Finance 527: Lecture 27, Market Efficiency V2

Finance 527: Lecture 27, Market Efficiency V2 [John Nofsinger]: Welcome to the second video for the efficient markets topic. This is gonna be sort of a real life demonstration about how you can kind of trick yourself into thinking that some data mining association that you ve found-some investment strategy-is a good one is gonna continue to work. So I call this from the dogs of Dow to the motley fools. Now there was a book that came out and it was called Dogs of the Dow. And it was an investment strategy where you just use the Dow Jones Industrial average stocks so the 30 stocks of the Dow. And at the end of the year, you rank order them by their dividend yield. And you simply buy the 10 out of 30 stocks and have the highest dividend yield. Now there is some sort of underlying investment overall strategy that is this is a value-oriented strategy, right? The reason that they have the highest dividends is possibly the prices have gone down or the dividends have gone up and so you have a good relative evaluation there. And what they find is that if you have done that instead of owning the 30 stocks of the Dow, you d own just those 10. You pick new ones every year. Though you would have actually beat the market or beat the overall Dow by 4% a year. Now this is a very high number to be able to beat the Dow by 4% a year. Well then some other people came along and said well you know you could even better than that. They go back and look at the prices of everything and they say now let s rank order. First we re gonna find the dogs. We re gonna find those ten stocks with the highest dividends. Then, of those ten stocks, we re gonna rank order them by price. Right, and we re gonna pick the 5 lowest priced stocks. And those are the five stocks we re going to buy. If you do that, you would have beat the market by 8%. Now we re getting to the point where it s hard to say that there s any basis in theory for something like this because lowest prices mean very little. Right a company could do a reverse stocks split and change its stock from 10 dollars a share to 50 dollars a share where they reverse stock split. Or it could go the other way from a high price to a low price. So the actual price isn t very relevant. Relative price is relevant, right. Price to earnings, price to market-i mean price to book value or price to dividends as seen above. And also now look we re not having a very diverse portfolio with only five stocks. Then come along the Motley Fools, and they re a fun couple of brothers and they operate fool.com. And they actually do a lot of good things, but I m gonna make fun of their early work here. They came along and they said they came up with the foolish four. They found the Dogs of the Dow. Then they found the Dow Five. Right and then they said instead of buying all five stocks. Remember we just pick the ones five with the lowest price. They say let s actually throw out the one with the really lowest price. And put that money on the one with the second lowest price. So we divide up our money evenly between the fifth lowest price, the fourth, the third, and then the second, we double the amount of money on that. So we only own four stocks with forty percent of our money in one of them. K if they do that, they said you would have earned 12% a year over and above the Dow. So this is what they were telling a bunch of things like this that they re telling everybody to do. Well it s kind of funny what actually happened was once there was a number of people following this, other investors said look-i know exactly what four stocks all these people are going to buy January 1 st or whenever the market opens, the first day of the year. I know what stocks those are gonna be. 1

Figure that out in advance. So they would go and they would buy them the day before, and all their buying would actually push up the price a little bit. Then the Motley Fool followers would get in and they would start buying and push up the price more. And the original investors would sell to the fools, and they would make a nice profit based on knowing what the fools are gonna do. And of course after everyone started doing this procedure, it failed and so the Motley Fool abandoned the strategy. And now the Motely Fools are all about stuff like diversification and things like that. Market Efficiency V2 John Nofsinger From Dogs to Fools Dogs of the Dow o Strategy identifies the 10 highest-dividend paying firms in the DJIA 1992 book, Beating the Dow by O Higgins and Downes Buying the Dogs at the beginning of each year is shown to beat the buy-and-hold strategy of just owning the 30 stocks in the Dow by over 4% per year Value-oriented strategy Dow Five o Buy the 5 lowest priced dogs Beats the Dow by 8% per year No basis in theory Motley Fool Adaptation o Foolish four: of the Dow Five, throw-out the lowest price stock and double-up on the second lowest price stock o Purported to beat the Dow by 12% per year o Motley Fool eventually abandoned strategy when it failed [John Nofsinger]: Alright investing in an efficient market. One of the kind of legendary stories of Wall Street is that you have two people walking down the street, and one of them sees a hundred dollar bill on the sidewalk and he points it out. And the other person says: nah that must not be a real hundred dollar bill or someone else would have picked it up. And that s really the efficient market story, right? If there are hundred dollars of bills lying around, we re gonna be looking for them. But the whole idea of market efficiency is that there are not these undervalued, easy bargains. So if there isn t any, why should we be bothered to look? And we get that circular tetralogy. So what about the evidence? Well mutual funds, on average, do not beat the S&P 500. They might on average one year or another. But over time, on average mutual funds do not. There might be some mutual funds that do one year or two years, right? I mean there is luck 2

involved if you don t have a diversified portfolio. Other like newsletter-investment newsletter writers-most of that is on blogs and emails now. But they tend not to beat-they don t beat the market on average either in their decisions. So that s evidence for the efficient market hypothesis. If markets are efficient-right-what kind of investor activities should you do? So if markets are weak form efficient that means things like technical analysis is not going to work. It will work if markets are not efficient potentially. But it should not work if markets are weak form efficient. If markets are semistrong form efficient, then fundamental analysis where you only use publically available information should not work. That is you should not be able to systematically find undervalued stocks using fundamental analysis if markets are at least semistrong form efficient. And of course if markets are strong form efficient, then none of these activities would work to be able to systematically find deals. So you know if it s not efficient, you can do either one. If it s weak form efficient, you can do fundamental analysis. If it s at least semistrong or higher, you can t really even do that. What would you do? Well you would work on portfolio design. Right, design good, efficient portfolios where you get the highest return possible for the lowest level of risk. And you would work on things like asset allocation-how much would be in stocks, how much of your money in bonds, etcetera. Investing in an Efficient Market Two investors are walking down the street when one spots a $100 bill on the sidewalk. He points it out. The companion says, It must not be a real $100 bill or someone would have already picked it up. o If markets are efficient, we should not bother to look for bargains. However, if no one is looking for bargains, how can markets be efficient? Studies show that, on average, mutual funds and investment newsletter writers do not beat their benchmark o Evidence for EMH What should investors do if markets are efficient? [John Nofsinger]: Alright well there s plenty of evidence that markets may not be efficient as well. One markets seem awfully volatile. Dividends don t go up and down. In fact, they re very, very stable so why do prices go up and down so much? Also we see huge bubbles. We saw a Japanese stock market bubble, which I ll show a graph of here soon. We had our own tech bubble, and then we see bubbling in housing. We ve seen credit bubbles so it seems hard to say that when the stock market goes way up here that you can say: oh prices are fair. And then you know a few months later, prices are down here. And so well could you really say prices were fair there? It seems like that s hard to say. It seems like things might have been overvalued there or they re undervalued here. 3

Challenges to EMH Excessive Volatility o Why is the market so volatile? o Dividends are not as volatile as stock prices Bubbles o Japanese stock bubble o Nasdaq bubble o Housing bubbles o Credit bubbles [John Nofsinger]: This is just the information about the big Japanese stock market bubble. In general, right, it over a 5 year period, it went up 27 and a half percent on average per year. But then it crashed. It lost 72% of its value after the crash. Then we had the tech bubble. Measuring from the Nasdaq 100, you had again a really high average return for a long period of time. And then it lost about 80%. From January 2, 1985 at 11,543, the Nikkei 225 soared to a closing high of 38,916 on December 29, 1989 o This represents a gain of 237.1% in the Nikkei over a 5-year period, and a stunning 27.5% compound annual rate of return o Then, the bubble burst and the bottom fell out of the Japanese equity market o Fifteen years after the Japanese market peak, in December, 2004, the Nikkei stood at 10,796. That s 72.3% below the December, 1989 peak From a (split-adjusted) level of 125 on January 31, 1985, the Nasdaq 100 soared to 4,816.35 on March 24, 2000. o This represents a 15 ¼-year return of 3,753.3%, and an amazing compound return of 27.1% per year o Then the Nasdaq plunged, losing over 80% of its value by 2002 [John Nofsinger]: If I put the two bubbles together-even though they did not really occur here in time-as you saw the Japanese one-this one-this occurred more like the 80s and the 90s. But if I just overlap them and kind of match up their peaks, you can see that the Nasdaq bubble was much more dramatic. It went up-way up-and it went way back down. The other thing to notice is that even a couple decades later, the Japanese stock market is still you know not even half of what it was at the peak. And we got similar kinds of things with the Nasdaq 100. Right now, the Dow and the S&P 500 have reached new highs. But the Nasdaq 100 hasn t because it was so tech laden and you can see what a high bubble it was. 4

Figure 6.6 Will Post-crash Nasdaq 100 Valuations Languish for a Decade or More? [Image of graph of years vs. Nasdaq 100 and Nikkei 225] [John Nofsinger]: If there are market imperfections you know that could cause this inefficiencies, where would they come from? Well I would argue that if there are, they come from us. They come from investors. We have psychological biases. We have emotions, cognitive errors. If these are coordinated between lots of us-large groups of them-if we re all too optimistic or too pessimistic, we could call that you know investor sentiment or investment mood. Then it can impact maybe expectations of the market, and therefore our buying and selling. Causes of Market Imperfections Investors make decisions influenced by emotions and psychological biases If large groups of investors become too optimistic or pessimistic, they may move prices o Investor mood Investor mood can impact expectations [John Nofsinger]: A little model that could demonstrate that. Let s just start with a simple model that had created earlier. It is a dividend discount model. It says the price of the stock or the fundamental value of a stock today should be next year s dividend divided by k minus g. Right, this is our constant growth rate model. Okay, let s say well what if we have biases about g? Right, what if we don t know g? We have expectations that can be wrong. If we think g is too high, we get too optimistic, right. Then the denominator gets smaller so the overall stock market goes up. So if we think g is higher than it really is-we re too optimistic-prices will rise. Also if we think g is we re too pessimistic on our growth, then prices will fall as well. So that s one way to think about it. We could of course get an assessment for how much prices are going to go up and down or be over or undervalued. So if we take the expected price that we just have right here and divide it by what s true or what should be true up there. See we find that this relationship we could actually calculate what the bias is putting into the market. If prices reflect a dividend discount model: PV=D1 / (k-g) But expectations become biased: E(P) = D1 / (k-e(g)) So prices can deviate from true value by: EE(PP) PPPP = kk gg kk EE(gg) 5

[John Nofsinger]: So here s an example. The market return over long period of time is 12%, and the growth rate is 4%. Let s say the Dow is at 10,000. If we all get too optimistic and think we re gonna grow at 5% forever instead of the true long term growth rate of 4%, then as we put the numbers in here, we see that we should be the market will become 14% over-valued. That is the market will rise from 10,000 to 11,400 as our optimism causes us to buy and causes the prices to become overvalued. You could do a similar thing with pessimism of course with a lower growth rate. Example: If the annual market return over a long period of time is expected to be 12% and the long-term expected growth rate of stock market firms is 4%, the Dow Jones Industrial Average is fairly valued at 10,000. If Optimistic investors believe the long-term growth rate is 5%, how far would the DJIA be expected to fall? The stock market should become over-valued as EE(PP) PPPP = kk gg kk EE(gg) = 12 4 12 5 = 8 7 = 1.14 The DJIA would be expected to rise to 1.14 x 10,000 = 11,400, or a 14% rise. However, it would also be 14% overvalued. [John Nofsinger]: So we can see that these things matter or could matter. If they do matter, that s how we do it. Here s a cycle of investor emotions. When we re at market highs, we tend to experience thrills and euphoria. As prices go down, we start with anxiety and then we deny: oh no problem it will go right back up. At a certain point, it s obvious it s not going back up. We re desperate. We panic. Finally, we capitulate and that s why investors tend to sell here. And it takes a while for it to go back up before we start getting optimism again and excitement and we start to buy. And this is one reason that investors sometimes have a problem with buying low and selling high because we tend to do the opposite because of our emotional cycle as it syncs with the market. Figure 6.7 A cycle of Investor Emotions Throughout a Price Bubble [Image of sinusoidal curve showing price breaks and price bottoms] [John Nofsinger]: So the efficient market hypothesis. Is it accurate? If so, what level of price is efficient? Well there s lots of stuff that would support some efficiency. Short-term prices are pretty unpredictable. They do seem to adjust quickly and professional investors can t often beat the market and they do it for a living. But the market on the other hand is too volatile, bubbles exist, mood might move things. I didn t talk about investment fraud. But if prices are already correct-they re fair-then fraud shouldn t work, right? They shouldn t be able to get a lot of 6

people to buy overpriced stocks and move prices and such. That should not work, and yet there is a lot of that type of fraud especially in the penny stock area of the market. EMH The EMH is still hotly debated o In support: Short-term prices are unpredictable Price adjust quickly and pretty accurately Professional investors don t seem to beat the market, on average o Against: Market is too volatile Stock market bubbles exist Investor mood may drive prices away from fair value Investment fraud o The next chapter examine some interesting anomalies that also put the validity of the EMH into question [John Nofsinger]: So we can maybe even be a little better at testing whether the market is really efficient or not. And there s lots of different kinds of tests people try to do. If we say that there s no undervalued or overvalued stocks, then it s just a matter of what is the risk of stocks and thinking of risk premiums and all of that. So we want to determine-in order to test Efficient Market Hypothesis-that is if certain strategies will work, we need say: well what returns should have those stocks gotten given their level of risk? And so we need an Asset Pricing Model. And we ve talked about CAPM, APT, and other Multi-factor models like Fama-French Three Factor Model etcetera. So when you re testing the Efficient Market Hypothesis, one-you have to determine whether your strategy beats you know regular buy and hold investing. And you need to control for risks so that includes an Asset Pricing Model. Testing the EMH The Efficient Market Hypothesis states that stock prices are fairly valued o No under- or over- valued firms o What return is expected? Expected return depends on the risk premium Asset Pricing Models show the relation between risk and return o CAPM, APT, Multi-factor models o Do these models express the right relationship? 7

[John Nofsinger]: But the problem is that you now have a joint test that is let s say that we reject market efficiency. Is it really because markets aren t efficient or is it because the asset pricing model isn t a very good one? We re never really sure when we reject market efficiency in a test whether is it because markets really are inefficient or it s not controlling for the risks properly? I have a bad model. So we always have to keep that in mind when we are trying to test this. Alright so let s conclude the second of the three videos for the efficient market hypothesis. Joint Test Problem If a portfolio of stocks seems to earn higher returns than it should given its level of risk, is this evidence of: o Market inefficiency? Stocks were undervalued o A miss-specified asset pricing model? Risk premium not estimated correctly Practical relevance o Implications for indexing, active management, diversification, etc. 8