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Alan Kring Productions in association with Emergent Light Studio presents the Illinois

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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 and the formulas are

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just basically arithmetic, but the conceptual framework behind it actually can almost become

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interesting in certain regards.

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

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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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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, a few weeks or whatever

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later, they actually say these were the earnings.

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

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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 and the market

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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 as the diehard Tesla people think

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they're getting a bargain and buying it back up in the aftermarket.

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Also, some of the other financial, some of the banks didn't come in with earnings that

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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

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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're nowhere near a global war or anything like that, but at the same time it just isn't

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quieting down.

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

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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

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little bit.

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Notice how it spiked up there and it chickened out.

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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

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it.

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Yeah.

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Oh, look at that.

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What the heck?

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Why does it do that?

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Because I type it in the wrong place.

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NFLX.

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NFLX.

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Holy shh.

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Do you see that?

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Did you know the aftermarket did that?

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Holy cow.

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Yeah.

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Yeah.

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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

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earnings a little bit there and then they hit their earnings, beat them and this time,

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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.

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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 got to have the money to do that kind of, play that kind

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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.

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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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Starting from the high end at Amazon, which Amazon is thinking of, well no, they're going

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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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And then you have Hulu, you have lower end services.

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And some people, you have niche services like Brit Box and things like that to some people.

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And then you have the sort of quasi pirate services like Plex that some people have figured

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out how to use.

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Oh, is it me or does the water here smell bad?

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Where did they get the water from?

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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, yellow and all that.

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Okay.

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Thank you for that one though.

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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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And some of my fellow, 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, you know, this is the beginning of the apocalypse

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

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It's nowhere, oh, well, I want to look at gold.

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Gold had a run upward of about one and a third percent.

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

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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, the ads off there, go over here to the bonds.

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Okay, you've got the 10-year bond, price up, yield down.

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

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So there are 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

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bullets.

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I recommend 556 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, not in Europe, not in London, in Great Britain,

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and the Japanese yen, yep, it depreciated, it's backwards, it depreciated too.

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So US, as bad as it might look in the stock market, it's still the currency that's showing

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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 ended 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, 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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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, so you're riding capital gain

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the whole way through a year investment.

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You're not going to get a check in the mail, so you just have to hope that price just keeps

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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 plow back ratio is 100%.

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Every penny of that $9.39 per share they make, they plow it right back into the country.

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The company's operations to keep that company growing.

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So as long as they can get the subscribers and they can keep producing good television

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series, and get some movies that were just out of first run, they are going to be fine.

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I hope.

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Now, let me take that off the table here 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

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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

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at your upper level use of statistics.

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And I also get into, make sure you understand

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that there is a difference between statistics

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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

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as their weaknesses and their strengths

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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, make your statistics simple

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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,

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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,

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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.

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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

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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,

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or otherwise known as the average.

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But the thing is that there are several of those.

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But if you want a fancy formula,

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one over n times the sum from i equals one to n,

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that's the number of data points.

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And let's talk about with stocks.

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The return at period i,

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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,

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the different tricks on it.

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One is the weighted average.

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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

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from i equals one to n of the return to i

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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,

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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

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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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And 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 1.5.

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And the weight of C in the portfolio is 0.5.

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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.

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Watch how I do this.

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I'm gonna show you how to do this.

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So you can see here, you can see the column of returns,

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comma, by the column of weights.

246
00:17:52,040 --> 00:17:55,040
And there's your Uncle Bob.

247
00:17:55,040 --> 00:17:58,040
11.13%.

248
00:17:58,040 --> 00:18:01,040
And you could do it the other way.

249
00:18:01,040 --> 00:18:05,040
This times this, plus this times this, plus this times this,

250
00:18:05,040 --> 00:18:07,040
and you get the same number.

251
00:18:07,040 --> 00:18:10,040
Some product just takes that pain out of it.

252
00:18:10,040 --> 00:18:12,040
That's all it does.

253
00:18:12,040 --> 00:18:15,040
And you'll find that that's useful in your homework.

254
00:18:15,040 --> 00:18:17,040
And there will be other classes.

255
00:18:17,040 --> 00:18:21,040
You'll probably find some product to be one of those little go-tos.

256
00:18:21,040 --> 00:18:23,040
Yeah?

257
00:18:23,040 --> 00:18:25,040
You wanna see it?

258
00:18:25,040 --> 00:18:26,040
Click on what?

259
00:18:26,040 --> 00:18:27,040
Oh, down right there.

260
00:18:27,040 --> 00:18:29,040
Click on the one over here.

261
00:18:29,040 --> 00:18:30,040
Oh, let me show you.

262
00:18:30,040 --> 00:18:31,040
This one?

263
00:18:31,040 --> 00:18:32,040
Oh, yeah.

264
00:18:32,040 --> 00:18:33,040
Watch.

265
00:18:33,040 --> 00:18:35,040
Sum product.

266
00:18:35,040 --> 00:18:39,040
Now, sum product actually has some really sophisticated uses, too,

267
00:18:39,040 --> 00:18:41,040
and I don't even know all I can do.

268
00:18:41,040 --> 00:18:43,040
But you take the sum product, and you say,

269
00:18:43,040 --> 00:18:47,040
I want you to take each cell in this column,

270
00:18:47,040 --> 00:18:52,040
comma, times its associated cell in this column.

271
00:18:52,040 --> 00:19:02,040
So it's B2, colon, B4, comma, C2, colon, C4.

272
00:19:02,040 --> 00:19:06,040
Boom.

273
00:19:06,040 --> 00:19:07,040
It's a nice little formula.

274
00:19:07,040 --> 00:19:09,040
It's a nice little Excel formula.

275
00:19:09,040 --> 00:19:15,040
Like I said, I saw someone using it in some bizarre, complicated way,

276
00:19:15,040 --> 00:19:19,040
and I didn't quite get it, what he was doing.

277
00:19:19,040 --> 00:19:26,040
But yeah, for us, you'll find that this is really useful

278
00:19:26,040 --> 00:19:31,040
the next week or the week after that for some problems

279
00:19:31,040 --> 00:19:38,040
where you calculate the weights by your given stock prices.

280
00:19:38,040 --> 00:19:43,040
So then you have the overall price of what's your portfolio,

281
00:19:43,040 --> 00:19:44,040
of your portfolio.

282
00:19:44,040 --> 00:19:47,040
So you take each of the prices divided by the total value,

283
00:19:47,040 --> 00:19:49,040
and that makes the weight,

284
00:19:49,040 --> 00:19:53,040
and the sum product can do that really cleanly for you.

285
00:19:53,040 --> 00:19:56,040
But that's next week. Don't worry about it.

286
00:19:56,040 --> 00:19:59,040
Okay, so that's weighted average.

287
00:19:59,040 --> 00:20:04,040
It's much more useful for a lot of data.

288
00:20:04,040 --> 00:20:08,040
But then there's another one.

289
00:20:08,040 --> 00:20:17,040
It's called the moving average.

290
00:20:17,040 --> 00:20:19,040
I'm going to put it over here.

291
00:20:19,040 --> 00:20:23,040
The moving average.

292
00:20:23,040 --> 00:20:34,040
Now the moving average actually takes a subset of the data.

293
00:20:34,040 --> 00:20:45,040
Suppose that you had one, two, three, four, five, six, seven, eight

294
00:20:45,040 --> 00:20:48,040
up through today, last eight days of data.

295
00:20:48,040 --> 00:20:52,040
Now a simple average would just take,

296
00:20:52,040 --> 00:20:57,040
add those data values up, divide by eight.

297
00:20:57,040 --> 00:21:07,040
A moving average says, okay, let's do an eight-day moving average.

298
00:21:07,040 --> 00:21:13,040
So the first for today, you get the average.

299
00:21:13,040 --> 00:21:19,040
And then tomorrow a new data point comes in,

300
00:21:19,040 --> 00:21:28,040
so you drop the oldest and find that average.

301
00:21:28,040 --> 00:21:36,040
And then the next day a new data point comes in,

302
00:21:36,040 --> 00:21:44,040
so this time you drop what is again the oldest data point,

303
00:21:44,040 --> 00:21:47,040
and you put in that one.

304
00:21:47,040 --> 00:21:51,040
Now that would be an eight-day moving average.

305
00:21:51,040 --> 00:21:55,040
You could do a three-day, a four-day.

306
00:21:55,040 --> 00:21:59,040
You see what this does is this doesn't hold on to old data.

307
00:21:59,040 --> 00:22:05,040
It keeps refreshing and dumping off the oldest data.

308
00:22:05,040 --> 00:22:18,040
And in my business, in stocks, currencies,

309
00:22:18,040 --> 00:22:21,040
we have different ones that tell us different things.

310
00:22:21,040 --> 00:22:27,040
Like for example, in currencies you'll have a 120-day moving average.

311
00:22:27,040 --> 00:22:30,040
Then you'll also have a 60-day moving average,

312
00:22:30,040 --> 00:22:34,040
a 30-day moving average, a 15-day moving average.

313
00:22:34,040 --> 00:22:38,040
And each of them tells you a little bit different story

314
00:22:38,040 --> 00:22:42,040
about what's happening with the data.

315
00:22:42,040 --> 00:22:45,040
So these moving averages are actually pretty popular,

316
00:22:45,040 --> 00:22:49,040
especially when there is new data coming in.

317
00:22:49,040 --> 00:22:53,040
You don't really want to see there's a trade-off.

318
00:22:53,040 --> 00:22:57,040
If you have a long moving average, like that eight-day,

319
00:22:57,040 --> 00:23:00,040
that's taking account of things that have happened

320
00:23:00,040 --> 00:23:02,040
for the last eight days.

321
00:23:02,040 --> 00:23:07,040
But it's a three-day, well that would take only what's happened

322
00:23:07,040 --> 00:23:09,040
over the last three days.

323
00:23:09,040 --> 00:23:14,040
The trade-off is that the three-day is a more current moving average.

324
00:23:14,040 --> 00:23:19,040
But it also has that at the expense of older data

325
00:23:19,040 --> 00:23:22,040
that might have some use in it.

326
00:23:22,040 --> 00:23:32,040
You know, I take your moving average of your weight, okay,

327
00:23:32,040 --> 00:23:36,040
for the last 60 days, okay.

328
00:23:36,040 --> 00:23:43,040
So every day I drop the weight to your 61 days earlier.

329
00:23:43,040 --> 00:23:47,040
Well, you're saying, well that's nonsense, okay.

330
00:23:47,040 --> 00:23:52,040
Let's take a five-day, every five days.

331
00:23:52,040 --> 00:23:56,040
I drop the oldest one every day of five days.

332
00:23:56,040 --> 00:24:01,040
Well, what I'm doing is I'm sacrificing what might be useful information

333
00:24:01,040 --> 00:24:05,040
about cycles in your weight behavior.

334
00:24:05,040 --> 00:24:09,040
I wouldn't see those anymore with the shorter moving average.

335
00:24:09,040 --> 00:24:12,040
The data is fresher, it's more current,

336
00:24:12,040 --> 00:24:16,040
but at the same time it is giving up some information.

337
00:24:16,040 --> 00:24:22,040
Like, who knows, every 40 days you decide to gain 30 pounds

338
00:24:22,040 --> 00:24:28,040
and then you lose it two days later after your pizza festivities are over.

339
00:24:28,040 --> 00:24:29,040
Something like that.

340
00:24:29,040 --> 00:24:31,040
That's a terrible example.

341
00:24:31,040 --> 00:24:36,040
Okay, now there's also another version of moving average.

342
00:24:36,040 --> 00:24:40,040
It's the weighted moving average where you would take like,

343
00:24:40,040 --> 00:24:43,040
okay, let's say a three-day moving average.

344
00:24:43,040 --> 00:24:50,040
Well, the most recent day you give like 60% weight.

345
00:24:50,040 --> 00:24:55,040
And then the day before that, let's say you give 30% weight.

346
00:24:55,040 --> 00:25:01,040
And then the day before that, the oldest day, you give 10% weight too.

347
00:25:01,040 --> 00:25:04,040
So it has a trailing off effect.

348
00:25:04,040 --> 00:25:09,040
So you're using older data, but you're giving it less weight,

349
00:25:09,040 --> 00:25:14,040
less seriousness in the overall average.

350
00:25:14,040 --> 00:25:16,040
So that's a weighted moving average.

351
00:25:16,040 --> 00:25:21,040
Those are quite popular in not just in stocks,

352
00:25:21,040 --> 00:25:26,040
but also in some kinds of businesses too.

353
00:25:26,040 --> 00:25:29,040
Weighted moving averages of cash flows,

354
00:25:29,040 --> 00:25:32,040
weighted moving averages of costs and things like that.

355
00:25:32,040 --> 00:25:37,040
So that we are recognizing older data as telling us something,

356
00:25:37,040 --> 00:25:41,040
but we're not giving it a lot of weight in the final story

357
00:25:41,040 --> 00:25:44,040
of the moving average that we're using.

358
00:25:44,040 --> 00:25:46,040
So there's that.

359
00:25:46,040 --> 00:25:52,040
Now, there is another measure of central tendency.

360
00:25:52,040 --> 00:25:55,040
It is not a formula.

361
00:25:55,040 --> 00:25:58,040
Well, I guess you could write it as a formula.

362
00:25:58,040 --> 00:26:06,040
But it's more of a metric that we really should pay attention to,

363
00:26:06,040 --> 00:26:11,040
especially comparing it to the mean.

364
00:26:11,040 --> 00:26:16,040
This one is called the median.

365
00:26:16,040 --> 00:26:24,040
50% of the data is above,

366
00:26:24,040 --> 00:26:38,040
and 50% of the data is below.

367
00:26:38,040 --> 00:26:44,040
The median has one very desirable feature.

368
00:26:44,040 --> 00:26:49,040
It is robust to the size of the data points.

369
00:26:49,040 --> 00:26:51,040
Let me show you what I mean.

370
00:26:51,040 --> 00:26:55,040
Forever in all kinds of subjects,

371
00:26:55,040 --> 00:27:01,040
we assume a normal distribution of data, the bell curve.

372
00:27:01,040 --> 00:27:04,040
Even if it's not normally distributed,

373
00:27:04,040 --> 00:27:06,040
they say, well, if you collect enough data,

374
00:27:06,040 --> 00:27:09,040
then it becomes a normal, we can have large numbers and all that.

375
00:27:09,040 --> 00:27:12,040
Bullshit. It actually doesn't.

376
00:27:12,040 --> 00:27:14,040
And that's an unfortunate thing,

377
00:27:14,040 --> 00:27:19,040
especially in a subject like education.

378
00:27:19,040 --> 00:27:21,040
Let me show you something.

379
00:27:21,040 --> 00:27:27,040
See, here, the average is also the 50-50 point.

380
00:27:27,040 --> 00:27:31,040
The median is the mean in a symmetric normal distribution,

381
00:27:31,040 --> 00:27:34,040
or in any symmetric distribution.

382
00:27:34,040 --> 00:27:39,040
But what if you have data,

383
00:27:39,040 --> 00:27:44,040
which mostly is clustering up here,

384
00:27:44,040 --> 00:27:51,040
but you have this long tail of low scores on a midterm?

385
00:27:51,040 --> 00:27:55,040
Well, see, that would mean that your average

386
00:27:55,040 --> 00:28:02,040
is going to be sensitive to these low scores impacting on it.

387
00:28:02,040 --> 00:28:06,040
And so your average is going to look very low

388
00:28:06,040 --> 00:28:12,040
because of all these people over here that didn't do so well.

389
00:28:12,040 --> 00:28:15,040
The same is true on the other side.

390
00:28:15,040 --> 00:28:21,040
You can have data where most people are down here.

391
00:28:21,040 --> 00:28:26,040
And then you have the few really bright students in the class

392
00:28:26,040 --> 00:28:29,040
who get the 98s and the 100s.

393
00:28:29,040 --> 00:28:34,040
Well, they are going to have a disproportionate impact on the average.

394
00:28:34,040 --> 00:28:36,040
But it doesn't matter for the median.

395
00:28:36,040 --> 00:28:41,040
The median just says half here, half here.

396
00:28:41,040 --> 00:28:45,040
The classic story is the professor,

397
00:28:45,040 --> 00:28:47,040
well, that test could have been that hard

398
00:28:47,040 --> 00:28:50,040
because I got one person who got 100.

399
00:28:50,040 --> 00:28:52,040
Everyone else got a low score, but one person got 100.

400
00:28:52,040 --> 00:28:57,040
So that 100 skews the story of what the data is trying to tell us.

401
00:28:57,040 --> 00:29:01,040
And so the median is actually quite useful

402
00:29:01,040 --> 00:29:05,040
for our purposes in a lot of different subjects.

403
00:29:05,040 --> 00:29:09,040
Like, for example, the average on this midterm

404
00:29:09,040 --> 00:29:14,040
was a – it came out to be a 75.5.

405
00:29:14,040 --> 00:29:18,040
The median came out to be a 78.

406
00:29:18,040 --> 00:29:21,040
Well, what would that tell me?

407
00:29:21,040 --> 00:29:25,040
Ah, the average was dragged down by –

408
00:29:25,040 --> 00:29:27,040
that I looked at the data.

409
00:29:27,040 --> 00:29:30,040
There were some – there were a couple of zeros.

410
00:29:30,040 --> 00:29:33,040
How do you – 10 percent?

411
00:29:33,040 --> 00:29:35,040
So a couple of 18 percent.

412
00:29:35,040 --> 00:29:39,040
Those very low scores made the average look lower

413
00:29:39,040 --> 00:29:43,040
than what the other – what the data was saying.

414
00:29:43,040 --> 00:29:47,040
Actually, it was about 50, 50 at 78 percent.

415
00:29:47,040 --> 00:29:51,040
But if you look at the average, it was lower than that

416
00:29:51,040 --> 00:29:53,040
because of some low data points.

417
00:29:53,040 --> 00:29:55,040
I've had it go the other way, too.

418
00:29:55,040 --> 00:29:58,040
I've got a couple of people ace an exam.

419
00:29:58,040 --> 00:30:01,040
Everyone else just didn't do so well.

420
00:30:01,040 --> 00:30:06,040
So this high 100 and a 99, they skewed the data upward

421
00:30:06,040 --> 00:30:08,040
a little bit.

422
00:30:08,040 --> 00:30:11,040
And so it looked like the average wasn't as bad –

423
00:30:11,040 --> 00:30:14,040
what it's telling me that it was better,

424
00:30:14,040 --> 00:30:17,040
the performance on the exam, than it really was.

425
00:30:17,040 --> 00:30:20,040
This happens in all kinds of situations.

426
00:30:20,040 --> 00:30:24,040
In human behaviors, in human behaviors,

427
00:30:24,040 --> 00:30:29,040
in testing of failure rates, it's rather surprising.

428
00:30:29,040 --> 00:30:32,040
When you've got – you don't – you think the data is

429
00:30:32,040 --> 00:30:34,040
normally distributed, okay?

430
00:30:34,040 --> 00:30:39,040
But there are – the outliers are actually kind of

431
00:30:39,040 --> 00:30:40,040
important to the process.

432
00:30:40,040 --> 00:30:43,040
So you want to look at the average and the median

433
00:30:43,040 --> 00:30:45,040
and see if they're telling you something

434
00:30:45,040 --> 00:30:49,040
because they're different, okay?

435
00:30:49,040 --> 00:30:54,040
So that's central tendency.

436
00:30:54,040 --> 00:30:57,040
Let me get that off the board here.

437
00:30:57,040 --> 00:31:02,040
And now we get to the other important aspect of this –

438
00:31:02,040 --> 00:31:06,040
risk.

439
00:31:06,040 --> 00:31:14,040
As I've said before, the classic measure of risk

440
00:31:14,040 --> 00:31:24,040
is the standard deviation.

441
00:31:24,040 --> 00:31:33,040
And that is the 1 over n minus 1, if it's a sample,

442
00:31:33,040 --> 00:31:39,040
times the sum from i equals 1 to the number of data points

443
00:31:39,040 --> 00:31:48,040
of the data point – each data point –

444
00:31:48,040 --> 00:31:51,040
minus the average of the data points, squared.

445
00:31:51,040 --> 00:31:57,040
And then you take the square root of that mess.

446
00:31:57,040 --> 00:32:04,040
Now for the population, you use n, not n minus 1.

447
00:32:04,040 --> 00:32:10,040
Notice that as the sample size increases, n minus 1

448
00:32:10,040 --> 00:32:14,040
and gets closer and closer to n, so the sample standard

449
00:32:14,040 --> 00:32:19,040
deviation approaches the population standard deviation.

450
00:32:19,040 --> 00:32:24,040
Just kind of a side point there.

451
00:32:24,040 --> 00:32:32,040
That's the measure of central tendency.

452
00:32:32,040 --> 00:32:38,040
However, that doesn't do us a lot of good.

453
00:32:38,040 --> 00:32:42,040
By the way, why does this square come in?

454
00:32:42,040 --> 00:32:46,040
Because we want data points below the average

455
00:32:46,040 --> 00:32:50,040
and data points above the average, not to cancel each other out.

456
00:32:50,040 --> 00:32:55,040
So we square, and that way they're always going to be some positive.

457
00:32:55,040 --> 00:32:57,040
Okay.

458
00:32:57,040 --> 00:33:01,040
That one isn't the only one.

459
00:33:01,040 --> 00:33:11,040
The one that is our friend is beta.

460
00:33:11,040 --> 00:33:15,040
That measures only one part of the risk.

461
00:33:15,040 --> 00:33:17,040
The systematic risk.

462
00:33:17,040 --> 00:33:24,040
The risk that cannot be canceled out by the volatility

463
00:33:24,040 --> 00:33:27,040
of other things in the portfolio.

464
00:33:27,040 --> 00:33:32,040
Now if you want to know, there is a formula for beta.

465
00:33:32,040 --> 00:33:36,040
And I'll just give it to you.

466
00:33:36,040 --> 00:33:42,040
Beta of a stock is going to be the correlation coefficient

467
00:33:42,040 --> 00:33:48,040
of that stock with the market portfolios

468
00:33:48,040 --> 00:33:54,040
times the standard deviation of that stock

469
00:33:54,040 --> 00:33:59,040
divided by the standard deviation of the market portfolio.

470
00:33:59,040 --> 00:34:04,040
Now, why am I showing you this?

471
00:34:04,040 --> 00:34:07,040
First of all, notice this.

472
00:34:07,040 --> 00:34:14,040
Beta is our favored measure because we can use it

473
00:34:14,040 --> 00:34:20,040
to compare stocks to things that are important,

474
00:34:20,040 --> 00:34:22,040
like the market portfolio.

475
00:34:22,040 --> 00:34:28,040
Suppose that you had a risk-free portfolio, just T-bills, okay?

476
00:34:28,040 --> 00:34:32,040
Well, the standard deviation of the T-bills would be zero.

477
00:34:32,040 --> 00:34:35,040
They have no volatility.

478
00:34:35,040 --> 00:34:39,040
They just sit there at the risk-free rate.

479
00:34:39,040 --> 00:34:46,040
So you'd have a zero divided by the market portfolio's

480
00:34:46,040 --> 00:34:48,040
standard deviation.

481
00:34:48,040 --> 00:34:51,040
So you'd have a beta of zero.

482
00:34:51,040 --> 00:34:56,040
So in other words, that explains why we use beta,

483
00:34:56,040 --> 00:35:02,040
the risk-free rate, when I start this little diagram right here

484
00:35:02,040 --> 00:35:10,040
of expected returns against beta, a beta of zero would be

485
00:35:10,040 --> 00:35:15,040
whatever the current risk-free rate is.

486
00:35:15,040 --> 00:35:17,040
Now, let's talk about another one.

487
00:35:17,040 --> 00:35:23,040
What if we were talking about the beta of the market portfolio?

488
00:35:23,040 --> 00:35:26,040
Well, the beta of the market portfolio, first of all,

489
00:35:26,040 --> 00:35:30,040
the correlation between the market and itself would be one,

490
00:35:30,040 --> 00:35:34,040
and then the sigma of the market portfolio divided by the

491
00:35:34,040 --> 00:35:37,040
sigma of the market portfolio would be one.

492
00:35:37,040 --> 00:35:44,040
That's why the beta of the market is one, 1.00.

493
00:35:44,040 --> 00:35:56,040
And so that is our expected return to the market portfolio.

494
00:35:56,040 --> 00:35:59,040
And so there's a straight line relationship there.

495
00:35:59,040 --> 00:36:03,040
Beta, and I'm going to show you beta in practice,

496
00:36:03,040 --> 00:36:08,040
how we can use it.

497
00:36:08,040 --> 00:36:16,040
Let me do something here.

498
00:36:16,040 --> 00:36:36,040
Which brings us to the all-time famous capital asset pricing model,

499
00:36:36,040 --> 00:36:41,040
the CAPM.

500
00:36:41,040 --> 00:36:43,040
Little history of it.

501
00:36:43,040 --> 00:36:50,040
It was first published, I believe, in the mid-1960s,

502
00:36:50,040 --> 00:36:53,040
maybe the early 60s.

503
00:36:53,040 --> 00:36:57,040
And it has an elegant simplicity to it.

504
00:36:57,040 --> 00:37:02,040
What it tells us is what we should expect the return to a

505
00:37:02,040 --> 00:37:07,040
stock should be if we know the beta of the stock.

506
00:37:07,040 --> 00:37:09,040
It tells us what to expect.

507
00:37:09,040 --> 00:37:11,040
That's not necessarily what's going to happen,

508
00:37:11,040 --> 00:37:13,040
but it's our best estimate.

509
00:37:13,040 --> 00:37:18,040
Now, the CAPM, at first, in academia,

510
00:37:18,040 --> 00:37:20,040
it was quickly embraced.

511
00:37:20,040 --> 00:37:22,040
Yeah, we get it.

512
00:37:22,040 --> 00:37:26,040
The theory is elegantly simple, kind of that stuff right there.

513
00:37:26,040 --> 00:37:28,040
Really nice, really easy.

514
00:37:28,040 --> 00:37:35,040
But then it was not embraced outside of academia until later.

515
00:37:35,040 --> 00:37:40,040
Corporations didn't embrace it for a long time,

516
00:37:40,040 --> 00:37:45,040
even business courts refused to recognize that this was the way

517
00:37:45,040 --> 00:37:52,040
to determine expected returns to securities until into the 1980s,

518
00:37:52,040 --> 00:37:54,040
I believe it was.

519
00:37:54,040 --> 00:38:00,040
And since then, some other theories that have got equations

520
00:38:00,040 --> 00:38:03,040
so that we can calculate expected returns have come along.

521
00:38:03,040 --> 00:38:06,040
I think the book even brings those up.

522
00:38:06,040 --> 00:38:09,040
One's called APT, the arbitrage pricing theory.

523
00:38:09,040 --> 00:38:14,040
None of them are as clean or as simple as the CAPM,

524
00:38:14,040 --> 00:38:18,040
and none of them do it much better.

525
00:38:18,040 --> 00:38:25,040
Even in graduate level courses, there is currently a really

526
00:38:25,040 --> 00:38:31,040
high-powered mathematical equation that will do the same

527
00:38:31,040 --> 00:38:33,040
thing CAPM does.

528
00:38:33,040 --> 00:38:38,040
I show it, I teach it, but I'm not convinced it's better than CAPM.

529
00:38:38,040 --> 00:38:40,040
Really I'm not convinced of that.

530
00:38:40,040 --> 00:38:42,040
But anyway, here it is.

531
00:38:42,040 --> 00:38:50,040
The expected return to a stock over let's say a year would be

532
00:38:50,040 --> 00:38:56,040
the risk-free rate plus the beta of the stock times expected

533
00:38:56,040 --> 00:39:03,040
return to the market portfolio minus the risk-free rate.

534
00:39:03,040 --> 00:39:06,040
That's CAPM.

535
00:39:06,040 --> 00:39:10,040
Now, if you were thinking of getting a tattoo,

536
00:39:10,040 --> 00:39:16,040
you can't go wrong with this equation.

537
00:39:16,040 --> 00:39:21,040
I mean, I actually knew a fellow who had CAPM.

538
00:39:21,040 --> 00:39:25,040
This was his tat.

539
00:39:25,040 --> 00:39:27,040
Weirdest ass guy I ever met.

540
00:39:27,040 --> 00:39:35,040
But anyway, so what you need for a stock,

541
00:39:35,040 --> 00:39:38,040
you would need the risk-free rate.

542
00:39:38,040 --> 00:39:42,040
You would need the expected return to the market portfolio,

543
00:39:42,040 --> 00:39:45,040
and you would need the beta of the stock,

544
00:39:45,040 --> 00:39:48,040
and then you could tell, you could determine the expected

545
00:39:48,040 --> 00:39:50,040
return to that stock.

546
00:39:50,040 --> 00:39:52,040
Those would be the parameters.

547
00:39:52,040 --> 00:39:56,040
Now, the beta, heck, you can just go to Yahoo Finance or the

548
00:39:56,040 --> 00:40:00,040
Standard & Poor's Global Net Advantage or any one of a

549
00:40:00,040 --> 00:40:01,040
number of other sites.

550
00:40:01,040 --> 00:40:04,040
I think Google has its own finance site.

551
00:40:04,040 --> 00:40:09,040
You can even just type in, what is the beta of TSLA?

552
00:40:09,040 --> 00:40:11,040
It will give it to you.

553
00:40:11,040 --> 00:40:14,040
So that's not hard to get.

554
00:40:14,040 --> 00:40:20,040
Okay, what about the risk-free rate?

555
00:40:20,040 --> 00:40:24,040
We, and this comes from a previous lecture,

556
00:40:24,040 --> 00:40:32,040
the risk-free rate, the best place to get a proxy.

557
00:40:32,040 --> 00:40:37,040
Now, the risk-free rate itself is a theoretical thing,

558
00:40:37,040 --> 00:40:42,040
but it has a very close approximation in real data.

559
00:40:42,040 --> 00:40:46,040
The yield on a one-year T-bill, that's about,

560
00:40:46,040 --> 00:40:52,040
a Treasury bill is riskless, or at least almost as close as you

561
00:40:52,040 --> 00:40:54,040
could get to riskless.

562
00:40:54,040 --> 00:40:57,040
So why don't we just use a one-year T-bill?

563
00:40:57,040 --> 00:41:02,040
And so I brought up the U.S. Department of Treasury par yield

564
00:41:02,040 --> 00:41:05,040
curve rates, and for the latest one,

565
00:41:05,040 --> 00:41:11,040
for a one-year T-bill, is 5.48%.

566
00:41:11,040 --> 00:41:15,040
That's how you do it.

567
00:41:15,040 --> 00:41:28,040
So that's what we'll use today, 5.48%.

568
00:41:28,040 --> 00:41:31,040
Now, the expected return on the market.

569
00:41:31,040 --> 00:41:35,040
This one is the wild card.

570
00:41:35,040 --> 00:41:43,040
What we do, there are services that take a survey of a group

571
00:41:43,040 --> 00:41:48,040
of industry and academic professionals in finance,

572
00:41:48,040 --> 00:41:53,040
and they have the members of the survey give their estimate

573
00:41:53,040 --> 00:41:57,040
what in their judgment will be the expected return to the

574
00:41:57,040 --> 00:42:00,040
world portfolio over the next year.

575
00:42:00,040 --> 00:42:04,040
There are many of these services out there.

576
00:42:04,040 --> 00:42:06,040
I'm in one of them.

577
00:42:06,040 --> 00:42:11,040
It has 70 people, people in the finance industry and in

578
00:42:11,040 --> 00:42:16,040
universities, reputable universities,

579
00:42:16,040 --> 00:42:19,040
and we each give our estimate, and then they take the average

580
00:42:19,040 --> 00:42:22,040
of the data that they get.

581
00:42:22,040 --> 00:42:25,040
And like I said, there's a group that I'm in,

582
00:42:25,040 --> 00:42:28,040
and then there's dozens and dozens of others who do the

583
00:42:28,040 --> 00:42:30,040
same thing.

584
00:42:30,040 --> 00:42:36,040
For the most part, those estimates are usually really

585
00:42:36,040 --> 00:42:42,040
close, in the same, to a tenth of a decimal place away from

586
00:42:42,040 --> 00:42:43,040
each other.

587
00:42:43,040 --> 00:42:45,040
Once in a while you see some deviation,

588
00:42:45,040 --> 00:42:47,040
but you pay your penny and you take your pick.

589
00:42:47,040 --> 00:42:49,040
This is the way it goes.

590
00:42:49,040 --> 00:42:59,040
Right now, here's one.

591
00:42:59,040 --> 00:43:04,040
11.85%.

592
00:43:04,040 --> 00:43:07,040
Now, one thing I would caution, if you try to do something like

593
00:43:07,040 --> 00:43:12,040
a Google, what is the expected return to the market portfolio,

594
00:43:12,040 --> 00:43:16,040
most of the hits you'll get are giving you a little explanation

595
00:43:16,040 --> 00:43:19,040
of what it is instead of giving you a hard number.

596
00:43:19,040 --> 00:43:21,040
The reason is simple.

597
00:43:21,040 --> 00:43:26,040
The hard numbers are done by services that charge you for it,

598
00:43:26,040 --> 00:43:29,040
and so they don't let Google find those numbers and then just

599
00:43:29,040 --> 00:43:32,040
say, oh, well, here it is.

600
00:43:32,040 --> 00:43:35,040
And we know because we charge for that service.

601
00:43:35,040 --> 00:43:41,040
But overall, that's somewhere in the right ballpark right now.

602
00:43:41,040 --> 00:43:44,040
But, okay, you've got all the pieces of it.

603
00:43:44,040 --> 00:43:48,040
Now, let's do the wild thing.

604
00:43:48,040 --> 00:43:52,040
Okay.

605
00:43:52,040 --> 00:43:55,040
Let's go back here to Yee-haw Finance.

606
00:43:55,040 --> 00:44:00,040
Let's take a stock.

607
00:44:00,040 --> 00:44:05,040
Let's go to Flix and Chill.

608
00:44:05,040 --> 00:44:09,040
Beta is 1.26.

609
00:44:09,040 --> 00:44:12,040
Now, I want to point out something.

610
00:44:12,040 --> 00:44:16,040
I'm going to write this here before I forget.

611
00:44:16,040 --> 00:44:21,040
The beta of NFLX is 1.26.

612
00:44:21,040 --> 00:44:25,040
Now, before I go any further, you see this little piece right

613
00:44:25,040 --> 00:44:29,040
here in the cap M, the expected return to the market portfolio

614
00:44:29,040 --> 00:44:31,040
is the highest risk-free rate.

615
00:44:31,040 --> 00:44:43,040
We call that the market premium over risk-free.

616
00:44:43,040 --> 00:44:51,040
Mostly you'll hear me say the market premium.

617
00:44:51,040 --> 00:44:56,040
It's the extra reward for taking the risk of the market

618
00:44:56,040 --> 00:44:59,040
going riskless.

619
00:44:59,040 --> 00:45:01,040
Let me explain.

620
00:45:01,040 --> 00:45:05,040
You.

621
00:45:05,040 --> 00:45:07,040
I'll tell you a story.

622
00:45:07,040 --> 00:45:11,040
One night when I was a teenager, I had all this angst

623
00:45:11,040 --> 00:45:14,040
and thoughts about my life in the future.

624
00:45:14,040 --> 00:45:18,040
So I climbed out on the roof of my house from my bedroom window

625
00:45:18,040 --> 00:45:22,040
and I just yelled to this guy, why am I here?

626
00:45:22,040 --> 00:45:25,040
This old guy across the street leans out his window and says,

627
00:45:25,040 --> 00:45:28,040
because you climbed out there, you moron.

628
00:45:28,040 --> 00:45:32,040
Now I heard his wife say, Harold, come to bed.

629
00:45:32,040 --> 00:45:34,040
And then a dog barked.

630
00:45:34,040 --> 00:45:40,040
That was my first introduction to existentialism.

631
00:45:40,040 --> 00:45:45,040
But anyway, you are here on an expectation that you will get

632
00:45:45,040 --> 00:45:48,040
a degree and you will get a good job and a good salary.

633
00:45:48,040 --> 00:45:51,040
That's one of the reasons that we do this.

634
00:45:51,040 --> 00:45:53,040
We pursue this life.

635
00:45:53,040 --> 00:45:59,040
You could have come out of high school and gotten a $12 an hour job.

636
00:45:59,040 --> 00:46:02,040
But your expectation is that you'll come out of college

637
00:46:02,040 --> 00:46:06,040
and you'll be able to get maybe a $50 an hour job.

638
00:46:06,040 --> 00:46:13,040
50 minus 12 is the market premium over risk-free life.

639
00:46:13,040 --> 00:46:15,040
That's exactly what this is.

640
00:46:15,040 --> 00:46:20,040
It's just what's the extra reward for going long

641
00:46:20,040 --> 00:46:23,040
instead of just playing the safe game.

642
00:46:23,040 --> 00:46:26,040
That's what the expected return of the market portfolio

643
00:46:26,040 --> 00:46:28,040
minus the risk-free rate is.

644
00:46:28,040 --> 00:46:29,040
And I'll show you here.

645
00:46:29,040 --> 00:46:31,040
We're going to look at the expected return to Netflix

646
00:46:31,040 --> 00:46:34,040
over the next year.

647
00:46:34,040 --> 00:46:46,040
The expected return to Netflix, NFLX, is equal to the risk-free rate

648
00:46:46,040 --> 00:46:54,040
5.48% plus the beta of the stock, 1.26,

649
00:46:54,040 --> 00:47:08,040
times 11.85% minus 5.48%.

650
00:47:08,040 --> 00:47:09,040
That's all there is to it.

651
00:47:09,040 --> 00:47:11,040
That's cap M.

652
00:47:11,040 --> 00:47:14,040
Now, I'm going to do something here.

653
00:47:14,040 --> 00:47:18,040
Let me take 11.85 minus-I'm going to do that market premium

654
00:47:18,040 --> 00:47:22,040
to show you something, to point out something.

655
00:47:22,040 --> 00:47:29,040
Let me pull up a calculator here.

656
00:47:29,040 --> 00:47:41,040
11.85 plus 5.48, 6.37.

657
00:47:41,040 --> 00:47:43,040
So I'm going to write this step.

658
00:47:43,040 --> 00:47:46,040
There's a reason why I'm doing this.

659
00:47:46,040 --> 00:47:49,040
I mean, you can just put this into a calculator

660
00:47:49,040 --> 00:47:51,040
and quickly get it.

661
00:47:51,040 --> 00:48:04,040
So we've got 5.48% plus 1.26 times 6.37%.

662
00:48:04,040 --> 00:48:08,040
And this is where you see what beta really is.

663
00:48:08,040 --> 00:48:10,040
Beta is a magnifier.

664
00:48:10,040 --> 00:48:11,040
That's all it is.

665
00:48:11,040 --> 00:48:17,040
It says how much the market premium is magnified by this stock.

666
00:48:17,040 --> 00:48:29,040
Notice the stocks above 1 will expand the market premium.

667
00:48:29,040 --> 00:48:35,040
Stocks that are below 1 will detract from it.

668
00:48:35,040 --> 00:48:37,040
I felt something fall off at my age,

669
00:48:37,040 --> 00:48:41,040
when you start feeling things fall off your belt.

670
00:48:41,040 --> 00:48:45,040
God, was that my spleen?

671
00:48:45,040 --> 00:48:50,040
There, get my recorder back in its pouch.

672
00:48:50,040 --> 00:48:53,040
There we go, good.

673
00:48:53,040 --> 00:48:54,040
That's all beta is.

674
00:48:54,040 --> 00:48:57,040
Beta is just a magnifier or a demagnifier.

675
00:48:57,040 --> 00:49:03,040
So a stock with a beta below 1 would demagnify the market premium.

676
00:49:03,040 --> 00:49:06,040
A stock above 1 will magnify it.

677
00:49:06,040 --> 00:49:12,040
And as you can see, Netflix above 1 magnifies the market premium.

678
00:49:12,040 --> 00:49:17,040
So in this case, finishing this up,

679
00:49:17,040 --> 00:49:21,040
first I'm going to now take the 6.37%

680
00:49:21,040 --> 00:49:33,040
and I'm going to multiply it by the 1.26 beta of Netflix, 8.0262.

681
00:49:33,040 --> 00:49:35,040
Now the thing that I want to bring up here,

682
00:49:35,040 --> 00:49:40,040
where's that risk-free rate sitting out there on its own?

683
00:49:40,040 --> 00:49:43,040
That's pretty easy to explain.

684
00:49:43,040 --> 00:49:52,040
Any investment with risk should at least pay you what you would get with no risk.

685
00:49:52,040 --> 00:49:57,040
That's why you add that 5.48, the risk-free rate on,

686
00:49:57,040 --> 00:50:01,040
because anything you invest in has darn well better pay you

687
00:50:01,040 --> 00:50:08,040
at least what you'd get if you didn't take the risk of a real investment.

688
00:50:08,040 --> 00:50:36,040
So when I add that in there, that 5.48, I get 13.5062%.

689
00:50:36,040 --> 00:50:45,040
That is our best estimate of your expected return for a one-year holding period.

690
00:50:45,040 --> 00:50:50,040
That's our best estimate.

691
00:50:50,040 --> 00:50:53,040
Will that be the holding period return?

692
00:50:53,040 --> 00:50:55,040
Probably not.

693
00:50:55,040 --> 00:50:58,040
I mean, it could be a little above that, a little below that.

694
00:50:58,040 --> 00:51:03,040
But we know from data that we've collected over and over again

695
00:51:03,040 --> 00:51:13,040
that on average, cap M is damn good at it.

696
00:51:13,040 --> 00:51:16,040
Now let me do one more here just to show you.

697
00:51:16,040 --> 00:51:23,040
I'm going to find a stock that would have a beta below one.

698
00:51:23,040 --> 00:51:33,040
Let's take, I wonder if Johnson & Johnson, I can't remember.

699
00:51:33,040 --> 00:51:38,040
Yeah, Johnson & Johnson has a.57 beta.

700
00:51:38,040 --> 00:51:47,040
So let me throw Johnson & Johnson in there.

701
00:51:47,040 --> 00:51:55,040
So the beta of J&J is, what did I say that was?

702
00:51:55,040 --> 00:51:57,040
.57, 0.57.

703
00:51:57,040 --> 00:52:02,040
So this beta will demagnify the market premium.

704
00:52:02,040 --> 00:52:08,040
So I run it again, the expected return for a one-year hold on Johnson & Johnson,

705
00:52:08,040 --> 00:52:25,040
J&J would be risk-free rate, 5.48%, plus the beta of Johnson & Johnson,

706
00:52:25,040 --> 00:52:32,040
0.57 times market premium over risk-free,

707
00:52:32,040 --> 00:52:40,040
which would be the same thing it was before for a given economic regime,

708
00:52:40,040 --> 00:52:46,040
minus the 5.48%.

709
00:52:46,040 --> 00:52:59,040
So we get the 5.48% plus 0.57 times that number I calculated there, 6.37.

710
00:52:59,040 --> 00:53:03,040
See those numbers in there are the same.

711
00:53:03,040 --> 00:53:09,040
And so you see what's happening again in this case,

712
00:53:09,040 --> 00:53:14,040
the beta is demagnifying the market premium.

713
00:53:14,040 --> 00:53:16,040
That's all beta does.

714
00:53:16,040 --> 00:53:18,040
It's not some magical thing.

715
00:53:18,040 --> 00:53:21,040
It's just a multiplier.

716
00:53:21,040 --> 00:53:31,040
And if I crank that one through just very quickly,

717
00:53:31,040 --> 00:53:53,040
5.48 plus 0.57 times, open parenthesis, 6.37, close the parenthesis,

718
00:53:53,040 --> 00:54:05,040
equals 9.1109.

719
00:54:05,040 --> 00:54:09,040
Look at that.

720
00:54:09,040 --> 00:54:16,040
Stocks with betas above one will pay more than the market premium.

721
00:54:16,040 --> 00:54:21,040
Stocks below one will pay less than it.

722
00:54:21,040 --> 00:54:29,040
If I put in a beta of one, surprise, you would get the 11.85.

723
00:54:29,040 --> 00:54:30,040
That's how it works.

724
00:54:30,040 --> 00:54:34,040
Beta is, and I don't know if you see it yet,

725
00:54:34,040 --> 00:54:38,040
but it is actually elegantly self-contained.

726
00:54:38,040 --> 00:54:41,040
It's self-explaining.

727
00:54:41,040 --> 00:54:43,040
And this is actually what we use.

728
00:54:43,040 --> 00:54:46,040
Now we can do little other things.

729
00:54:46,040 --> 00:54:48,040
I'll show you later.

730
00:54:48,040 --> 00:54:55,040
But if you got a beta for a stock, this is the, or a beta for your portfolio,

731
00:54:55,040 --> 00:55:03,040
this is the best way, best practices for a forecast of what you should expect to make.

732
00:55:03,040 --> 00:55:07,040
Take stocks with higher betas, you get a higher expected return.

733
00:55:07,040 --> 00:55:12,040
Take stocks with lower betas, you get a lower expected return.

734
00:55:12,040 --> 00:55:16,040
And the risk level is commensurate with it.

735
00:55:16,040 --> 00:55:19,040
The beta is telling you, you're going to take risk with this,

736
00:55:19,040 --> 00:55:21,040
so you expect a higher return.

737
00:55:21,040 --> 00:55:22,040
Duh.

738
00:55:22,040 --> 00:55:26,040
You don't take as much risk, then you should expect a lower return.

739
00:55:26,040 --> 00:55:29,040
That's all beta does.

740
00:55:29,040 --> 00:55:32,040
Now that graph that I did over there,

741
00:55:32,040 --> 00:55:37,040
that's just the capital asset pricing model graphed out.

742
00:55:37,040 --> 00:55:39,040
We even have a name for that line.

743
00:55:39,040 --> 00:55:51,040
It's called the securities market line.

744
00:55:51,040 --> 00:55:53,040
That's where it is.

745
00:55:53,040 --> 00:55:58,040
So in other words, I could, if I had this graph done really well,

746
00:55:58,040 --> 00:56:04,040
I could say, oh, a beta of 1.26, there would be its,

747
00:56:04,040 --> 00:56:11,040
this would be the 5.48 here on the graph, the R sub F, the Y intercept,

748
00:56:11,040 --> 00:56:17,040
and this would be the 11.85% expected return in the market portfolio.

749
00:56:17,040 --> 00:56:24,040
You tell me 1.26, I could run up to the line and I could say, oh, there it is on the graph.

750
00:56:24,040 --> 00:56:31,040
The 0.57, there that is, take it up to the line, there's that one.

751
00:56:31,040 --> 00:56:37,040
It's just like, it's just plain old high school linear algebra is all it is.

752
00:56:37,040 --> 00:56:40,040
A two-point formula of a line.

753
00:56:40,040 --> 00:56:48,040
And that's the whole story of the capital asset pricing model.

754
00:56:48,040 --> 00:56:53,040
One, okay, two last points here.

755
00:56:53,040 --> 00:57:00,040
I almost forgot there's another measure of risk too, but I'll get to that in a second.

756
00:57:00,040 --> 00:57:06,040
We collect data all the time on stocks, on portfolios,

757
00:57:06,040 --> 00:57:12,040
and see what CAPM says will happen and see what really happens.

758
00:57:12,040 --> 00:57:21,040
For the most part, the data is really nicely clustered right around the securities market line.

759
00:57:21,040 --> 00:57:26,040
It's not going to be perfect because the risk-free rate changes, obviously,

760
00:57:26,040 --> 00:57:31,040
and the expected return to the market portfolio changes and all that kind of stuff.

761
00:57:31,040 --> 00:57:34,040
But it stays very close to the line.

762
00:57:34,040 --> 00:57:48,040
However, once in a blue moon, we find a stock or some investor or fund manager's portfolio

763
00:57:48,040 --> 00:57:59,040
that has a certain beta, and it's way above the securities market line.

764
00:57:59,040 --> 00:58:04,040
In other words, it's temporarily blowing theory.

765
00:58:04,040 --> 00:58:08,040
We actually look for this.

766
00:58:08,040 --> 00:58:14,040
See this portfolio right here, whatever it is, maybe a bay of 1.10,

767
00:58:14,040 --> 00:58:25,040
it should have this return, but instead it has an abnormally higher return.

768
00:58:25,040 --> 00:58:26,040
It happens.

769
00:58:26,040 --> 00:58:30,040
It's not frequent that it happens, but it does happen.

770
00:58:30,040 --> 00:58:34,040
We have a name for that.

771
00:58:34,040 --> 00:58:44,040
Whatever that number is, we call it that unusual extra.

772
00:58:44,040 --> 00:58:57,040
We call it Jensen's alpha.

773
00:58:57,040 --> 00:59:05,040
Believe me, there are algorithms running night and day looking for Jensen's alphas, positive.

774
00:59:05,040 --> 00:59:07,040
Now, you could have a negative one.

775
00:59:07,040 --> 00:59:08,040
Who cares about that?

776
00:59:08,040 --> 00:59:14,040
That's just a bad investor or a bad stock.

777
00:59:14,040 --> 00:59:20,040
Now, if you've got a Jensen's alpha of 0.2%, who cares?

778
00:59:20,040 --> 00:59:25,040
We're looking for those Jensen's alphas that are like a couple percent

779
00:59:25,040 --> 00:59:33,040
because those tell us something is interesting about the stock or about this investor.

780
00:59:33,040 --> 00:59:41,040
In fact, one of the few really reputable websites for investment advice

781
00:59:41,040 --> 00:59:47,040
and really good explanations has the name.

782
00:59:47,040 --> 00:59:50,040
It's called Seeking Alpha.

783
00:59:50,040 --> 00:59:55,040
If you're an insider, which you sort of are now, you get it.

784
00:59:55,040 --> 01:00:00,040
We're always looking for the Jensen's alpha.

785
01:00:00,040 --> 01:00:09,040
Trying to find stocks, portfolios, fund managers that have positive Jensen's alphas in their portfolios.

786
01:00:09,040 --> 01:00:12,040
Now, that's one of those great ridiculous things.

787
01:00:12,040 --> 01:00:17,040
You've got all these awards for, well, this fund manager got the highest return of the year.

788
01:00:17,040 --> 01:00:19,040
Isn't he a great son of a bitch?

789
01:00:19,040 --> 01:00:21,040
Oh, who cares?

790
01:00:21,040 --> 01:00:26,040
You can get any portfolio return you want by taking a high enough beta.

791
01:00:26,040 --> 01:00:31,040
What really matters is you might not have even made that much money,

792
01:00:31,040 --> 01:00:37,040
but did you have a Jensen's alpha that was well, that was decently positive?

793
01:00:37,040 --> 01:00:39,040
That's impressive.

794
01:00:39,040 --> 01:00:45,040
That's when you should get an award, not for just getting the highest return of all the funds.

795
01:00:45,040 --> 01:00:51,040
You can do that, you know, that securities market line goes to betas of 5, 10.

796
01:00:51,040 --> 01:00:52,040
Yeah, who cares?

797
01:00:52,040 --> 01:00:55,040
You took a huge risk and you got a huge return.

798
01:00:55,040 --> 01:00:56,040
Good for you.

799
01:00:56,040 --> 01:00:57,040
Give yourself a cookie.

800
01:00:57,040 --> 01:01:00,040
What we really want to know is did you have magic?

801
01:01:00,040 --> 01:01:06,040
And believe me, the houses all the time, they're looking, you know, you get your,

802
01:01:06,040 --> 01:01:10,040
you get your, well, free stock trades at our brokerage house.

803
01:01:10,040 --> 01:01:13,040
Aren't we wonderful sons of bitches?

804
01:01:13,040 --> 01:01:19,040
Do you think they're not looking for the investors who pulled positive Jensen's alphas?

805
01:01:19,040 --> 01:01:20,040
They're looking for them.

806
01:01:20,040 --> 01:01:21,040
We all are.

807
01:01:21,040 --> 01:01:28,040
And if one of you was, you know, if I found out that one of you had a consistently positive Jensen's alpha,

808
01:01:28,040 --> 01:01:32,040
well, you and I would be buddies.

809
01:01:32,040 --> 01:01:34,040
So that's important.

810
01:01:34,040 --> 01:01:36,040
Just keep that in mind, okay?

811
01:01:36,040 --> 01:01:37,040
What about insider trading?

812
01:01:37,040 --> 01:01:39,040
Is that also like, searching for that as well?

813
01:01:39,040 --> 01:01:42,040
Insider trading, we don't talk about insider trading.

814
01:01:42,040 --> 01:01:45,040
It never happens, ever.

815
01:01:45,040 --> 01:01:52,040
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

816
01:01:52,040 --> 01:01:56,040
and then I'd have someone would pop a cap in my ass.

817
01:01:56,040 --> 01:01:59,040
So it doesn't happen, okay?

818
01:01:59,040 --> 01:02:01,040
It's sort of like aliens.

819
01:02:01,040 --> 01:02:09,040
If I were slurped up by an alien mothership and they, and when I came back, would I tell people?

820
01:02:09,040 --> 01:02:14,040
No, because they'd all think I'm a crazy mofo, which I am, but I don't want people to believe that.

821
01:02:14,040 --> 01:02:21,040
Besides, their captain of that mothership, Zork, he said if I told anyone what had happened in that ship,

822
01:02:21,040 --> 01:02:25,040
he would personally use his phaser on me.

823
01:02:25,040 --> 01:02:33,040
So what happened in that mothership stays in that mothership.

824
01:02:33,040 --> 01:02:34,040
One last thing.

825
01:02:34,040 --> 01:02:38,040
I forgot one measure of risk.

826
01:02:38,040 --> 01:02:41,040
The Sharpe ratio.

827
01:02:41,040 --> 01:02:51,040
Let me show it to you real quick here.

828
01:02:51,040 --> 01:03:03,040
The Sharpe ratio is kind of a low brow measure.

829
01:03:03,040 --> 01:03:04,040
And you can calculate.

830
01:03:04,040 --> 01:03:08,040
All it says is what was the return to the stock minus risk-free rate?

831
01:03:08,040 --> 01:03:16,040
In other words, what was this stock's premium divided by the standard deviation of the stock?

832
01:03:16,040 --> 01:03:18,040
Here's the thing about it, though.

833
01:03:18,040 --> 01:03:26,040
What we hate about it is that it uses standard deviation, which we really don't want to use.

834
01:03:26,040 --> 01:03:31,040
We want to use only the part of the risk that cannot be removed.

835
01:03:31,040 --> 01:03:37,040
All we want, so the question is why would you use sigma instead of beta?

836
01:03:37,040 --> 01:03:40,040
Well, here's the logic of it.

837
01:03:40,040 --> 01:03:47,040
Suppose that you had the risk-free rate, we'll just keep it at 5.48%.

838
01:03:47,040 --> 01:04:04,040
Now let's say we have stock one with a standard deviation of let's say 3.00%, just to make it simple.

839
01:04:04,040 --> 01:04:06,040
Okay?

840
01:04:06,040 --> 01:04:15,040
And the return to stock one is 8.50%.

841
01:04:15,040 --> 01:04:31,040
So Sharpe would be the return to the stock 8.50 less 5.48% divided by 3.00%.

842
01:04:31,040 --> 01:04:33,040
Now watch this.

843
01:04:33,040 --> 01:04:39,040
I'm going to run that one for you and see what happens.

844
01:04:39,040 --> 01:04:52,040
So I would have the return 8.5 minus 5.48 equals and then divided by 3%.

845
01:04:52,040 --> 01:05:03,040
You get 1.0067.

846
01:05:03,040 --> 01:05:17,040
Now suppose that we have another stock which has a return to stock two of let's say some lower amount,

847
01:05:17,040 --> 01:05:23,040
let's say 7.10%.

848
01:05:23,040 --> 01:05:31,040
Standard deviation of stock two is let's say 4.2%.

849
01:05:31,040 --> 01:05:36,040
And the risk-free rate is still 5.48%.

850
01:05:36,040 --> 01:05:43,040
Let me run the Sharpe again and see what happens.

851
01:05:43,040 --> 01:06:04,040
The Sharpe again in this case would be 7.1 minus 5.48 divided by standard deviation of 4.2, 0.3857.

852
01:06:13,040 --> 01:06:24,040
The stock dominates by the Sharpe ratio.

853
01:06:24,040 --> 01:06:31,040
Scaled by the standard deviation, each one scaled by a standard deviation.

854
01:06:31,040 --> 01:06:35,040
That first one is giving you more bang for the buck.

855
01:06:35,040 --> 01:06:37,040
Now here's another part of it.

856
01:06:37,040 --> 01:06:40,040
Why are they using sigma instead of beta?

857
01:06:40,040 --> 01:06:51,040
Because a normal investor doesn't have a well-diversified portfolio of 30, 50 fancy stocks.

858
01:06:51,040 --> 01:06:54,040
They're riding a thin portfolio.

859
01:06:54,040 --> 01:06:59,040
So Sharpe, the whole standard deviation, not just a beta,

860
01:06:59,040 --> 01:07:05,040
is the correct way to judge performance of their portfolios.

861
01:07:05,040 --> 01:07:09,040
And another thing, finding the Sharpe ratio.

862
01:07:09,040 --> 01:07:13,040
You notice with these stocks that I've shown you so far this semester,

863
01:07:13,040 --> 01:07:18,040
you don't see the Sharpe ratio, you see the beta.

864
01:07:18,040 --> 01:07:26,040
If I showed you mutual funds, almost every one of them, they report Sharpe.

865
01:07:26,040 --> 01:07:31,040
Because mutual funds can tend to be thin portfolios.

866
01:07:31,040 --> 01:07:37,040
And so the Sharpe is how you compare the performance of the portfolios.

867
01:07:37,040 --> 01:07:43,040
And the mutual funds report Sharpe ratio is.

868
01:07:43,040 --> 01:07:51,040
And it is actually more useful for funds like mutual funds than the beta is.

869
01:07:51,040 --> 01:07:54,040
There's no cap M or anything like this.

870
01:07:54,040 --> 01:08:02,040
All it does is say what is relatively better than what else.

871
01:08:02,040 --> 01:08:04,040
That's all Sharpe can do for you.

872
01:08:04,040 --> 01:08:08,040
Well, I shouldn't say that. There are people who do really fancy things with it.

873
01:08:08,040 --> 01:08:15,040
But it's out there and I can't criticize it because honestly for a thin portfolio,

874
01:08:15,040 --> 01:08:21,040
beta means nothing because beta is risk in a well-diversified portfolio.

875
01:08:21,040 --> 01:08:24,040
But if you're sitting there with one, two, three stocks,

876
01:08:24,040 --> 01:08:27,040
then probably you're eating the whole sigma.

877
01:08:27,040 --> 01:08:31,040
So you might as well use a metric that uses the whole sigma

878
01:08:31,040 --> 01:08:35,040
instead of that piece of it that's beta.

879
01:08:35,040 --> 01:09:02,040
That's all I have for you today. I thank you.