe in myself.

452
00:41:58,000 --> 00:42:00,000
Try it.

453
00:42:00,000 --> 00:42:03,000
Just keep going and see if there's a speed,

454
00:42:03,000 --> 00:42:06,000
and you can't reach above that speed.

455
00:42:06,000 --> 00:42:08,000
That's the envelope.

456
00:42:08,000 --> 00:42:09,000
Explosives.

457
00:42:09,000 --> 00:42:14,000
There's only so much thermal energy that we can create

458
00:42:14,000 --> 00:42:17,000
from a specific chemical reaction.

459
00:42:17,000 --> 00:42:18,000
We cannot get more.

460
00:42:18,000 --> 00:42:21,000
No matter how efficient, no matter how pure

461
00:42:21,000 --> 00:42:23,000
the elements, the ingredients are,

462
00:42:23,000 --> 00:42:30,000
we can't get past a specific explosive charge on it.

463
00:42:30,000 --> 00:42:31,000
This is the envelope.

464
00:42:31,000 --> 00:42:33,000
It's all through the universe in us,

465
00:42:33,000 --> 00:42:38,000
in everything that we do, and that's the envelope.

466
00:42:38,000 --> 00:42:40,000
We see it in finance.

467
00:42:40,000 --> 00:42:45,000
No matter how hard you try to diversify your portfolio,

468
00:42:45,000 --> 00:42:51,000
you will never get past a specific risk return point.

469
00:42:51,000 --> 00:42:54,000
Well, I want to take this much risk right here.

470
00:42:54,000 --> 00:42:56,000
Dag on it.

471
00:42:56,000 --> 00:42:58,000
I want to get my, I'll take this risk,

472
00:42:58,000 --> 00:43:01,000
and I'll make my portfolio, I want to be up here.

473
00:43:01,000 --> 00:43:02,000
You'll never make it.

474
00:43:02,000 --> 00:43:03,000
It just can't happen.

475
00:43:03,000 --> 00:43:05,000
We've never, we don't see it.

476
00:43:05,000 --> 00:43:07,000
It's just not there.

477
00:43:07,000 --> 00:43:10,000
You'll never get close to this envelope,

478
00:43:10,000 --> 00:43:14,000
but you won't even actually touch the envelope.

479
00:43:14,000 --> 00:43:17,000
We actually know what this envelope is.

480
00:43:17,000 --> 00:43:21,000
Those points on the envelope, those are world portfolios,

481
00:43:21,000 --> 00:43:25,000
different combinations of all the stocks and bonds in the world

482
00:43:25,000 --> 00:43:28,000
and different weights and all that kind of stuff.

483
00:43:28,000 --> 00:43:30,000
We just can't get there.

484
00:43:30,000 --> 00:43:32,000
We have a name for this.

485
00:43:32,000 --> 00:43:39,000
We call this the frontier of efficient investments.

486
00:43:45,000 --> 00:43:51,000
And like I said, this graph can look all kinds of ways

487
00:43:51,000 --> 00:43:54,000
with the axes switched around and all that,

488
00:43:54,000 --> 00:43:58,000
but watch this.

489
00:43:58,000 --> 00:44:00,000
We know about this place.

490
00:44:00,000 --> 00:44:05,000
Let me try, I'll do a Google, Google.

491
00:44:05,000 --> 00:44:33,000
Google.com images frontier of efficient investments.

492
00:44:33,000 --> 00:44:35,000
There it is.

493
00:44:35,000 --> 00:44:36,000
We know about it.

494
00:44:36,000 --> 00:44:42,000
It's the 800-pound gorilla in the world of investments.

495
00:44:42,000 --> 00:44:46,000
Can't get past that envelope.

496
00:44:46,000 --> 00:44:51,000
Now, let me really quickly here just to explain

497
00:44:51,000 --> 00:44:55,000
what we're actually seeing here.

498
00:44:55,000 --> 00:44:57,000
Portfolios could be all over here.

499
00:44:57,000 --> 00:44:59,000
For example, this is a portfolio.

500
00:44:59,000 --> 00:45:09,000
Let's say this is a portfolio only of Microsoft stock.

501
00:45:09,000 --> 00:45:15,000
So it's got this risk level here, sigma some MFST,

502
00:45:15,000 --> 00:45:24,000
and it's got this return right here, R sub MSFT.

503
00:45:24,000 --> 00:45:34,000
What this is telling us is that for that risk level

504
00:45:34,000 --> 00:45:41,000
or it is for that return, I could actually be clear back

505
00:45:41,000 --> 00:45:48,000
here in terms of risk because this piece that's inside

506
00:45:48,000 --> 00:45:54,000
of the frontier, that is the diversifiable risk

507
00:45:54,000 --> 00:45:57,000
that you could get rid of by adding more stocks

508
00:45:57,000 --> 00:46:01,000
to the portfolio.

509
00:46:01,000 --> 00:46:07,000
The part from here to here is the nonsystematic risk

510
00:46:07,000 --> 00:46:20,000
or the non-diversifiable risk.

511
00:46:20,000 --> 00:46:22,000
The beta risk.

512
00:46:22,000 --> 00:46:24,000
This whole thing is the sigma risk.

513
00:46:24,000 --> 00:46:29,000
This little part in here is the beta risk.

514
00:46:29,000 --> 00:46:33,000
So in other words, whenever you buy a single security,

515
00:46:33,000 --> 00:46:37,000
you are sitting inside of the frontier of efficient investments.

516
00:46:37,000 --> 00:46:41,000
You get that same return from a portfolio

517
00:46:41,000 --> 00:46:45,000
that had less total risk.

518
00:46:45,000 --> 00:46:50,000
In the case of Microsoft, if I remember the numbers right,

519
00:46:50,000 --> 00:46:54,000
that part that is diversifiable, if you have Microsoft stock

520
00:46:54,000 --> 00:47:00,000
and only Microsoft stock, it's 60% of the volatility,

521
00:47:00,000 --> 00:47:03,000
of the total volatility.

522
00:47:03,000 --> 00:47:07,000
Only about 40% of the volatility of Microsoft

523
00:47:07,000 --> 00:47:10,000
is rewardable by the markets.

524
00:47:10,000 --> 00:47:12,000
The markets are going to reward you for taking risks

525
00:47:12,000 --> 00:47:14,000
that you did not need to take.

526
00:47:14,000 --> 00:47:20,000
That's the important point about that.

527
00:47:20,000 --> 00:47:24,000
Now, a word about this envelope.

528
00:47:24,000 --> 00:47:26,000
We can't get to it.

529
00:47:26,000 --> 00:47:27,000
It's just a theoretical thing.

530
00:47:27,000 --> 00:47:29,000
It's like the speed of light.

531
00:47:29,000 --> 00:47:31,000
We can get as close as we want.

532
00:47:31,000 --> 00:47:35,000
We get subatomic particles within a fraction of a percent,

533
00:47:35,000 --> 00:47:37,000
but we can't get to it.

534
00:47:37,000 --> 00:47:42,000
But there are some strategies that will get you really close to it.

535
00:47:42,000 --> 00:47:47,000
In other words, almost no diversifiable risk.

536
00:47:47,000 --> 00:47:51,000
Like for example, here's an ETF right here,

537
00:47:51,000 --> 00:47:54,000
sort of like maybe the S&P 500.

538
00:47:54,000 --> 00:48:00,000
It's highly diversified, but it's not perfectly diversified.

539
00:48:00,000 --> 00:48:06,000
Now, and I teach this in the next level, 242.

540
00:48:06,000 --> 00:48:13,000
If you get a couple, maybe three ETFs or things like the ETNs

541
00:48:13,000 --> 00:48:18,000
that are all very efficient, they're all very close to the boundary,

542
00:48:18,000 --> 00:48:23,000
and you get by all of those, what you have is what we call a super portfolio.

543
00:48:23,000 --> 00:48:31,000
And the super portfolio is even closer than any of the individual components.

544
00:48:31,000 --> 00:48:33,000
You can squeeze that down.

545
00:48:33,000 --> 00:48:40,000
First, you let the highly diversified portfolios squeeze out as much as they can,

546
00:48:40,000 --> 00:48:47,000
and then you let the – put them together,

547
00:48:47,000 --> 00:48:52,000
and you can squeeze out even a little more of the diversifiable risk.

548
00:48:52,000 --> 00:48:54,000
Those are called super portfolios.

549
00:48:54,000 --> 00:48:58,000
It's something that I teach in a little more advanced class.

550
00:48:58,000 --> 00:49:01,000
There are other ways that you can squeeze out.

551
00:49:01,000 --> 00:49:08,000
I mean, I don't know how much you're gaining if you go from 99% pure,

552
00:49:08,000 --> 00:49:13,000
non-diversifiable risk to 99.5%,

553
00:49:13,000 --> 00:49:20,000
but you can get awfully close to that boundary, that envelope.

554
00:49:20,000 --> 00:49:27,000
In other words, closer and closer to having, effectively, the world portfolio.

555
00:49:27,000 --> 00:49:32,000
That's the return to the market portfolio.

556
00:49:32,000 --> 00:49:34,000
There. Okay.

557
00:49:34,000 --> 00:49:38,000
So, excuse me.

558
00:49:38,000 --> 00:49:41,000
Next step.

559
00:49:41,000 --> 00:49:54,000
Let me take you over here.

560
00:49:54,000 --> 00:49:57,000
How do we use the beta?

561
00:49:57,000 --> 00:50:09,000
How are we going to – what's that going to – what is it going to buy us, that beta?

562
00:50:09,000 --> 00:50:12,000
This – well, I just erased that.

563
00:50:12,000 --> 00:50:15,000
I'm going to put it up here.

564
00:50:15,000 --> 00:50:20,000
The market premium over the risk-free rate.

565
00:50:20,000 --> 00:50:25,000
It's the extra reward you get for taking the risk of the market

566
00:50:25,000 --> 00:50:32,000
instead of a risk-free treasury bill, as it were.

567
00:50:32,000 --> 00:50:36,000
Now, here's what beta does.

568
00:50:36,000 --> 00:50:50,000
The beta of a stock times the market premium over risk-free.

569
00:50:50,000 --> 00:51:01,000
This tells you what your stock will get of that premium.

570
00:51:01,000 --> 00:51:13,000
So, in other words, let's say you've got a beta of a stock that's 0.5.

571
00:51:13,000 --> 00:51:38,000
Then, by this little formula right here, your stock would get 6% of that extra.

572
00:51:38,000 --> 00:51:54,000
If you had a stock with a beta of 1.50, then you would get 1.50 times 16% minus 4%.

573
00:51:54,000 --> 00:52:02,000
Or you would get 18% on that premium.

574
00:52:02,000 --> 00:52:08,000
Beta is a magnifier or a demagnifier of the premium.

575
00:52:08,000 --> 00:52:10,000
That's all beta does.

576
00:52:10,000 --> 00:52:13,000
Look at this.

577
00:52:13,000 --> 00:52:22,000
Let's take a beta that had a perfectly zero – that was perfectly zero.

578
00:52:22,000 --> 00:52:32,000
Then what you would get is 0.0 times 16% minus 4%.

579
00:52:32,000 --> 00:52:38,000
You would get no extra.

580
00:52:38,000 --> 00:52:41,000
Because you didn't take any risk, why should you participate?

581
00:52:41,000 --> 00:52:47,000
Why should you get any of the market's extra reward?

582
00:52:47,000 --> 00:53:09,000
But if you had a beta of 1.00, then you would get 1.00 times 16% minus 4%.

583
00:53:09,000 --> 00:53:18,000
You would get the whole market premium over risk-free.

584
00:53:18,000 --> 00:53:20,000
You get the whole thing.

585
00:53:20,000 --> 00:53:28,000
Because you took on the market, perfect market risk.

586
00:53:28,000 --> 00:53:42,000
So let's put it all together here.

587
00:53:42,000 --> 00:53:52,000
This is called the capital asset pricing model.

588
00:53:52,000 --> 00:54:06,000
Cap M.

589
00:54:06,000 --> 00:54:11,000
Sort of the triumph of finance.

590
00:54:11,000 --> 00:54:20,000
Discovered or created, as it were, in the 1960s, I believe, it simply says this.

591
00:54:20,000 --> 00:54:26,000
The expected return to a stock or a portfolio of stocks.

592
00:54:26,000 --> 00:54:29,000
It doesn't matter.

593
00:54:29,000 --> 00:54:41,000
It's going to be, first things first, any investment that is risky would have to give you,

594
00:54:41,000 --> 00:54:45,000
at a minimum, the risk-free rate.

595
00:54:45,000 --> 00:54:49,000
At a minimum, it would have to give you the risk-free rate.

596
00:54:49,000 --> 00:54:54,000
So that's the substrate of any investment.

597
00:54:54,000 --> 00:54:58,000
You expect it to at least give you the risk-free rate.

598
00:54:58,000 --> 00:55:04,000
Because otherwise, why wouldn't you just buy treasury bills and get no risk?

599
00:55:04,000 --> 00:55:19,000
Plus that little piece beta sub S times R sub M minus R sub F.

600
00:55:19,000 --> 00:55:25,000
That extra on the premium.

601
00:55:25,000 --> 00:55:30,000
The R sub F sitting by itself is just, you've got to get that.

602
00:55:30,000 --> 00:55:37,000
No investment should have an expectation of getting you less than you could get by taking no risk.

603
00:55:37,000 --> 00:55:50,000
Plus you get the market premium magnified or demagnified by the beta.

604
00:55:50,000 --> 00:55:58,000
So just take one here on that one example where the beta was 1.5.

605
00:55:58,000 --> 00:56:03,000
You would get, what did I say, risk-free rate was 4%.

606
00:56:03,000 --> 00:56:04,000
You'll get that.

607
00:56:04,000 --> 00:56:14,000
Plus you'll get 1.5 times 16% minus 4%.

608
00:56:14,000 --> 00:56:15,000
So what is that?

609
00:56:15,000 --> 00:56:22,000
4% plus 18%.

610
00:56:22,000 --> 00:56:37,000
So your expected return to a stock with a beta of 1.5 should be 22% under the current conditions of R sub M 16% and R sub F 4%.

611
00:56:37,000 --> 00:56:47,000
That's what you should get.

612
00:56:47,000 --> 00:56:49,000
Draw a graph here.

613
00:56:49,000 --> 00:56:52,000
We're almost home, folks.

614
00:56:52,000 --> 00:56:54,000
Draw a graph here.

615
00:56:54,000 --> 00:56:55,000
Okay.

616
00:56:55,000 --> 00:56:57,000
Ouch.

617
00:56:57,000 --> 00:57:02,000
On the horizontal axis, I'm going to mark, I'm going to plot beta.

618
00:57:02,000 --> 00:57:07,000
0.2, 0.4, 0.6, 0.8, 1.0.

619
00:57:07,000 --> 00:57:13,000
1.2, 1.4, 1.6, 1.8, 2.0.

620
00:57:13,000 --> 00:57:16,000
In other words, I'm looking at a range of betas.

621
00:57:16,000 --> 00:57:23,000
And on this axis, the expected return.

622
00:57:23,000 --> 00:57:33,000
If I have a beta of 0.0, I should get the risk-free rate.

623
00:57:33,000 --> 00:57:38,000
No risk, you just get the risk-free rate, whatever it is.

624
00:57:38,000 --> 00:57:46,000
In that case, that would be the 4% for the current example.

625
00:57:46,000 --> 00:57:59,000
If I have a stock that has a beta of 1, look what happens if I do a beta of 1.

626
00:57:59,000 --> 00:58:14,000
I would get the expected return would be 4% plus 1.00 times 16% minus 4%.

627
00:58:14,000 --> 00:58:20,000
So I should get 4% plus 12%.

628
00:58:20,000 --> 00:58:27,000
Surprise, surprise, you get the expected return to the market.

629
00:58:27,000 --> 00:58:37,000
So in other words, at a 1, you would get the return to the market.

630
00:58:37,000 --> 00:58:40,000
And then two points are enough to make a line.

631
00:58:40,000 --> 00:58:45,000
That's the graph of cap M. Don't make it complicated, for God's sake.

632
00:58:45,000 --> 00:58:50,000
All it is is just a graph of that weird equation right there.

633
00:58:50,000 --> 00:58:58,000
So in other words, I could say, well, 1.5 should be up there, about 22%.

634
00:58:58,000 --> 00:59:05,000
A 0.5 should be about there, about what?

635
00:59:05,000 --> 00:59:13,000
What did I figure that to be? 0.56%.

636
00:59:13,000 --> 00:59:16,000
It's just a graph. We have a name for this.

637
00:59:16,000 --> 00:59:28,000
It's called the securities market line.

638
00:59:28,000 --> 00:59:36,000
In test after test that we've done, looking at actual returns versus their betas,

639
00:59:36,000 --> 00:59:51,000
the scatter plots are always very close to the securities market line.

640
00:59:51,000 --> 00:59:59,000
It is so reliable that we can use it very confidently.

641
00:59:59,000 --> 01:00:01,000
It's not perfect. It's not going to give you exactly,

642
01:00:01,000 --> 01:00:05,000
because there is random noise in the system like there is in any system.

643
01:00:05,000 --> 01:00:11,000
But the plots almost always, we take a stock, it's beta, we look at its return,

644
01:00:11,000 --> 01:00:14,000
and we plot the beta against the return.

645
01:00:14,000 --> 01:00:17,000
It's always very close to the securities market line.

646
01:00:17,000 --> 01:00:22,000
This works. And there have been fancier models since then.

647
01:00:22,000 --> 01:00:29,000
Book mentions them, but everyone knows that the cap M has not been overrun by the data.

648
01:00:29,000 --> 01:00:36,000
The data establishes the reliability of cap M.

649
01:00:36,000 --> 01:00:40,000
However, one mention.

650
01:00:40,000 --> 01:00:46,000
First of all, the worst thing in the world that you could believe in is someone

651
01:00:46,000 --> 01:00:53,000
who is said to have way high returns on his or her portfolio.

652
01:00:53,000 --> 01:00:59,000
This is a genius. This person beat the market.

653
01:00:59,000 --> 01:01:04,000
Well, actually all that means is that they just took a high beta.

654
01:01:04,000 --> 01:01:09,000
They didn't beat the cap M. They just got higher than the market return.

655
01:01:09,000 --> 01:01:13,000
Why did they get that? Because they took a beta above one with their portfolio.

656
01:01:13,000 --> 01:01:17,000
There's no hero in that kind of activity.

657
01:01:17,000 --> 01:01:20,000
And yet these contests are done all the time.

658
01:01:20,000 --> 01:01:25,000
The awards are given to the fund managers who get the highest return on their portfolios.

659
01:01:25,000 --> 01:01:29,000
Almost invariably all they did was take giant betas.

660
01:01:29,000 --> 01:01:36,000
They didn't do anything. But what the cap M says to do if you want high returns,

661
01:01:36,000 --> 01:01:41,000
you take high risk. Nothing heroic about that.

662
01:01:41,000 --> 01:01:51,000
However, once in a while we do see someone who's got like a magic.

663
01:01:51,000 --> 01:01:58,000
A fund manager, an amateur investor, and they will get, let me show you.

664
01:01:58,000 --> 01:02:03,000
They'll get a return way above what cap M says they should.

665
01:02:03,000 --> 01:02:08,000
It'll be way above the cap of the securities market line.

666
01:02:08,000 --> 01:02:11,000
We see it. I've seen it myself.

667
01:02:11,000 --> 01:02:14,000
How they do it, God only knows.

668
01:02:14,000 --> 01:02:19,000
And oftentimes they'll do it for a couple of years and then they'll lose their magic.

669
01:02:19,000 --> 01:02:22,000
But we do have a name for this.

670
01:02:22,000 --> 01:02:31,000
See this distance from what they get down to what they should have gotten from the cap M?

671
01:02:31,000 --> 01:02:37,000
We have a name for that. It's called Jensen's Alpha.

672
01:02:37,000 --> 01:02:45,000
Now Jensen's Alpha can be negative too, obviously.

673
01:02:45,000 --> 01:02:47,000
But we do see it.

674
01:02:47,000 --> 01:02:54,000
Now if you've got a Jensen's Alpha of 0.2%, that's nothing.

675
01:02:54,000 --> 01:02:57,000
That's within the data noise range.

676
01:02:57,000 --> 01:03:04,000
But we see some, once in a while we'll see someone who's got a Jensen's Alpha of 3, 4%.

677
01:03:04,000 --> 01:03:08,000
In one case there was some guy who had 12% Jensen's Alpha.

678
01:03:08,000 --> 01:03:12,000
In other words, it was up there in the screen.

679
01:03:12,000 --> 01:03:15,000
Matter of fact, there's even a website.

680
01:03:15,000 --> 01:03:18,000
It's very reliable and I love the people who run it.

681
01:03:18,000 --> 01:03:22,000
It's called Seeking Alpha.

682
01:03:22,000 --> 01:03:26,000
If you get into investments, yeah, it's a play on that.

683
01:03:26,000 --> 01:03:28,000
We're looking for the alpha.

684
01:03:28,000 --> 01:03:37,000
Trying to find a way to do investments so that we get above what the physics of the model say we should.

685
01:03:37,000 --> 01:03:41,000
So Jensen's Alpha, we're all seeking Jensen's Alpha.

686
01:03:41,000 --> 01:03:52,000
Getting a higher reward than we deserve to get based upon the beta of the stock.

687
01:03:52,000 --> 01:04:01,000
And believe me, when you invest, any investment house that is worth its salt is looking for the magician,

688
01:04:01,000 --> 01:04:11,000
looking for the wizard, looking for that person who does trades and is constantly getting better than the CAPM says.

689
01:04:11,000 --> 01:04:21,000
And so once, I mean, if you've got, give 10,000 free accounts, all you have to do is find three or four who have found alpha.

690
01:04:21,000 --> 01:04:23,000
They're not seeking it, they're finding it.

691
01:04:23,000 --> 01:04:25,000
And then we just front run them.

692
01:04:25,000 --> 01:04:31,000
Whenever they put in an order, we put an order in front of them so that we can play on their genius.

693
01:04:31,000 --> 01:04:36,000
This is how it's done. It's illegal, but illegal schmigel.

694
01:04:36,000 --> 01:04:38,000
But there you go.

695
01:04:38,000 --> 01:04:43,000
That's the extent of this.

696
01:04:43,000 --> 01:04:45,000
I should point out one thing here.

697
01:04:45,000 --> 01:04:47,000
And it's almost obvious.

698
01:04:47,000 --> 01:05:06,000
Look, the return to a portfolio would be you take the weight of each stock in the portfolio times its return.

699
01:05:06,000 --> 01:05:08,000
You know, it's just the weighted average.

700
01:05:08,000 --> 01:05:14,000
The portfolio's return will be the weighted average of the component's returns.

701
01:05:14,000 --> 01:05:30,000
And also, the beta of a portfolio is nothing but the same trick from I equals one to N of the beta of each stock times its weight in the entire portfolio.

702
01:05:30,000 --> 01:05:33,000
In other words, just a sum product from Excel.

703
01:05:33,000 --> 01:05:36,000
So you can find the beta of your portfolio.

704
01:05:36,000 --> 01:05:41,000
So if you've got like a high beta stock in your portfolio and you don't want to be that high,

705
01:05:41,000 --> 01:05:46,000
just put in a low beta stock and the weights of them will balance it out.

706
01:05:46,000 --> 01:05:49,000
That's part of what's called portfolio control.

707
01:05:49,000 --> 01:05:59,000
It's just making sure that you always have betas in a range so that they average out to the beta that you desire.

708
01:05:59,000 --> 01:06:00,000
Had enough?

709
01:06:00,000 --> 01:06:01,000
That's all I have for you today.

710
01:06:01,000 --> 01:06:11,000
I thank you.