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Alan Kring Productions in association with Emergent Light Studio presents

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

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This is Business Finance, FIL 240 for autumn semester 2023.

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Today, the Capital Asset Pricing Model.

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This is kind of a mathy thing, but none of the math really gets all that intense.

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It's just one of those things where you have to have the formulas,

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and the formulas are just basically arithmetic,

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but the conceptual framework behind it actually can almost become interesting in certain regards.

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But first, I'll look at the numbers, and the numbers suck.

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Really bad. This was a bare day.

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As you can see, the Dow was down almost a percent.

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The S&P 500 down more, a percent and a third,

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and the NASDAQ down more than a percent and a half.

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The typical risk return kind of thing.

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Now, it was a sour day on the street.

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Part of that was, this is a big earnings week.

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You see, companies estimate their earnings, and then a while later,

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a few weeks or whatever later, they actually say these were the earnings.

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And so the question is, well, did they beat their earnings estimate,

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or did they fail to even meet their earnings estimate?

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Well, it's a mixed bag right now.

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For example, if you want to look at Tesla TSLA, it missed its earnings,

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and the market just potty trained that stock down four and three quarters percent for the day.

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Now, in the aftermarket, it's recovering some of that.

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As the diehard Tesla people think they're getting a bargain

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and buying it back up in the aftermarket.

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But also, some of the other financial, some of the banks didn't come in with earnings that were up to expectations.

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So that caused some concerns, and that was one of the things that was rattling the market a little bit today.

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And then, of course, you have the issues, first of all, the Middle East.

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We know we're not near a global war or anything like that,

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but at the same time, it just isn't quieting down.

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And there's always a concern that someone's going to do something really stupid

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and cause the whole shenanigans to turn ugly.

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And that would, of course, oil distribution would be disrupted and all that good stuff.

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Not a lot of chance of it, but it still spooks the market a little bit, that kind of thing.

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And there are other things that are concerning the market.

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Notice that crude oil, it pushed up against its resistance at 88, and it came through a little bit.

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Notice how it spiked up there and it chickened out.

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It went to like 89.50, and then it just dropped right back down, back through the resistance,

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and then it just kind of flopped around, moved up a little bit.

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But that is what we would call a war premium.

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Notice that it is not large at all, but there's a low probability of a premium, so they do it. Yeah?

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Oh, look at Netflix.

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What the heck? Why did it do that?

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Because I typed it in the wrong place. NFLX.

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NFLX.

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Holy sh-

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Do you see that? Did you know the aftermarket did that?

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Holy cow.

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Yeah, that's the only thing that would do that.

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Now if you look down here, they didn't show it, but see, this is the earnings pattern.

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Notice that they beat their earnings in third quarter last year, and then they missed their earnings a little bit there.

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And then they hit their earnings, beat them, and this time, wow.

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Blow out Q3 subscriber gains.

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Most of that is the extra money I'm paying the damn service now.

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That's where they got all that extra earnings.

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But yeah, that's impressive.

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Yeah, I was going to invest in it, but I decided to let other people do that one today.

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I'm impressed though. That's an awful heavy spike.

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One day, if you bought at the bottom today, you would have earned 12.83%.

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Boy, would I like to have bought a couple of call options on that.

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But notice that's a very expensive stock.

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Anyway, that's more of those, you've got to have the money to do that kind of, play that kind of a game.

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But the whole big thing is subscriptions.

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It's not quality of the shows. They have great movies, but they have these subscriptions.

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They apparently beat their subscription estimates by a mile.

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It's also the competition.

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There are other services out there that are offering shows.

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Everything from the high end at Amazon, which Amazon is thinking of.

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Well, no, they're going to charge a premium starting in January.

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If you don't want ads in their movies, then you have to pay them extra money.

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Then you have Hulu, you have lower end services.

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Some people, you have niche services like Britbox and things like that to some people.

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Then you have the quasi pirate services like Plex that some people have figured out how to use.

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Is it me or does the water here smell bad?

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Where did we get the water from? Ass Lake?

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Oh, so it collected all the bacteria and fungus in the pipes.

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Oh well, meh.

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Yellow and all that.

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Okay.

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Thank you for that one though. That one was pretty impressive.

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I mean, it always brings joy to my heart when I see someone, not me, making money on a day.

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One thing I do want to point out though, S&P 500, look at the volume on those 500 stocks.

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Typical day, vol is 3.7 billion.

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Today, only 2.4 billion.

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Again, the investors are staying on the sidelines.

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That's much weaker.

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Some of my former students who are now traders in Chicago and New York,

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they've been sending me DMs on LinkedIn saying this is the beginning of the apocalypse and all that.

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No one's investing.

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I don't think it's that bad, but it is kind of spooky.

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Anyway, going over here, you see the gold had a little bit of a run upward.

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It's nowhere...

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Oh, well, I want to look at gold.

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Gold had a run upward of about 1 1⁄3%.

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It's not really that close to its resistance level of $2,000 an ounce,

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but still, that is a lack of confidence when investors are going toward the metals.

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Now, over here with the 10-year bonds, just have a look at those puppies today.

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The bonds...

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Get the ads off there.

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Go over here to the bonds.

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Okay, you've got the 10-year bond.

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Price up, yield down.

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So the price...

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I'm sorry, the yield up, price down.

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So there's a price going down that's selling bonds.

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Investors are getting out of the safety of bonds.

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They're certainly not going to the riskier equities.

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So, uh-oh, gold.

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That's that sequence of the flight to quality from stocks to bonds to get safety.

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If bonds don't look safe, then bonds to gold, the safe harbor of gold.

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And, of course, the last round of that is if the gold's selling, then people are buying bullets.

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And I recommend 5.56 rounds myself just for the fun.

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Now, notice that the euro and the pound both depreciated against the dollar modestly today.

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Still, the strength is in the American economy.

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Not in Europe, not in London, in Great Britain.

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And the Japanese yen...

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Yep, it depreciated. It's backwards. It depreciated, too.

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So, U.S., as bad as it might look in the stock market,

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it's still the currency that's showing the strength in the global community.

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Nikkei was all down, all through last night, and just barely eked out a tiny 1.01% return.

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It came up above negatives and, at the end of the day, just a tiny bit up.

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That's not anything.

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London, on the other hand, it just went down the whole day.

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It just kept flushing the toilet.

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And by the end of the day, it was down more than 1%.

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So, there you go. There's the look at the world as it is today.

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And we had...let me see something. I'm dying. I can't stand it.

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Netflix, I can't quit you.

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Okay, right now in the aftermarket, that's still going to be a hell of a return.

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But notice something. Netflix doesn't pay a dividend,

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so you're riding capital gain the whole way through a year investment.

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You're not going to get a check in the mail,

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so you just have to hope that price just keeps going up and up and up.

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And that's just the way those kinds of stocks are.

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Their plowback ratio is 100%.

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Every penny of that $9.39 per share they make,

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they plow it right back into the company's operations

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to keep that company growing.

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As long as they can get the subscribers,

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and they can keep producing good television series

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and get some movies that are just out of first run,

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they're going to be fine.

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I hope. Okay, now.

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Let me take that off the table here

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and do something.

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Now part of this sounds like it's kind of off the track,

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but it really isn't, especially for your longer term.

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Harkening back to your statistics course,

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your sadistic course as it were, long ago,

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and I won't assume that you remember a whole lot from that course.

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As you take more of your upper level courses,

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you'll run into some of these statistics again,

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and this is just one first shot at your upper level use of statistics.

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And I also get into, make sure you understand that there is a difference

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between statistics and the underlying probability theory.

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One thing that is always of concern is that if numbers,

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if you don't know what these numbers have as their weaknesses

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and their strengths from probability theory,

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you can get into trouble using statistics.

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That's why I bring up a few little points along here.

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Now, measures.

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In other words, what numbers are useful to us?

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There's an old saying,

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make your statistics simple or make them PhD level.

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Don't play in the middle.

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And these are all simple statistics,

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and they are the ones that are mostly what we use.

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We sometimes get into the heavy duty stuff, or at least we think we do.

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But first things first, central tendency.

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Good grief.

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Did that like hurt?

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Sounded like a nose fart.

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Okay, here we go, central tendency.

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Where does the data want to show up?

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It clusters around.

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It's like you have a light outside at night,

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and all of the moths, they tend to go to that light.

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Sometimes you'll find one over here saying, where the hell am I?

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You'll find one over there, uh-oh, a bat's about to eat me.

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But they tend to go toward that light, and that's what data, we hope, does.

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Now, some data just doesn't do that very much.

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We can find a measure of central tendency,

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but it really doesn't tell us because the data's so scattered, so evenly,

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that the central tendency measure doesn't usually work.

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The big one is the mean, or otherwise known as the average.

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But the thing is, there are several of those.

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But if you want a fancy formula, one over N times the sum from I equals one to N,

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that's the number of data points, and let's talk about with stocks.

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The return at period I, the sum of the returns divided by the number of returns.

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Nothing hard about that.

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Add them up, divide by the number of data points.

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However, even that one can have some different uses, the different tricks on it.

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One is the weighted average, where different data points have different weights.

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I'll show you this in practice.

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But the way you would do it is you would do the sum from I equals one to N

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of the return to I times the weight of that stock in the portfolio.

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You say, well, where's the N?

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Oh, it's actually right there.

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You see, this simple formula, one over N, is the weight of each one in the portfolio.

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So a simple average is just a very basic application of a weighted average.

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Now let me show you this in practice.

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Pull up an Excel sheet here and do it again.

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Okay, the stock, the return, and the weight.

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So you could have stock A, stock B, and stock C.

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And I'm showing you this in Excel to show you,

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and I think I've shown you this formula, this trick before,

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but it's a really sweet little thing.

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Return to stock A, let's say, is 7.6%.

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The return to stock B is 13.9%.

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And the return to stock C is 11.4%.

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Now let's say the weight of A in the portfolio is 0.4.

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The weight of B in the portfolio is 0.5.

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And the weight of C in the portfolio is 0.1.

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The sum of those would be 100%.

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Now then, the weighted average, watch how I do this,

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equals sum product of the column of returns, by the column of weights.

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And there's your Uncle Bob, 11.13%.

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And you could do it the other way.

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You could take this times this, plus this times this,

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plus this times this, and you get the same number.

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Sum product just takes that pain out of it.

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That's all it does.

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And you'll find that that's useful in your homework,

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and there will be other classes.

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You'll probably find sum product to be one of those little go-tos.

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Yeah?

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Click on what?

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This one?

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Watch.

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Sum product, now sum product actually has some really sophisticated uses too,

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and I don't even know all I can do.

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But you take the sum product, and you say,

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I want to take each cell in this column, comma, times its associated cell in this column.

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So it's B2 colon B4, comma, C2 colon C4.

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Boom.

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It's a nice little formula.

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It's a nice little Excel formula.

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Like I said, I saw someone using it in some bizarre, complicated way,

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and I didn't quite get it, what he was doing.

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But yeah, for us, you'll find that this is really useful the next week or the week after that

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for some problems where you calculate the weights by your given stock prices.

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So then you have the overall price of what's the value of your portfolio.

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So you take each of the prices divided by the total value, and that makes the weight.

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And the sum product can do that really cleanly for you.

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But that's next week.

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Don't worry about it.

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Okay, so that's weighted average.

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It's much more useful for a lot of data.

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But then there's another one.

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It's called the moving average.

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I'm going to put it over here.

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The moving average.

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Now, the moving average actually takes a subset of the data.

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Suppose that you had one, two, three, four, five, six, seven, eight, up through today,

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last eight days of data.

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Now, a simple average would just take, add those data values up, divide by eight.

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A moving average says, okay, let's do an eight-day moving average.

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So the first for today, you get the average.

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And then tomorrow, a new data point comes in, so you drop the oldest and find that average.

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And then the next day, a new data point comes in.

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So this time, you drop what is again the oldest data point, and you put in that one.

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Now, that would be an eight-day moving average.

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You could do a three-day, a four-day.

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You see, what this does is this doesn't hold on to old data.

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It keeps refreshing and dumping off the oldest data.

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And in my business, in stocks, currencies, we have different ones that tell us different things.

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Like, for example, in currencies, you'll have a 120-day moving average.

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Then you'll also have a 60-day moving average, a 30-day moving average, a 15-day moving average.

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And each of them tells you a little bit different story about what's happening with the data.

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So these moving averages are actually pretty popular, especially when there is new data coming in.

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You don't really want to see there's a tradeoff.

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If you have a long moving average, like that eight-day, that's taking account of things that have happened for the last eight days.

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But it's a three-day, well, that would take only what's happened over the last three days.

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The tradeoff is that the three-day is a more current moving average.

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But it also has that at the expense of older data that might have some use in it.

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You know, I take your moving average of your weight, okay, for the last 60 days, okay?

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So every day I drop the weight to your 61 days earlier.

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Well, you're saying, well, that's nonsense, okay?

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Let's take a five-day.

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Every five days, I drop the oldest one every day of five days.

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Well, what I'm doing is I'm sacrificing what might be useful information about cycles in your weight behavior.

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I wouldn't see those anymore with the shorter moving average.

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The data is fresher, it's more current, but at the same time it is giving up some information.

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Like, who knows, every 40 days you decide to gain 30 pounds.

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And then you lose it two days later after your pizza festivities are over.

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Something like that.

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That's a terrible example.

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Okay, now there's also another version of moving average.

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It's the weighted moving average where you would take, like, okay, let's say a three-day moving average.

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Well, the most recent day you give, like, 60% weight.

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And then the day before that, let's say you give 30% weight.

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And then the day before that, the oldest day, you give 10% weight too.

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So it has a trailing off effect.

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So you're using older data, but you're giving it less weight, less seriousness in the overall average.

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So that's a weighted moving average.

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Those are quite popular in, not just in stocks, but also in some kinds of businesses too.

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Weighted moving averages of cash flows, weighted moving averages of costs and things like that.

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So that we are recognizing older data as telling us something,

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but we're not giving it a lot of weight in the final story of the moving average that we're using.

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So there's that.

302
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Now, there is another measure of central tendency.

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It is not a formula.

304
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Well, I guess you could write it as a formula.

305
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But it's more of a metric that we really should pay attention to, especially comparing it to the mean.

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This one is called the median.

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50% of the data is above, and 50% of the data is below.

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The median has one very desirable feature.

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It is robust to the size of the data points.

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Let me show you what I mean.

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Forever in all kinds of subjects, we assume a normal distribution of data, the bell curve.

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Even if it's not normally distributed, they say, well, if you collect enough data, then it becomes a normal.

313
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We could have large numbers and all that.

314
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Bullshit.

315
00:27:10,000 --> 00:27:12,000
It actually doesn't.

316
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And that's an unfortunate thing, especially in a subject like education.

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Let me show you something.

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See, here, the average is also the 50-50 point.

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The median is the mean in a symmetric normal distribution or in any symmetric distribution.

320
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But what if you have data, which mostly is clustering up here, but you have this long tail of low scores on a midterm?

321
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Well, see, that would mean that your average is going to be sensitive to these low scores impacting on it.

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And so your average is going to look very low because of all these people over here that didn't do so well.

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The same is true on the other side.

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You can have data where most people are down here.

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And then you have the few really bright students in the class who get the 98s and the 100s.

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Well, they are going to have a disproportionate impact on the average.

327
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But it doesn't matter for the median.

328
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The median just says half here, half here.

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The classic story is the professor, well, that test couldn't have been that hard because I got one person who got 100.

330
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Everyone else got a low score, but one person got 100.

331
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So that 100 skews the story of what the data is trying to tell us.

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And so the median is actually quite useful for our purposes in a lot of different subjects.

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00:29:05,000 --> 00:29:14,000
Like, for example, the average on this midterm was a – it came out to be a 75.5.

334
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The median came out to be a 78.

335
00:29:18,000 --> 00:29:21,000
Well, what would that tell me?

336
00:29:21,000 --> 00:29:26,000
Ah, the average was dragged down by – that I looked at the data.

337
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There were some – there were a couple of zeros – 10 percent, so a couple of 18 percent.

338
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Those very low scores made the average look lower than what the data was saying.

339
00:29:42,000 --> 00:29:47,000
Actually, it was about 50, 50 at 78 percent.

340
00:29:47,000 --> 00:29:53,000
But if you look at the average, it was lower than that because of some low data points.

341
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I've had it go the other way, too.

342
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I got a couple of people ace an exam.

343
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Everyone else just didn't do so well.

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But those high 100 and a 99, they skewed the data upward a little bit.

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00:30:08,000 --> 00:30:12,000
And so it looked like the average wasn't as bad –

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it's telling me that it was better, the performance on the exam, than it really was.

347
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This happens in all kinds of situations – in human behaviors, in testing of failure rates.

348
00:30:27,000 --> 00:30:34,000
It's rather surprising when you've got – you think the data is normally distributed, okay?

349
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But there are – the outliers are actually kind of important to the process.

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So you want to look at the average and the median and see if they're telling you something

351
00:30:45,000 --> 00:30:49,000
because they're different, okay?

352
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So that's central tendency.

353
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Let me get that off the board here.

354
00:30:57,000 --> 00:31:06,000
And now we get to the other important aspect of this – risk.

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Now, as I've said before, the classic measure of risk is the standard deviation.

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And that is the 1 over n minus 1, if it's a sample, times the sum from i equals 1

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to the number of data points of the data point –

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each data point minus the average of the data points, squared.

359
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And then you take the square root of that mess.

360
00:31:57,000 --> 00:32:04,000
Now for the population, you use n, not n minus 1.

361
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Notice that as the sample size increases, n minus 1 gets closer and closer to n.

362
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So the sample standard deviation approaches the population standard deviation.

363
00:32:19,000 --> 00:32:24,000
Just kind of a side point there.

364
00:32:24,000 --> 00:32:32,000
That's the measure of central tendency.

365
00:32:32,000 --> 00:32:38,000
However, that doesn't do us a lot of good.

366
00:32:38,000 --> 00:32:43,000
By the way, why does this square come in?

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Because we want data points below the average and data points above the average,

368
00:32:49,000 --> 00:32:51,000
not to cancel each other out.

369
00:32:51,000 --> 00:32:56,000
So we square, and that way they're always going to be sum positive.

370
00:32:56,000 --> 00:32:58,000
Okay.

371
00:32:58,000 --> 00:33:02,000
That one isn't the only one.

372
00:33:02,000 --> 00:33:12,000
The one that is our friend is beta.

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00:33:12,000 --> 00:33:18,000
That measures only one part of the risk, the systematic risk,

374
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the risk that cannot be canceled out by the volatility of other things in the portfolio.

375
00:33:28,000 --> 00:33:37,000
Now if you want to know, there is a formula for beta, and I'll just give it to you.

376
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The beta of a stock is going to be the correlation coefficient of that stock

377
00:33:44,000 --> 00:33:54,000
with the market portfolios times the standard deviation of that stock

378
00:33:54,000 --> 00:34:00,000
divided by the standard deviation of the market portfolio.

379
00:34:00,000 --> 00:34:04,000
Now, why am I showing you this?

380
00:34:04,000 --> 00:34:07,000
First of all, notice this.

381
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Beta is our favored measure because we can use it to compare stocks

382
00:34:16,000 --> 00:34:21,000
to things that are important, like the market portfolio.

383
00:34:21,000 --> 00:34:28,000
Suppose that you had a risk-free portfolio, just T-bills, okay?

384
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Well, the standard deviation of the T-bills would be zero.

385
00:34:32,000 --> 00:34:34,000
They have no volatility.

386
00:34:34,000 --> 00:34:39,000
You just sit there at the risk-free rate.

387
00:34:39,000 --> 00:34:47,000
So you'd have a zero divided by the market portfolio's standard deviation.

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So you'd have a beta of zero.

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00:34:50,000 --> 00:34:57,000
So in other words, that explains why we use beta, the risk-free rate,

390
00:34:57,000 --> 00:35:06,000
when I start this little diagram right here of expected returns against beta.

391
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A beta of zero would be whatever the current risk-free rate is.

392
00:35:15,000 --> 00:35:17,000
Now, let's talk about another one.

393
00:35:17,000 --> 00:35:23,000
What if we were talking about the beta of the market portfolio?

394
00:35:23,000 --> 00:35:25,000
Well, the beta of the market portfolio,

395
00:35:25,000 --> 00:35:30,000
first of all, the correlation between the market and itself would be one.

396
00:35:30,000 --> 00:35:34,000
And then the sigma of the market portfolio divided by the market,

397
00:35:34,000 --> 00:35:37,000
sigma of the market portfolio would be one.

398
00:35:37,000 --> 00:35:44,000
That's why the beta of the market is one, 1.00.

399
00:35:44,000 --> 00:35:56,000
And so that is our expected return to the market portfolio.

400
00:35:56,000 --> 00:35:59,000
And so there's a straight line relationship there.

401
00:35:59,000 --> 00:36:08,000
Beta, and I'm going to show you beta in practice, how we can use it.

402
00:36:08,000 --> 00:36:16,000
Let me do something here.

403
00:36:16,000 --> 00:36:36,000
Which brings us to the all-time famous capital asset pricing model.

404
00:36:36,000 --> 00:36:41,000
The CAPM.

405
00:36:41,000 --> 00:36:43,000
Little history of it.

406
00:36:43,000 --> 00:36:53,000
It was first published, I believe, in the mid-1960s, maybe the early 60s.

407
00:36:53,000 --> 00:36:57,000
And it has an elegant simplicity to it.

408
00:36:57,000 --> 00:37:05,000
What it tells us is what we should expect the return to a stock should be

409
00:37:05,000 --> 00:37:07,000
if we know the beta of the stock.

410
00:37:07,000 --> 00:37:09,000
It tells us what to expect.

411
00:37:09,000 --> 00:37:13,000
That's not necessarily what's going to happen, but it's our best estimate.

412
00:37:13,000 --> 00:37:20,000
Now, the CAPM, at first, in academia, it was quickly embraced.

413
00:37:20,000 --> 00:37:22,000
Yeah, we get it.

414
00:37:22,000 --> 00:37:26,000
The theory is elegantly simple, kind of that stuff right there.

415
00:37:26,000 --> 00:37:28,000
Really nice, really easy.

416
00:37:28,000 --> 00:37:35,000
But then it was not embraced outside of academia until later.

417
00:37:35,000 --> 00:37:40,000
Corporations didn't embrace it for a long time.

418
00:37:40,000 --> 00:37:49,000
Even business courts refused to recognize that this was the way to determine expected returns to securities

419
00:37:49,000 --> 00:37:54,000
until into the 1980s, I believe it was.

420
00:37:54,000 --> 00:38:03,000
And since then, some other theories that have got equations so that we can calculate expected returns have come along.

421
00:38:03,000 --> 00:38:06,000
I think the book even brings those up.

422
00:38:06,000 --> 00:38:09,000
One's called APT, the arbitrage pricing theory.

423
00:38:09,000 --> 00:38:18,000
None of them are as clean or as simple as the CAPM, and none of them do it much better.

424
00:38:18,000 --> 00:38:32,000
Even in graduate level courses, there is currently a really high-powered mathematical equation that will do the same thing CAPM does.

425
00:38:32,000 --> 00:38:37,000
I show it, I teach it, but I'm not convinced it's better than CAPM.

426
00:38:37,000 --> 00:38:40,000
I really am not convinced of that.

427
00:38:40,000 --> 00:38:42,000
But anyway, here it is.

428
00:38:42,000 --> 00:38:55,000
The expected return to a stock over, let's say, a year would be the risk-free rate plus the beta of the stock

429
00:38:55,000 --> 00:39:03,000
times the expected return to the market portfolio minus the risk-free rate.

430
00:39:03,000 --> 00:39:06,000
That's CAPM.

431
00:39:06,000 --> 00:39:16,000
Now, if you were thinking of getting a tattoo, you can't go wrong with this equation.

432
00:39:16,000 --> 00:39:21,000
I mean, I actually knew a fellow who had CAPM.

433
00:39:21,000 --> 00:39:25,000
This was his tat.

434
00:39:25,000 --> 00:39:27,000
Weirdest ass guy I ever met.

435
00:39:27,000 --> 00:39:38,000
Anyway, so what you need for a stock, you would need the risk-free rate.

436
00:39:38,000 --> 00:39:45,000
You would need the expected return to the market portfolio, and you would need the beta of the stock.

437
00:39:45,000 --> 00:39:50,000
And then you could tell, you could determine the expected return to that stock.

438
00:39:50,000 --> 00:39:52,000
Those would be the parameters.

439
00:39:52,000 --> 00:39:59,000
Now, the beta, heck, you can just go to Yahoo Finance or the Standard & Poor's Global Net Advantage

440
00:39:59,000 --> 00:40:01,000
or any one of a number of other sites.

441
00:40:01,000 --> 00:40:04,000
I think Google has its own finance site.

442
00:40:04,000 --> 00:40:09,000
You can even just type in, what is the beta of TSLA?

443
00:40:09,000 --> 00:40:12,000
It'll give it to you.

444
00:40:12,000 --> 00:40:14,000
So that's not hard to get.

445
00:40:14,000 --> 00:40:20,000
Okay, what about the risk-free rate?

446
00:40:20,000 --> 00:40:32,000
We, and this comes from a previous lecture, the risk-free rate, the best place to get a proxy.

447
00:40:32,000 --> 00:40:42,000
Now, the risk-free rate itself is a theoretical thing, but it has a very close approximation in real data.

448
00:40:42,000 --> 00:40:49,000
The yield on a one-year T-bill, that's about, a treasury bill is riskless.

449
00:40:49,000 --> 00:40:53,000
Or at least almost as close as you could get to riskless.

450
00:40:53,000 --> 00:40:56,000
So why don't we just use a one-year T-bill?

451
00:40:56,000 --> 00:41:02,000
And so I brought up the U.S. Department of Treasury par yield curve rates,

452
00:41:02,000 --> 00:41:10,000
and for the latest one, for a one-year T-bill, is 5.48%.

453
00:41:10,000 --> 00:41:15,000
That's how you do it.

454
00:41:15,000 --> 00:41:28,000
So that's what we'll use today, 5.48%.

455
00:41:28,000 --> 00:41:31,000
Now, the expected return in the market.

456
00:41:31,000 --> 00:41:35,000
This one is the wild card.

457
00:41:35,000 --> 00:41:48,000
What we do, there are services that take a survey of a group of industry and academic professionals in finance,

458
00:41:48,000 --> 00:41:53,000
and they have the members of the survey give their estimate,

459
00:41:53,000 --> 00:42:00,000
what in their judgment will be the expected return to the world portfolio over the next year.

460
00:42:00,000 --> 00:42:04,000
There are many of these services out there.

461
00:42:04,000 --> 00:42:06,000
I'm in one of them.

462
00:42:06,000 --> 00:42:16,000
It has 70 people, people in finance industry and in universities, reputable universities,

463
00:42:16,000 --> 00:42:22,000
and we each give our estimate, and then they take the average of the data that they get.

464
00:42:22,000 --> 00:42:30,000
And like I said, there's a group I'm in, and then there's dozens and dozens of others who do the same thing.

465
00:42:30,000 --> 00:42:37,000
For the most part, those estimates are usually really close,

466
00:42:37,000 --> 00:42:43,000
in the same, to a tenth of a decimal place away from each other.

467
00:42:43,000 --> 00:42:47,000
Once in a while, you see some deviation, but you pay your penny and you take your pick.

468
00:42:47,000 --> 00:42:49,000
This is the way it goes.

469
00:42:49,000 --> 00:43:04,000
Right now, here's one, 12.85%.

470
00:43:04,000 --> 00:43:09,000
Now, one thing I would caution, if you try to do something like a Google,

471
00:43:09,000 --> 00:43:12,000
what is the expected return to the market portfolio,

472
00:43:12,000 --> 00:43:17,000
most of the hits you'll get are giving you a little explanation of what it is,

473
00:43:17,000 --> 00:43:19,000
instead of giving you a hard number.

474
00:43:19,000 --> 00:43:21,000
The reason is simple.

475
00:43:21,000 --> 00:43:26,000
The hard numbers are done by services that charge you for it,

476
00:43:26,000 --> 00:43:30,000
and so they don't let Google find those numbers and then just say,

477
00:43:30,000 --> 00:43:34,000
oh, well, here it is, no, because we charge for that service.

478
00:43:34,000 --> 00:43:40,000
But overall, that's somewhere in the right ballpark right now.

479
00:43:40,000 --> 00:43:44,000
But okay, you've got all the pieces of it.

480
00:43:44,000 --> 00:43:47,000
Now, let's do the wild thing.

481
00:43:47,000 --> 00:43:51,000
Okay.

482
00:43:51,000 --> 00:43:54,000
Let's go back here to Yee-haw Finance.

483
00:43:54,000 --> 00:43:59,000
Let's take a stock.

484
00:43:59,000 --> 00:44:04,000
Netflix and Chill.

485
00:44:04,000 --> 00:44:08,000
Pay is 1.26.

486
00:44:08,000 --> 00:44:11,000
Now, I want to point out something.

487
00:44:11,000 --> 00:44:16,000
I'm going to write this here before I forget.

488
00:44:16,000 --> 00:44:21,000
The beta of NFLX is 1.26.

489
00:44:21,000 --> 00:44:27,000
Now, before I go any further, you see this little piece right here in the cap M,

490
00:44:27,000 --> 00:44:30,000
the expected return to the market portfolio minus risk-free rate.

491
00:44:30,000 --> 00:44:42,000
We call that the market premium over risk-free.

492
00:44:42,000 --> 00:44:51,000
Mostly you'll hear me say the market premium.

493
00:44:51,000 --> 00:44:59,000
It's the extra reward for taking the risk of the market instead of going riskless.

494
00:44:59,000 --> 00:45:01,000
Let me explain.

495
00:45:01,000 --> 00:45:05,000
You.

496
00:45:05,000 --> 00:45:07,000
I'll tell you a story.

497
00:45:07,000 --> 00:45:14,000
One night when I was a teenager, I had all this angst and thoughts about my life in the future.

498
00:45:14,000 --> 00:45:18,000
So I climbed out on the roof of my house from my bedroom window,

499
00:45:18,000 --> 00:45:22,000
and I just yelled to this guy, why am I here?

500
00:45:22,000 --> 00:45:26,000
This old guy across the street leans out his window and says,

501
00:45:26,000 --> 00:45:29,000
you climbed out there, you moron.

502
00:45:29,000 --> 00:45:32,000
Now I heard his wife say, Harold, come to bed.

503
00:45:32,000 --> 00:45:34,000
And then a dog barked.

504
00:45:34,000 --> 00:45:40,000
That was my first introduction to existentialism.

505
00:45:40,000 --> 00:45:46,000
But anyway, you are here on an expectation that you will get a degree

506
00:45:46,000 --> 00:45:48,000
and you will get a good job and a good salary.

507
00:45:48,000 --> 00:45:51,000
That's one of the reasons that we do this.

508
00:45:51,000 --> 00:45:53,000
We pursue this life.

509
00:45:53,000 --> 00:45:59,000
You could have come out of high school and gotten a $12 an hour job.

510
00:45:59,000 --> 00:46:02,000
But your expectation is that you'll come out of college

511
00:46:02,000 --> 00:46:06,000
and you'll be able to get maybe a $50 an hour job.

512
00:46:06,000 --> 00:46:13,000
50 minus 12 is the market premium over risk-free life.

513
00:46:13,000 --> 00:46:15,000
That's exactly what this is.

514
00:46:15,000 --> 00:46:23,000
It's just what's the extra reward for going long instead of just playing the safe game?

515
00:46:23,000 --> 00:46:28,000
That's what the expected return of the market portfolio minus the risk-free rate is.

516
00:46:28,000 --> 00:46:29,000
And I'll show you here.

517
00:46:29,000 --> 00:46:33,000
We're going to look at the expected return to Netflix over the next year.

518
00:46:33,000 --> 00:46:35,000
Okay?

519
00:46:35,000 --> 00:46:46,000
The expected return to Netflix, NFLX, is equal to the risk-free rate,

520
00:46:46,000 --> 00:47:08,000
5.48% plus the beta of the stock, 1.26, times 11.85% minus 5.48%.

521
00:47:08,000 --> 00:47:09,000
That's all there is to it.

522
00:47:09,000 --> 00:47:11,000
That's cap M.

523
00:47:11,000 --> 00:47:14,000
Now, I'm going to do something here.

524
00:47:14,000 --> 00:47:20,000
Let me take 11.85 minus, I'm going to do that market premium to show you something,

525
00:47:20,000 --> 00:47:22,000
to point out something.

526
00:47:22,000 --> 00:47:30,000
Let me pull up a calculator here.

527
00:47:30,000 --> 00:47:41,000
11.85 plus 5.48, 6.37.

528
00:47:41,000 --> 00:47:43,000
So I'm going to write this step.

529
00:47:43,000 --> 00:47:46,000
There's a reason why I'm doing this.

530
00:47:46,000 --> 00:47:51,000
I mean, you can just put this into a calculator and quickly get it.

531
00:47:51,000 --> 00:48:05,000
So we've got 5.48% plus 1.26 times 6.37%.

532
00:48:05,000 --> 00:48:08,000
And this is where you see what beta really is.

533
00:48:08,000 --> 00:48:10,000
Beta is a magnifier.

534
00:48:10,000 --> 00:48:11,000
That's all it is.

535
00:48:11,000 --> 00:48:17,000
It says how much the market premium is magnified by this stock.

536
00:48:17,000 --> 00:48:29,000
Notice the stocks above 1 will expand the market premium.

537
00:48:29,000 --> 00:48:35,000
Stocks that are below 1 will detract from it.

538
00:48:35,000 --> 00:48:41,000
I felt something fall off at my age when you start feeling things fall off your belt.

539
00:48:41,000 --> 00:48:46,000
God, was that my spleen?

540
00:48:46,000 --> 00:48:51,000
There, get my recorder back in its pouch.

541
00:48:51,000 --> 00:48:53,000
There we go, good.

542
00:48:53,000 --> 00:48:55,000
That's all beta is.

543
00:48:55,000 --> 00:48:58,000
Beta is just a magnifier or a demagnifier.

544
00:48:58,000 --> 00:49:03,000
So a stock with a beta below 1 would demagnify the market premium.

545
00:49:03,000 --> 00:49:06,000
A stock above 1 will magnify it.

546
00:49:06,000 --> 00:49:12,000
And as you can see, Netflix above 1 magnifies the market premium.

547
00:49:12,000 --> 00:49:20,000
So in this case, finishing this up, first I'm going to now take the 6.37%

548
00:49:20,000 --> 00:49:30,000
and I'm going to multiply it by the 1.26 beta of Netflix.

549
00:49:30,000 --> 00:49:33,000
8.0262.

550
00:49:33,000 --> 00:49:40,000
Now the thing I want to bring up here, well where's that risk-free rate sitting out there on its own?

551
00:49:40,000 --> 00:49:43,000
That's pretty easy to explain.

552
00:49:43,000 --> 00:49:52,000
Any investment with risk should at least pay you what you would get with no risk.

553
00:49:52,000 --> 00:49:57,000
That's why you add that 5.48, the risk-free rate on,

554
00:49:57,000 --> 00:50:04,000
because anything you invest in has darn well better pay you at least what you'd get

555
00:50:04,000 --> 00:50:08,000
if you didn't take the risk of a real investment.

556
00:50:08,000 --> 00:50:23,000
So when I add that in there, that 5.48, 5.48, I get 13.5062.

557
00:50:23,000 --> 00:50:33,000
Percent.

558
00:50:33,000 --> 00:50:44,000
That is our best estimate of your expected return for a one-year holding period.

559
00:50:44,000 --> 00:50:46,000
That's our best estimate.

560
00:50:46,000 --> 00:50:53,000
Will that be the holding period return?

561
00:50:53,000 --> 00:50:55,000
Probably not.

562
00:50:55,000 --> 00:50:58,000
I mean, it could be a little above that, a little below that.

563
00:50:58,000 --> 00:51:02,000
But we know from data that we've collected over and over again

564
00:51:02,000 --> 00:51:08,000
that on average, cap M is damn good at it.

565
00:51:08,000 --> 00:51:15,000
Now let me do one more here just to show you.

566
00:51:15,000 --> 00:51:23,000
I'm going to find a stock that would have a beta below one.

567
00:51:23,000 --> 00:51:33,000
Let's take, I wonder if Johnson & Johnson, I can't remember.

568
00:51:33,000 --> 00:51:38,000
Yeah, Johnson & Johnson has a.57 beta.

569
00:51:38,000 --> 00:51:46,000
So let me throw Johnson & Johnson in there.

570
00:51:46,000 --> 00:51:54,000
The beta of JNJ is, what did I say that was?

571
00:51:54,000 --> 00:51:57,000
.570.57.

572
00:51:57,000 --> 00:52:02,000
So this beta will demagnify the market premium.

573
00:52:02,000 --> 00:52:11,000
So I run it again, the expected return for a one-year hold on Johnson & Johnson JNJ

574
00:52:11,000 --> 00:52:22,000
would be risk-free rate, 5.48 percent,

575
00:52:22,000 --> 00:52:32,000
plus the beta of Johnson & Johnson, 0.57 times market premium over risk-free,

576
00:52:32,000 --> 00:52:40,000
which would be the same thing it was before for a given economic regime,

577
00:52:40,000 --> 00:52:46,000
minus the 5.48 percent.

578
00:52:46,000 --> 00:52:59,000
So, we get to 5.48 percent plus 0.57 times that number I calculated there, 6.37.

579
00:52:59,000 --> 00:53:03,000
See those numbers in there are the same.

580
00:53:03,000 --> 00:53:09,000
And so you see what's happening again in this case,

581
00:53:09,000 --> 00:53:14,000
the beta is demagnifying the market premium.

582
00:53:14,000 --> 00:53:21,000
That's all beta does, it's not some magical thing, it's just a multiplier.

583
00:53:21,000 --> 00:53:31,000
And if I crank that one through, just very quickly,

584
00:53:31,000 --> 00:53:57,000
5.48 plus 0.57 times, open parenthesis, 6.37, close the parenthesis, equals 9.1109.

585
00:53:57,000 --> 00:54:09,000
Look at that.

586
00:54:09,000 --> 00:54:16,000
Stocks with betas above one will pay more than the market premium,

587
00:54:16,000 --> 00:54:21,000
stocks below one will pay less than it.

588
00:54:21,000 --> 00:54:29,000
If I put in a beta of one, surprise, you would get the 11.85.

589
00:54:29,000 --> 00:54:30,000
That's how it works.

590
00:54:30,000 --> 00:54:38,000
Beta is, and I don't know if you see it yet, but it is actually elegantly self-contained.

591
00:54:38,000 --> 00:54:41,000
It's self-explaining.

592
00:54:41,000 --> 00:54:43,000
And this is actually what we use.

593
00:54:43,000 --> 00:54:48,000
Now we can do little other things, I'll show you later,

594
00:54:48,000 --> 00:54:55,000
but if you got a beta for a stock, this is the beta for your portfolio,

595
00:54:55,000 --> 00:55:03,000
this is the best way, best practices for a forecast of what you should expect to make.

596
00:55:03,000 --> 00:55:07,000
Take stocks with higher betas, you get a higher expected return.

597
00:55:07,000 --> 00:55:12,000
Take stocks with lower betas, you get a lower expected return.

598
00:55:12,000 --> 00:55:16,000
And the risk level is commensurate with it.

599
00:55:16,000 --> 00:55:19,000
The beta is telling you, you're going to take risk with this,

600
00:55:19,000 --> 00:55:22,000
so you expect a higher return, duh.

601
00:55:22,000 --> 00:55:26,000
You don't take as much risk, then you should expect a lower return.

602
00:55:26,000 --> 00:55:29,000
That's all beta does.

603
00:55:29,000 --> 00:55:37,000
Now that graph that I did over there, that's just the capital asset pricing model grasped out.

604
00:55:37,000 --> 00:55:39,000
We even have a name for that line.

605
00:55:39,000 --> 00:55:51,000
It's called the securities market line.

606
00:55:51,000 --> 00:55:53,000
That's where it is.

607
00:55:53,000 --> 00:55:58,000
So in other words, I could, if I had this graph done really well,

608
00:55:58,000 --> 00:56:04,000
I could say, oh, a beta of 1.26, there would be its,

609
00:56:04,000 --> 00:56:11,000
this would be the 5.48 here on the graph, the R sub F, the Y intercept,

610
00:56:11,000 --> 00:56:17,000
and this would be the 11.85% expected return in the market portfolio.

611
00:56:17,000 --> 00:56:24,000
You tell me 1.26, I could run up to the line and I could say, oh, there it is on the graph.

612
00:56:24,000 --> 00:56:31,000
The.57, there that is, take it up to the line, there's that one.

613
00:56:31,000 --> 00:56:37,000
It's just like, it's just a plain old high school linear algebra is all it is.

614
00:56:37,000 --> 00:56:40,000
A two-point formula of a line.

615
00:56:40,000 --> 00:56:48,000
And that's the whole story of the capital asset pricing model.

616
00:56:48,000 --> 00:56:53,000
One, okay, two last points here.

617
00:56:53,000 --> 00:57:00,000
I almost forgot there's another measure of risk too, but I'll get to that in a second.

618
00:57:00,000 --> 00:57:07,000
Now, we collect data all the time on stocks, on portfolios,

619
00:57:07,000 --> 00:57:12,000
and see what CAPM says will happen and see what really happens.

620
00:57:12,000 --> 00:57:21,000
For the most part, the data is really nicely clustered right around the securities market line.

621
00:57:21,000 --> 00:57:26,000
It's not going to be perfect because the risk-free rate changes, obviously,

622
00:57:26,000 --> 00:57:31,000
and the expected return to the market portfolio changes and all that kind of stuff.

623
00:57:31,000 --> 00:57:34,000
But it stays very close to the line.

624
00:57:34,000 --> 00:57:48,000
However, once in a blue moon, we find a stock or some investor or fund manager's portfolio

625
00:57:48,000 --> 00:57:59,000
that has a certain beta, and it's way above the securities market line.

626
00:57:59,000 --> 00:58:04,000
In other words, it's temporarily blowing theory.

627
00:58:04,000 --> 00:58:08,000
We actually look for this.

628
00:58:08,000 --> 00:58:14,000
See this portfolio right here, whatever it is, maybe a beta of 1.10,

629
00:58:14,000 --> 00:58:25,000
it should have this return, but instead it has an abnormally higher return.

630
00:58:25,000 --> 00:58:26,000
It happens.

631
00:58:26,000 --> 00:58:30,000
It's not frequent that it happens, but it does happen.

632
00:58:30,000 --> 00:58:34,000
We have a name for that.

633
00:58:34,000 --> 00:58:44,000
Whatever that number is, we call it that unusual extra.

634
00:58:44,000 --> 00:58:57,000
We call it Jensen's alpha.

635
00:58:57,000 --> 00:59:05,000
Believe me, there are algorithms running night and day looking for Jensen's alphas, positive.

636
00:59:05,000 --> 00:59:07,000
Now, you could have a negative one.

637
00:59:07,000 --> 00:59:08,000
Who cares about that?

638
00:59:08,000 --> 00:59:14,000
That's just a bad investor or a bad stock.

639
00:59:14,000 --> 00:59:20,000
Now, if you've got a Jensen's alpha of 0.2%, who cares?

640
00:59:20,000 --> 00:59:25,000
We're looking for those Jensen's alphas that are like a couple percent

641
00:59:25,000 --> 00:59:33,000
because those tell us something is interesting about the stock or about this investor.

642
00:59:33,000 --> 00:59:41,000
In fact, one of the few really reputable websites for investment advice

643
00:59:41,000 --> 00:59:47,000
and really good explanations has the name.

644
00:59:47,000 --> 00:59:50,000
It's called Seeking Alpha.

645
00:59:50,000 --> 00:59:55,000
If you're an insider, which you sort of are now, you get it.

646
00:59:55,000 --> 01:00:00,000
We're always looking for the alpha, the Jensen's alpha,

647
01:00:00,000 --> 01:00:09,000
trying to find stocks, portfolios, fund managers that have positive Jensen's alphas in their portfolios.

648
01:00:09,000 --> 01:00:12,000
Now, that's one of those great ridiculous things.

649
01:00:12,000 --> 01:00:18,000
You've got all these awards for, well, this fund manager got the highest return of the year.

650
01:00:18,000 --> 01:00:20,000
Great son of a bitch.

651
01:00:20,000 --> 01:00:22,000
Who cares?

652
01:00:22,000 --> 01:00:26,000
You can get any portfolio return you want by taking a high enough beta.

653
01:00:26,000 --> 01:00:32,000
What really matters is you might not have even made that much money,

654
01:00:32,000 --> 01:00:38,000
but did you have a Jensen's alpha that was well, that was decently positive?

655
01:00:38,000 --> 01:00:40,000
That's impressive.

656
01:00:40,000 --> 01:00:46,000
That's when you should get an award, not for just getting the highest return of all the funds.

657
01:00:46,000 --> 01:00:48,000
You can do that.

658
01:00:48,000 --> 01:00:51,000
That securities market line goes to betas of 5, 10.

659
01:00:51,000 --> 01:00:53,000
Yeah, who cares?

660
01:00:53,000 --> 01:00:55,000
You took a huge risk and you got a huge return.

661
01:00:55,000 --> 01:00:56,000
Good for you.

662
01:00:56,000 --> 01:00:57,000
Give yourself a cookie.

663
01:00:57,000 --> 01:01:01,000
What we really want to know is did you have magic?

664
01:01:01,000 --> 01:01:06,000
And believe me, the houses all the time, they're looking.

665
01:01:06,000 --> 01:01:10,000
You get your free stock trades at our brokerage house.

666
01:01:10,000 --> 01:01:13,000
Aren't we wonderful sons of bitches?

667
01:01:13,000 --> 01:01:19,000
Do you think they're not looking for the investors who pulled positive Jensen's alphas?

668
01:01:19,000 --> 01:01:20,000
They're looking for them.

669
01:01:20,000 --> 01:01:21,000
We all are.

670
01:01:21,000 --> 01:01:28,000
And if one of you was, you know, if I found out that one of you had a consistently positive Jensen's alpha,

671
01:01:28,000 --> 01:01:32,000
well, you and I would be buddies.

672
01:01:32,000 --> 01:01:34,000
So that's important.

673
01:01:34,000 --> 01:01:36,000
Just keep that in mind, okay?

674
01:01:36,000 --> 01:01:37,000
What about insider trading?

675
01:01:37,000 --> 01:01:39,000
Is that also like, searching for that as well?

676
01:01:39,000 --> 01:01:42,000
Insider trading, we don't talk about insider trading.

677
01:01:42,000 --> 01:01:45,000
It never happens, ever.

678
01:01:45,000 --> 01:01:52,000
I've never seen a case of insider trading that I would want to talk about because then I'd have to testify in court

679
01:01:52,000 --> 01:01:56,000
and then I'd have someone would pop a cap in my ass.

680
01:01:56,000 --> 01:01:59,000
So it doesn't happen, okay?

681
01:01:59,000 --> 01:02:01,000
It's sort of like aliens.

682
01:02:01,000 --> 01:02:09,000
If I were slurped up by an alien mothership and they, and when I came back, would I tell people?

683
01:02:09,000 --> 01:02:14,000
No, because they'd all think I'm a crazy mofo, which I am, but I don't want people to believe that.

684
01:02:14,000 --> 01:02:21,000
Besides, their captain of that mothership, Zork, he said if I told anyone what had happened in that ship,

685
01:02:21,000 --> 01:02:25,000
he would personally use his phaser on me.

686
01:02:25,000 --> 01:02:33,000
So what happened in that mothership stays in that mothership.

687
01:02:33,000 --> 01:02:34,000
One last thing.

688
01:02:34,000 --> 01:02:38,000
I forgot one measure of risk.

689
01:02:38,000 --> 01:02:41,000
The Sharpe ratio.

690
01:02:41,000 --> 01:02:43,000
Let me show it to you real quick here.

691
01:02:51,000 --> 01:03:03,000
The Sharpe ratio is kind of a low brow measure.

692
01:03:03,000 --> 01:03:04,000
And you can calculate.

693
01:03:04,000 --> 01:03:08,000
All it says is what was the return to the stock minus the risk-free rate?

694
01:03:08,000 --> 01:03:16,000
In other words, what was this stock's premium divided by the standard deviation of the stock?

695
01:03:16,000 --> 01:03:18,000
Here's the thing about it, though.

696
01:03:18,000 --> 01:03:26,000
What we hate about it is that it uses standard deviation, which we really don't want to use.

697
01:03:26,000 --> 01:03:31,000
We want to use only the part of the risk that cannot be removed.

698
01:03:31,000 --> 01:03:37,000
All we want, so the question is why would you use sigma instead of beta?

699
01:03:37,000 --> 01:03:40,000
Well, here's the logic of it.

700
01:03:40,000 --> 01:03:47,000
Suppose that you had the risk-free rate, we'll just keep it at 5.48%.

701
01:03:47,000 --> 01:04:04,000
Now let's say we have stock one with a standard deviation of let's say 3.00%, just to make it simple.

702
01:04:04,000 --> 01:04:06,000
Okay?

703
01:04:06,000 --> 01:04:15,000
And the return to stock one is 8.50%.

704
01:04:15,000 --> 01:04:31,000
So Sharpe would be the return to the stock 8.50 less 5.48% divided by 3.00%.

705
01:04:31,000 --> 01:04:33,000
Now watch this.

706
01:04:33,000 --> 01:04:39,000
I'm going to run that one for you and see what happens.

707
01:04:39,000 --> 01:04:53,000
So I would have the return 8.5 minus 5.48 equals and then divided by 3%.

708
01:04:53,000 --> 01:05:03,000
You get 1.0067.

709
01:05:03,000 --> 01:05:23,000
Now suppose that we have another stock which has a return to stock two of let's say some lower amount, let's say 7.10%.

710
01:05:23,000 --> 01:05:31,000
Standard deviation of stock two is let's say 4.2%.

711
01:05:31,000 --> 01:05:36,000
And the risk-free rate is still 5.48%.

712
01:05:36,000 --> 01:05:43,000
Let me run the Sharpe again and see what happens.

713
01:05:43,000 --> 01:06:12,000
The Sharpe again in this case would be 7.1 minus 5.48 divided by standard deviation of 4.2, 0.3857.

714
01:06:12,000 --> 01:06:24,000
The stock dominates by the Sharpe ratio.

715
01:06:24,000 --> 01:06:30,000
Scaled by the standard deviation, each one scaled by a standard deviation.

716
01:06:30,000 --> 01:06:34,000
That first one is giving you more bang for the buck.

717
01:06:34,000 --> 01:06:36,000
Now here's another part of it.

718
01:06:36,000 --> 01:06:39,000
Why are they using sigma instead of beta?

719
01:06:39,000 --> 01:06:51,000
Because a normal investor doesn't have a well-diversified portfolio of 30, 50 fancy stocks.

720
01:06:51,000 --> 01:06:54,000
They're riding a thin portfolio.

721
01:06:54,000 --> 01:07:05,000
So Sharpe, the whole standard deviation, not just a beta, is the correct way to judge performance of their portfolios.

722
01:07:05,000 --> 01:07:09,000
And another thing, finding the Sharpe ratio.

723
01:07:09,000 --> 01:07:18,000
You notice with these stocks that I've shown you so far this semester, you don't see the Sharpe ratio, you see the beta.

724
01:07:18,000 --> 01:07:26,000
If I showed you mutual funds, almost every one of them, they report Sharpe.

725
01:07:26,000 --> 01:07:31,000
Because mutual funds can tend to be thin portfolios.

726
01:07:31,000 --> 01:07:37,000
And so the Sharpe is how you compare the performance of the portfolios.

727
01:07:37,000 --> 01:07:43,000
And the mutual funds report Sharpe ratio is.

728
01:07:43,000 --> 01:07:51,000
And it is actually more useful for funds like mutual funds than the beta is.

729
01:07:51,000 --> 01:07:54,000
There's no cap M or anything like this.

730
01:07:54,000 --> 01:08:02,000
All it does is say what is relatively better than what else.

731
01:08:02,000 --> 01:08:04,000
That's all Sharpe can do for you.

732
01:08:04,000 --> 01:08:05,000
Well, I shouldn't say that.

733
01:08:05,000 --> 01:08:08,000
There are people who do really fancy things with it.

734
01:08:08,000 --> 01:08:16,000
But it's out there and I can't criticize it because honestly for a thin portfolio, beta means nothing.

735
01:08:16,000 --> 01:08:21,000
Because beta is risk in a well-diversified portfolio.

736
01:08:21,000 --> 01:08:27,000
If you're sitting there with one, two, three stocks, then probably you're eating the whole sigma.

737
01:08:27,000 --> 01:08:35,000
So you might as well use a metric that uses the whole sigma instead of that piece of it that's beta.

738
01:08:35,000 --> 01:08:36,000
That's all I have for you today.

739
01:08:36,000 --> 01:08:51,000
Thank you.