[MUSIC] So in this video, we're going to talk about complicated firms and potential inattention on the part of some segment of the market. And how that can be used to form potentially profitable investment strategies. Okay, so the idea is that some group of investors takes a longer time to process relevant, complicated information to value a firm, okay? But at least it's not so complicated that no one can figure it out or maybe there's just some group of people is not paying immediate attention to all the relevant information for valuing a firm, okay? So that suggests a potential profit opportunity for the group of investors that has the ability to process this complicated information. Or simply paying attention to the news immediately after it's released, okay? So ideas find things that are complicated for many. So I don't know how many of you remember this Rubik's cube, but when I was a kid is quite a phenomenon and you could buy these books. How can I get this back to all yellow on one side, all blue on another. So for a lot of people, this may be very complicated, but maybe for you, you can figure things out quite quickly. Now, for me, I was actually risk averse. So my mom bought the Rubik's cube and I actually just kept it like this because I was very afraid that if I started to monkey with it, I would never get it back to the original original condition here. So my Rubik's cube is somewhere at home exactly like this, very sad level of risk aversion here as a kid. Okay, so let's pause, think and answer, okay? What's answer have in store for us now. What type of firms do you think might be more complicated than others, right? If the firms aren't that complicated to follow, they're probably aren't great profit opportunities, right? Because as soon as any news comes out, everyone instantaneously understands what it means for the firm and stock prices adjust up or down right away, okay? So you want to look for firms that maybe not everyone can figure out what this news means for firm prospects right away, okay? If we find such firms, what's a portfolio strategy, one could construct, assuming you have an edge in processing information about these complicated firms. So let's think about this and then I'll come back and give you some examples. What type of firms are complicated? What's a portfolio strategy one could construct, assuming you have an edge in processing information about these complicated firms? Well, let's go through a few examples of some research that shows potential returns to some of these inattention or complicated firm investment strategies. So one of these is research known or titled, Does Attention Stop At Water's Edge? So what's the idea behind this? US firms increasingly have more and more of their sales overseas. So if we want to forecast how a firm is going to be doing in the future, it's not just enough to understand what's happening in the US economy. But you want to know what's happening in economies in Europe and Asia where these US firms also are doing a lot of business, okay? The question is, are investors in these US firms fully keeping track of how these overseas economies are performing and thus affecting the sales going forward of these US firms. That's the idea. So perhaps it takes a little time for overseas shocks to the economy to be fully reflected in the stock price of the US firms, okay? This is research reported by Kwak Nguyen in his 2012 paper and Huang also reports similar results in her research. So what's the portfolio strategy here? Let's get down to kind of the nitty gritty here. For each month, from 1999 to 2010, let's rank a firm by a weighted average geographic return, okay? So it turns out, what do we mean by this weighted average geographic return? Well, the weight will be the share of sales in a particular country the return is the stock market in that country, okay? So the idea is let's say Coach Purse sells all their products to Portugal, okay? So if we look at the kind of share of sales that Coach Purse has, 100% will be in Portugal. And we have data over this period, 1999 to 2010. For each firm, we know the geographic regions where they do sales, is in the US, is in Portugal, is in South Korea. So Coach Purse does their sales in Portugal, it turns out last month, Portugal stock return was minus 10%, okay? Let's say Pepsi does their sales, it's 100% in South Korea, okay? Last month, South Korea stock return was 10%, okay? So South Korea 10% return, let's say Portugal was minus 10%. So we would say, hey, going forward, good news for investors in Pepsi, the economy seems to be doing well in South Korea. Bad news for investors in Coach, the economy seems to be doing not so well in Portugal. So we calculate this weight average geographic return for each firm. And I gave you a simple example with Coke and Pepsi. Then, rank firms by this geographic return. So Pepsi, their sales are in South Korea. South Korea did well last month, so they're going to get a high ranking. Coach, their sales are in Portugal, Portugal did poorly last month, so Coach will get a low ranking in this rank firms by this geographic return. Form portfolios over the next month based on this ranking. So they, the idea would be US investors don't instantly realize that the good news in South Korea is good news for Pepsi and the bad news in Portugal is bad news for Coach. So let's see if we can earn returns over the next month by investing in Pepsi and avoiding Coach or shorting Coach. And then we're going to repeat this process month after month after month, okay? And we'll present the returns from this strategy and will report monthly returns in percentage points. We can evaluate, is there some inefficiency in the market that this international news doesn't instantaneously get reflected in US stock prices. So let's look at the results here. Does attention stop at the water's edge? Here are the returns that we're looking at for this strategy. So let's just focus on the fifth column here. And here we're looking at those firms that are in the top 20% based on this geographic return. So in the simple example I had, Pepsi would be in group five here because they come from a region South Korea whose stock market did very well last month. And here we're looking at the returns to evaluate this strategy using different benchmarks. So in row one, we're simply looking at what's the excess return of this strategy? The return of the stocks that are in this group that makes sales to regions that did well last month. In our example, Pepsi would be in this group. They're beating the treasury bill returned by 1.35% on a monthly basis over this period, 1999 to 2010. How about if we do a CAPM model analysis? What's the Alpha from that? 1.36%. Standard errors are in parentheses here. So when we see these standard errors around two or more in magnitude, we know that's a statistically significant result. We go to the three factor model, we're getting an alpha out performance at 0.9% per month, okay? Now remember 0.9% per month that's over, 10% edge points on an annual basis that's a huge, huge return. How about the firms that are in group one? So in my example that would be coach, in my example coach sales are all in Portugal, Portugal stock market did poorly last month. So we would put coach and other firms that made sales to regions whose stock market did poorly last month, they would be in group one. So we can see their returns are much lower than those firms that are in group 5, okay? So key is kind of the difference. What's the return to the firms of like Pepsi minus the return of the firms like coach? Group 5, the high these are the firms that come make sales to regions last month whose stock market's gone up a lot, minus a return from firms who make sales to region whose stock return last month did poorly. So this is the key result, group 5 minus group one regardless of the kind of risk model you use whether you simply just look at differences in returns. You use a CAPM model to control for risk, you use a fama french three factor model. You also include momentum effects or you even do this five factor model where you control for market risk, size factor, value factor, momentum effects, and liquidity of the underlying investment. All these estimates are exactly the same, there are about 1.4 to 1.5% points on a monthly basis, or about 18% point on an annual basis. In terms of this portfolio strategy where each month invest in US firms that make sale to regions whose stock market did well last month. Short firms of the US, short US firms that make sales to regions where whose stock market did poorly last month. So there does seem to be a little bit of inefficiency in the market, some strong predictability of returns. So how does a payoff from this geographic momentum strategy if you will, how does it vary with the state of the economy and other investment strategies? So we're going to focus again on this, invest in the groups who make sales to regions that did very well last month. Short the US the stocks that make sales to regions that did very poorly last month, the high minus low group. Let's see, we reported these kind of alphas are these differences in performance, they're all the same. Regardless of the risk model we use, they're all 1.4, 1.5% points per month, about 18% points on an annual basis like very lucrative strategy here. What's the R-squared of these risk return models? It's basically zero, usually you would say in R-squared of zero is very bad, but in this case I think it's actually good. And if you want to look at it a different way the p-value of the test do these models explain any of the returns of this strategy. These p-values are all very high, so the answer is no. What you know kind of quote and win was finding in his research and Wong as well is that they've uncovered a strategy whose return aren't sensitive to the state of the economy, right? The coefficient on market conditions is zero. Don't have a size or value tilt aren't related to the famous momentum strategy and aren't subject to liquidity risk. So none of these known factors that predict returns can account for the returns from this geographic strategy, so that's important to you document. How many months in the future should one be able to earn returns from this geographic-momentum strategy if you will, okay? So this kind of gets that the market might not be perfectly efficient in processing this information, but you probably don't want to send this information too long. Like it would be surprising if people haven't figured out six months from now that good news in South Korea is good news for Pepsi, and bad news in Portugal is bad news for coach. Maybe they don't figure it out right away in the first month but they probably figure it out after six months. So let's rank stocks again by their weighted-average geographic return, we do that. And then let's see how does that predict returns over the next month? We already show that results. But how does it predict returns in month two, or month three, or month four or month five? How long does this predictability last? So here's the results looking at ranked firms by how their region where they make sales did last month. These are the estimates we already presented. Looking at the group that comes from re that make sales to regions that did well last month, short those that come that make sales to regions that did poorly last month. This return over the next month is differential return 1.4 to 1.5% point difference in returns next month. How about if you look at the second month, the third month, the fourth month, the fifth month or the six month, let's just focus on the fama french three factor alpha. All these alphas are zero, so there is no longer any return predictability once you get past the first month, okay? So it makes the market's pretty efficient processing this information, it just doesn't do it right away. But if you wait more than a month too late, investors have figured it out prices have adjusted. How about if instead of using the stock market return, maybe you don't think that's the best measure to get a sense of the customers buying power and all these different countries. Like South Korea for Pepsi, Portugal for coach using my example. What if instead you look at GDP growth? So gross domestic product, that's report did on a quarterly basis. So instead of looking at the stock market return each month every quarter, we could look at the GDP growth of the different countries where a firm has sales. And then ranked firms by their weighted average of what's the GDP growth in the regions where they make sales, and it's simply weighted by how many sales do you have in that region? So if you do a lot of sales in one region, we're going to give a high weight to that region's GDP growth. So then we do this four times a years as opposed to 12 times a year ranking firms by their GDP growth over the past quarter. The regions where they do sales, does that predict future returns, okay? The answer is yes, it still does. So these coefficient estimates here are again, are focusing on the difference between the top quintile and bottom quintile. Top quintile firms, those are firm US firms that make sales to regions that had the highest GDP growth over the last quarter. The bottom group are those that make, US firms that make sales to regions have the smallest GDP growth over the last quarter. What do we see going forward on a monthly basis? We're getting excess, that we're getting risk adjusted returns 0.6 to 0.9% on this measure here. So these returns are lower than what we had when we do the monthly adjustment with stock returns when we form portfolios on a monthly basis. With stock returns here, we just informed portfolios four times per year. Once we have the different GDP growth, these returns still are almost 10% points on an annual basis. They're lower, but we're making less frequent adjustments to the portfolios, so lower transactions costs, okay? So kind of the bottom line whether you rank. Firms buy the stock market performance in the regions where they do sales or the economic growth. The GDP growth in the regions you have sales. You still have predictability. The market doesn't seem to fully incorporate immediately good news, good news and overseas markets and what it means for US firms. So another example of the valuation of complicated firms, kind of very similar in spirit. Can one use the performance of easy to analyze firms to predict the performance of their more complicated peers. This is research by Lauren Cohen and **** Lou. They're basically documenting that you can predict the return of conglomerate firms, firms that have multiple divisions by paying attention to the performance of standalone firms. Okay, so let's look at their results here. They're kind of using a similar methodology. Let's look at a firm and they have four divisions. So last month, let's see how the standalone firms did in all four of those divisions. So if your division, and let's make it simpler since I don't want to do, for example, let's just say they have two examples. I have a railroad and I sell soft drinks. Okay, so let's last month, let's look at the return of the railroad industry. Let's look at the return of soft drink firms, and let's see if these returns of your standalone competitors predict your return next month. Okay, so under efficient markets, if good news happens to soft drinks, that should be good news for me as well because part of my business is soft drinks. There shouldn't be any predictability. The fact soft drink industry did well last month shouldn't predict me doing well this month. I should have done well last month as well. But if it takes a little while for people to process the news, hey, good news for soft drinks isn't only good news for Coke and Pepsi, it's good news for me as well as maybe 40% of my business, but that's still good news for me. If it takes a little while for people to figure that out, maybe there can be some predictability, right? Good news for Coke and Pepsi last month, predicts kind of good things happening to my stock price this month. And this is what Cohen and Lou look at. So let's look at the kind of results here. So first, let's wait firms each month, let's categorize firms each month by what was the performance of their standalone competitors. So group 1, these are firms that their standalone competitors did very well. That predicts me doing very well next month. If I look at a kappa model, I'll perform my benchmark by 0.7% next month in a three factor model, I outperformed by 0.5% next month with T statistics here in parentheses. So statistically significant results. So the fact my stay standalone competitors did well last month, predicts that I do well in terms of higher stock returns next month. How about those groups? Group 1, where my standalone competitors did poorly last month. Well that predicts me underperforming my benchmark next month in the cabin model, I unperformed by 0.5%, in the three factor model, I outperformed by 0.7%. So the fact Coke and Pepsi did poorly last month, that predicts my stock return doesn't go down last month or doesn't fully go down last month. It goes down the next month because it takes a little while for people to process, hey, bad news about soft drink also affects Scott's firm because he's 40% in the soft drink industry. It takes a little while for the market to process that. And then the key is the final roll, which is long short where we're long, we're investing in the stocks who stand alone competitors did well last month were shorting the stocks or the standalone competitors did poor last month. We see here these risk adjusted returns kind of similar to the, does the water stop, does the, kind of news stop at the water's edge paper, the geographic momentum paper having 1.2% monthly returns to the strategy about 15% on an annual basis. So quite impressive here. So conglomerates, firms with sales overseas. Both seem to be complicated firms where the information isn't kind of fully efficiently processed in real time, and then here is just looking at these returns to the strategy. Whether you look at the blue line where we're equal weighting firms or you look at the red line where we're evaluating firms, okay. And we're simply reporting the returns to this hedge fund strategy of buy the stocks whose standalone competitors did well last month. Short the stocks with the standalone competitors do poorly. You see there's this big 1% return to that strategy in the first month and then if anything, the return to strategy goes up a little bit, but it doesn't reverse, okay? It doesn't reverse over time here, okay? Final example of this drift strategy here, using information that's publicly available to predict future, future returns. A classic drift strategy here is based on the under reaction to accounting news awkward acronym here, PEAD, P E A D, post earnings announcement drift. So the idea is investing companies that had a positive reaction to the most recent earnings report for the next 3 months, they continue to do well, okay? Short or avoid companies had a negative reaction to the most recent earnings report for the next 3 months, they continue to do poorly. So it's not surprising if firms announced good earnings, their stock price goes up. What's interesting is that historically you look the prices continue to drift off after that. It's not surprising if a firm announces bad earnings that the market doesn't expect the stock price falls on that day. But what is surprising is the price continues to go down. Okay, so let's think a little more about this. What would be the returns from this post earnings announcement drift strategy in a fully efficient market? What should the returns from this post earnings announcement drift strategy be if there's some behavioral factors so there's under reaction to this accounting news. So I think I kind of set this up pretty well. Hopefully, these questions aren't too challenging. Think your responses then I'll give you my take. What should the returns from this post earnings announcement drift strategy being a fully efficient market? 0, right? Publicly available information shouldn't predict future returns unless that's associated with some type of risk. So the fact a firm had good earnings, that should cause the price to go up when the earnings are announced. But there should be this positive drift afterwards, okay. How about if there's some under reaction? Well, then if there's under reaction kind by definition, if the price isn't going up as much as it should to reflect the good news, then eventually there is this drift as the prices get to fundamentals and historically that's what we observe. Okay, so a lot of people have documented this. I thought one recent study that kind of showed these results fairly well was Andrea Francini 2006. So we're looking at the monthly returns to post earnings announcement drift strategy, at the beginning of each calendar month, we simply rank stocks by their stock return on their most recent earnings announcement date. So the idea is, let's have the market tell us which firms had the most surprising good earnings and the most surprising bad earnings. So we're going to rank firms by, well, as the stock market reaction. When they most recently announced earnings. And we're going to put them in groups of five, where group 5 are the 20% of firms that had the highest return when they announced earnings. The biggest earnings surprise. And group 1 are those that had the worst earnings announcement in terms of the most negative surprise. These are most negative announcements. We're going to kind of control for risk in a Fama-French three-factor model. We've kind of done that quite a bit at this point. So, we're going to report the alphas. The risk-adjusted returns, controlling for the market risk of the investments, controlling for the size and the value characteristics of the investment. And we're going to look our hedge fund strategy it's long short is going to be long. Those stocks that reported the most positive earnings had the biggest market surprise and short those that had the most negative surprise. And the key thing is, we're looking at this strategy going forward. So, it's not surprising, good earnings lead to a good stock return the day it's announced bad earnings lead to a bad stock return the day it's announced. What's surprising is, is there any return to this drift strategy, okay? And we're looking here at kind of t-statistics here in parentheses to strategy. What do we find? What we find over the next three months, those firms that had the great earnings announcement they underperform their benchmark. They continue by 0.6% on a monthly basis or 1.8% over the next quarter. There's a symmetry here, those bad earnings announcers, they continue to underperform their benchmark by -0.6% on a monthly basis or would be about 1.8% on a quarterly basis. Then you look at the difference here, invest in the stocks that had the good earnings announcement, short those that had the bad. The difference between the two is 1.2% on a monthly basis, or 3.6% each points difference over the next quarter. So, the good earnings announcers. It's hard to predict which firms will have the good earnings announcements or the bad earning announcement. It seems like it should be easy to read the Wall Street Journal and just read which had big surprises, which had negative surprises. These results here suggest that simple strategy of just investing in the past. Good announcers shorting the past, bad announcers still yields nontrivial returns going forward. Okay, so stay tuned. Faculty-focus episode, talking more about inattention and predictability in returns. Faculty focus episode that I have in store for you would be quite a treat here. When I interview Josh Pollack we'll talk about two of his areas of research. One is, are people fully paying attention to firm earnings announcements that happens on Friday? And the second is, does the market fully price future changes in demographics that may lead to more demand or less demand for certain industries, okay? And one thing I really want you to focus on in this faculty-focus episode is pay attention to the awkward moment. It's going to be quite a shocker.
