en en

Ľuboš Pástor, člen Bankovej rady NBS: Judging Fund Managers by the Company They Keep (Podcast, v angličtine)


Jeff Ptak: Hi, and welcome to The Long View. I’m Jeff Ptak, chief ratings officer for Morningstar Research Services.

Christine Benz: And I’m Christine Benz, director of personal finance and retirement planning for Morningstar.

Ptak: Our guest this week is Lubos Pastor. Dr. Pastor is the Charles P. McQuaid Professor of Finance at the University of Chicago Booth School of Business, where he’s taught since 1999. He’s a leading researcher on financial markets and asset management, publishing numerous award-winning papers that have been widely cited in the academic finance literature. Dr. Pastor’s research spans a number of areas, including liquidity risk, return predictability, active fund performance and more. Dr. Pastor serves as director of the Center for Research in Security Prices and is a member of the board of the Fama-Miller Center for Research in Finance. He also holds various positions outside of the Booth School, including serving as a member of the bank board of the National Bank of Slovakia. He received his Ph.D. in finance from the Wharton School at the University of Pennsylvania.

Dr. Pastor, welcome to The Long View.

Lubos Pastor: Thanks for having me. Great to be here.

Ptak: It’s our pleasure. Thank you so much for being on. We’re going to delve deeply into your research of which there is lots and there’s lots of good stuff to dig into, but we wanted to start it at a high level. You’ve lots of experience reviewing papers and choosing research collaborators. So, we are curious, based on that experience what advice would you give to our listeners, investors, financial advisors, and the like, who are themselves trying to sift through this ocean of literature and find the most relevant research that they can put to use. What’s a good screening process for them that’s worked well for you?

Pastor: I think there are a couple of dimensions to keep in mind. One is the quality of the research and two is the relevance. If you’re looking for the highest-quality research, I recommend going to top academic journals like The Journal of Finance, Journal of Financial Economics, The Review of Financial Studies. They have the best screening process, peer review, multiple rounds, and so on. If you’re looking for the most relevant research, easily, immediately applicable, there are some excellent practitioner journals: The Journal of Portfolio ManagementJournal of Investment Management, and Financial Analyst Journal and others.

Ideally, you would find the most applicable research in academic journals. That’s not always the case, because academic journals publish not only what’s immediately applicable but also lots of basic research. There’s a lot of research that will hopefully be built on by others and that’s why you see a lot of basic research papers in academic journals. So, ideally, you would follow both of these streams, watching out for relevant work.

I’ll say one more thing. Looking at just published papers may not be what you necessarily need, because research that already comes out in print is not exactly new. The wheels of the publication process turn slowly. I can tell you from my own experience. Sometimes it takes two, three, four years for a new paper to come out in print. So, I also recommend just following working papers, whether it’s SSRN—Social Science Research Network—whether it’s going to conferences, you can often get brand-new research several years before it comes out.

Benz: What topic hasn’t gotten the attention it deserves in academic finance? And conversely, what’s been done to death?

Pastor: I’d say some of the topics that I like to work on are precisely the topics that I think are under-researched. My latest paper is on ETFs, for example, looking at bond ETFs, how exactly they work, how their baskets work. There are very few papers on ETFs in the academic literature. I also like to connect political economy with asset pricing. So, what are the effects of political uncertainty on asset prices, just as an example. I also think we’ll see a lot more research on big data using new datasets that people haven’t explored previously. A lot of machine-learning research, text analysis, that kind of stuff. You also asked about the other side. I’m a little tired with papers on the 350th factor that can perhaps explain the cross section of stock returns. So, that’s where I am.

Ptak: We’re going to ask you about factors a little bit later in the conversation. You can give us some tips on what’s the best way to separate the wheat from the chaff when it comes to risk factors. Before we did that, though, I wanted to turn and talk about some of the research that you’ve done on stocks. You wrote a paper in 2012 called “Are Stocks Really Less Volatile over the Long Run?”—in which you question the conventional wisdom that stock returns are more predictable over longer horizons. You found the opposite. Can you explain that?

Pastor: The conventional wisdom is that volatility of stock returns becomes smaller as the investment horizon increases. And my co-author and I—Rob Stambaugh and I—we find the opposite. We find that long-horizon investors actually face more volatility per period than short-horizon investors. And this is not because we argue that some people crunched their numbers incorrectly. Not at all. It’s because we compute volatility looking into the future rather than back in the past.

Let me take a step back. This conventional wisdom that stocks are less volatile in the long run is based on historical estimates of volatility. So, you look back, you determine the average return. Suppose it’s, let’s say 10% per year nominal. And you compute volatility at various horizons around that known average return. Well, we look into the future, as we believe investors do, and we compute volatility around this unknown mean that we’re going to have going forward. We don’t know what the average return is going to be in the future. And this forward-looking variance that we calculate takes this uncertainty about the mean into account. And that is the main reason why we find stocks being more volatile in the long run rather than less volatile. So, it’s not that we find that there’s no predictability or no mean reversion in returns. We actually do find mean reversion in returns. It’s just that we find that mean version is more than offset by uncertainty about the trend around which stock prices are going to fluctuate in the future. Does that make sense?

Benz: It does. But I have a follow-up question, which is, whether that finding is more pertinent to individual stock holdings than it is for diversified stock portfolios, because it seems like the range of returns does narrow for popular indexes like the S&P 500 as you lengthen the measurement period?

Pastor: We never actually look at individual stocks. So, I cannot really answer your question. We only look at the market as a whole. We look at the aggregate stock market portfolio and calculate its volatility over various horizons, ranging from one year all the way to I think maybe something like 30 or 50 years. And you’re right that historically the range of returns narrows for the market as a whole as you lengthen the investment period. But as I said earlier, that’s the perspective of the historian, looking backward, looking at what happened in the past. And the historian knows the average return. It’s a number that was realized in the past. We take the perspective of an investor who’s looking into the future, and the forward-looking investor doesn’t know the average return. The investor doesn’t know the equity premium, and it’s essentially the uncertainty about this equity premium that matters and that causes stocks to be more volatile in the long run.

Let me give you an example. Suppose that you accumulate returns at, let’s say, 5% and I accumulate them at 6%. So, it’s like having a different equity premium loosely speaking. And one year out, there’s not much difference between what you earn and what I earn. You’re at 1.05; I’m at 1.06. But as we go further out, that difference between 5% and 6% per year actually grows and cumulatively, at long horizons, the uncertainty about the trend matters a lot more than it does at short horizons. So, that’s why we find what we find.

Ptak: Maybe to segue from that and talk about one of the potential implications of that finding. I think I’ve heard you say in previous interviews that maybe one implication is you back off even as a young person your equity allocation a little bit, just acknowledging the fact that there’s maybe a little bit more uncertainty to the stock return than historically based forecasts would suggest. I think that the conversation shortly turned to target-date funds and how they allocate more to stocks in investors’ early years when they’re far from retirement and then reduce it as time goes on. And it sounds like you have somewhat mixed feelings about the shape of that glide path. Is that correct? So, maybe you can talk about the implications of your research in the context of things like target-date funds and how they’re constructed.

Pastor: That’s a great question. I’d say, usually when people try to justify target-date funds, so they try to justify a downward-sloping glide path, they tend to give two reasons. One is mean reversion, which is what we’ve talked about; and the other is essentially people’s human capital, the fact that our labor income is pretty safe. And I think the second argument in favor of target-date funds is very strong, and I think it makes a lot of sense for a lot of people to go with target-date funds. But the first argument, the mean-reversion argument, strikes me as pretty weak precisely because of this work that I’ve done with Rob Stambaugh. Essentially, we do find mean reversion in stock returns. We do find that ups and downs cancel out. But at the same time, as I said earlier, we find this tremendous uncertainty about the mean, which actually more than offsets mean reversion.

So, it’s confusing with too many arguments. I actually think that target-date funds can be useful once you recognize the human capital component of people’s income. But I just don’t fully buy the mean-reversion justification. If I may, I actually think target-date funds are good for some people and not for others. My main quibbles with target-date funds are that they are sometimes too expensive. So, depending on which fund family you go to, sometimes they charge a second layer of fees. Sometimes the underlying funds in these funds of funds are not necessarily the best funds that the family has to offer. Sometimes people use target-date funds incorrectly. You’re supposed to put all of your money in the target-date fund. Instead, some people do one third in stocks, one third in bonds, and one third in a target-date fund, and that’s not how they’re intended to be used. So, there are issues. But I also think that target-date funds can be perfectly fine for you if your labor income is safe, highly predictable, if you put all of your money in the target-date fund, if there’s no second layer of fees, if the underlying funds are passive, low-fee funds. So, I’m not opposed to target-date funds. I just think that they’re better for some people than for others.

Benz: We wanted to switch over to discuss environmental, social, and governance investing, ESG investing. You’ve done work examining sustainable investing. Seems like your conclusion is that there’s no clear link between an asset’s greenness and its expected returns. Given this, why has ESG become so popular?

Pastor: My co-authors Rob Stambaugh, Luke Taylor, and I, we actually do find a clear link between greenness and expected returns. Specifically, we argue both theoretically and empirically that greener assets have lower expected returns. And we give two reasons. One is based on tastes, and one is based on risk. So, the taste reason is that people love holding green assets. Investors love holding them. So, in equilibrium, these greener assets end up with higher prices and therefore, lower expected returns going forward. And then, there’s a risk reason that also generates lower returns for greener assets, and that is that greener assets are a better hedge against climate risk. Or flipping this around, brown assets are more exposed to climate risk. So, they have to offer higher expected returns to compensate. So, again, both of these channels, economic channels, tastes and risk, leads to lower expected returns for greener assets.

You also asked why has ESG become so popular, especially given that expected returns are lower. I’d say people love holding assets that they like even for nonpecuniary reasons. As our society grows richer, people are increasingly willing to pay more for organic coffee, pay more for clothes that are made by companies that they like, things like that. So, I think that’s why ESG has grown. I think people value nonpecuniary issues more and more.

Ptak: Maybe to build on that, let’s suppose your assertion is correct and people derive greater joy from those green assets than they do investments in brown assets. But if all the trading takes place in the secondary market and has little to no impact on a firm’s greenness, so to speak, do you think those investors who are taking joy in making those investments in green firms are in effect they’re deluding themselves?

Pastor: So, yes and no. If you believe that by divesting from brown stocks, you are somehow going to change the world, you’re going to make the world significantly better, then yes, I believe you are deluding yourself. But if you believe that your green tilt, if I may, is going to have a small impact on the world, then I think that’s fair. And also, if you derive pleasure from overweighting green assets and underweighting brown assets, again that is perfectly fine. You’re not deluding yourself at all. You derive pleasure from doing this.

So, why do I think that you can actually have a small impact on the world when you do this? Because when enough people divest from brown firms, in equilibrium, the stock price of brown firms will be slightly lower. And therefore, the cost of capital for brown firms will be slightly higher. And so, brown firms will find it harder to raise capital. It will be a little bit more expensive for them to raise capital. So, some investments that would have been NPV-positive will now be NPV-negative and brown firms will end up investing a little bit less. So, there will be a little bit less investment by brown firms as a result of these portfolio changes.

In addition, if enough people behave this way, if people value greenness in assets, then managers, corporate managers, will have an incentive to make their funds greener. And this is true not only about managers who have some inherent desire to make the world a better place. Even a pragmatic market value-maximizing corporate manager will want to make their firm greener because that is how you increase market value in the world in which investors value greenness. So, I guess that’s a long answer to your question. But I do think that investors can make the world a better place, slightly better place, by investing in green assets and downgrading brown assets.

Benz: Markets are quite efficient. But with the benefit of hindsight, it would have been good to be long green assets and short brown assets back in 2008, as the former handily outperformed the latter in the 15 or so years since. Markets look forward factoring risks into prices. So, why wasn’t that the case back in 2008?

Pastor: Yeah, you’re right. So, green assets outperformed brown assets over the past decade or so, maybe with the exception of the last couple of years. And it’s a good question. How come the markets didn’t know that in advance? My perspective on this is that we were simply surprised by what happened over the past decade. So, market efficiency doesn’t mean that somehow markets have a crystal ball that tells them what’s going to happen in the future. According to my colleague Gene Fama, markets are efficient if and only if prices incorporate all available information, information that’s available today, not information that’s available tomorrow. So, market prices move when you get news, when you get new information. And what I believe happened over the past decade, and I have some research on this, is that we actually got news that favored green firms rather than brown firms over the past decade. So, for example, somebody else’s climate index that moved sharply up over the past decade suggesting that investors and consumers cared more and more about sustainability as the past decade progressed. In other words, 10 years ago, I wouldn’t have anticipated that there would be such an increase in interest in sustainability, environmental sustainability, and so on. But that is what happened. And as a result, we got repricing of green assets upward. We got repricing of brown assets downward. And yeah, going forward, things are different though. Going forward, I think we’re looking at the equilibrium again and green assets are expected to perform less well than brown.

Ptak: Wanted to turn and talk about some of your other research. Maybe it could be classified as myth-busting. One example is the notion that active stock funds perform well amid market turbulence. You examined that in a recent paper in which you analyzed how those funds did during the COVID crisis. Maybe you could talk about what you found in your study.

Pastor: I think you’re talking about the study I did with a very talented Ph.D. student here, Blair Vorsatz. We actually used Morningstar data to look at the performance of mutual funds, U.S. mutual funds, during the COVID crisis. We looked at the worst part of the COVID crisis, like the March and April of 2020. And what we found was that active funds underperformed passive benchmarks during this crisis and by a wide margin, something like three quarters of all funds underperformed the S&P 500 and almost 60% of them underperformed their corresponding FTSE Russell benchmarks. And this, at least to us, was somewhat surprising, because when people point to active fund’s underperformance, long-run underperformance, they often say, well, active funds underperform in the long run, but they make up for it because they perform better, they outperform in bad times, like in recessions or in crises precisely when you want them to outperform. So, this is commonly given as a justification for investing in active funds.

So, we looked for it in the biggest economic crises that we’ve had in a long time, in the COVID crisis of 2020, and we didn’t find any support for that hypothesis. Instead of outperforming, active funds actually underperformed by a pretty wide margin. So, that was a surprise to us. And by the way, we also found that some key Morningstar variables had predictive power for performance during the COVID crisis. So, funds with more Morningstar sustainability globes performed better, so more-sustainable funds had better performance, also better flows. And more surprisingly, funds with higher star ratings also performed better. We didn’t quite understand why, but we found that significant relation.

Benz: That’s interesting. I wanted to ask about previous time periods—while active stock funds didn’t do well in 2020, it does seem that they hold up a little better amid downturns going back through time. What do you think made 2020 different from those earlier periods?

Pastor: Great question. There is this prior work that you’ve alluded to, suggesting that active funds do perform somewhat better during recessions. One of the first papers was written by my former colleague Toby Moskowitz, who’s now at Yale. I’ve looked at that evidence myself. I think it’s not terribly strong. It does go in the direction you’ve mentioned. Whether it’s statistically significant is, I guess, debatable. What makes 2020 different? Well, it was a far bigger recession than the recessions analyzed by prior work. But other than that, I’m not really sure. It will be interesting to see how active funds hold up in the next crisis.

Ptak: You’ve also done some work on newer funds, finding that they did better by some measures than older funds. That bucks industry convention, which usually favors more established and proven funds. What explains why newer funds did better when you examined them for that study?

Pastor: This is a paper I did with Luke Taylor and Rob Stambaugh. What we find specifically is that we find that fund performance tends to deteriorate over a fund’s lifetime. As a given fund grows older, its performance tends to get slightly worse, benchmark-adjusted performance. So, in that sense, newer funds perform better because you perform better when you’re younger than when you’re older. And this is controlling for lots of things.

Why is that? Our story, the story we tell in our paper, is that it’s an issue of growing competition. So, suppose you just graduated from Chicago Booth with an MBA. You have all this cutting-edge knowledge about the best investment strategies, and so on. Couple of years later you start running a fund. You are on the cutting edge. You’re going to be doing really well. But then, as time passes, 10 years later, 15 years later, maybe your knowledge is not so cutting edge anymore. New graduates start running new funds, and perhaps, they’ll outperform you unless you keep up successfully. So, that’s our story, that there’s growing competition over a fund’s lifetime that tends to make their performance worse.

Benz: You referenced factors at the outset of our conversation, and your most cited paper is on liquidity as a factor. Can we start out by discussing what a factor is, how you would define it, and also on the flip side, what’s something that someone might think of is a factor but really isn’t?

Pastor: I think of a factor as an economic variable that moves over time and captures common variation in returns. So, it’s a variable that is correlated with many assets’ performance, with the performance of many assets. Or put differently, maybe slightly more technically, there are many assets that have significant betas, or significant loading, or significant exposures to this variable. So, that’s what I think of as a factor.

You mentioned liquidity as a factor. When liquidity evaporates from the market, many assets lose market value. So, liquidity is highly correlated with asset returns, and we find that that is the case above and beyond any correlation with the stock market as a whole because the stock market also falls when liquidity evaporates. But many assets have a beta with respect to liquidity even after controlling for their exposure to the stock market. So, that’s a factor. I think it’s also very important to distinguish just a factor from a priced factor because not every factor is priced. And a priced factor is a factor that has a risk premium attached to it. So, a priced factor is one where assets that have high betas with respect to this factor have higher average returns going forward. Assets that have low betas have lower returns going forward. And that is precisely what we find about our liquidity factor, that assets that have higher liquidity betas have higher risk-adjusted returns going forward.

Ptak: That’s helpful. Maybe at this point it makes sense to talk about liquidity and how you defined it for purposes of the paper that you wrote and also, maybe how sensitive the findings were to one’s definition of liquidity?

Pastor: So, Rob Stambaugh and I designed this liquidity factor many years ago, I think 20 years ago. And we measured liquidity at the individual stock level by essentially trying to measure price impact. Because we believe that that’s the dimension of liquidity that matters the most to institutional investors. How much do you move the price when you trade a given amount? Specifically, we designed a regression model where we asked, suppose you trade $1 million worth of a stock; you move the price, and we ask how big is the reversal after that price impact the following day? So, the idea is if you sell $1 million worth of the stock, you depress the price because some market maker has to provide liquidity. They have to get compensated. So, the price drops temporarily. But then there will be a reversal going forward so that the market maker can actually get paid in expectation for providing liquidity.

We measure that temporary reversal in price that results from a $1 million trade. And then, we average this quantity across many stocks, across all stocks that are trading in the marketplace to get a marketwide measure of liquidity. And when you plot it, it looks like liquidity. Most of the time, it’s just fine. It’s not doing much. But now and then, it dries up. Now and then, there’s a spike in liquidity where liquidity evaporates from the market. These are moments like October ‘87, ‘98, 2008. So, that’s what we do.

You also asked about sensitivity to other definitions of liquidity. We haven’t really tried many in the paper. We’ve tried a few to show that they don’t work. It’s very important what you use as a measure of liquidity. Some obvious candidates are not very good when you’re trying to construct the liquidity factor. So, for example, people like to use trading volume as a metric for liquidity. The idea being when there’s a lot of trading, liquidity is high. And I think that’s a good idea if you’re looking across assets. But it’s not a good idea to do this when you look across time. Trading volume is not a good measure of liquidity over time. A perfect counter example is October 1987 when we had a total evaporation of liquidity and yet we had a record high trading volume in the market. So, price impact was very high; at the same time, the trading volume was very high. It was basically one-sided volume. So, long story short, it actually matters a lot how you define liquidity and if you do it the way we do it by looking at price impact, then you find that liquidity is priced.

Benz: Why hasn’t liquidity caught on more? And if it did, is there a reason to believe that investors would love it to death and arbitrage away whatever premium might have formerly been available?

Pastor: Good question. Why it hasn’t caught on more? I don’t know. I am in the business of academic research. So, I’d love to know why it hasn’t caught on more. But as far as it being arbitraged away, there are signs that it has not been arbitraged away. We wrote our paper 20 years ago. We published it in 2003. Our dataset ended in December 1999. We found that there was a liquidity risk premium, a significant one, in that period, 20th century essentially. Since then, we and others have shown that the liquidity risk premium has persisted out of sample. So, going forward, if you add almost 20 years of data, you find that the result continues. In fact, the liquidity risk premium has been slightly larger out of sample. So, high-liquidity beta starts continue having higher average returns out of sample. So, it may have been arbitraged away, but it wasn’t. It was a possibility, but that possibility was not realized.

I will also mention that even though we designed our liquidity measure 20 years ago before the financial crisis of 2008, it was good and comforting to see that this measure, the same measure, actually successfully captures the large drops in liquidity in 2008. So, when you plotted our measure in 2008, which is after it was designed, you see big drops in liquidity. It’s doing what it’s supposed to be doing.

Ptak: I wanted to shift and talk about managers and funds. You’ve done lots of good research in this area. You wrote a popular paper called “Judging Fund Managers by the Company They Keep.” Can you walk through the premise of that research and explain what had inspired it?

Pastor: Thank you. This is a paper with Randy Cohen and Josh Coval from Harvard. The idea behind our paper is that instead of just looking at a manager’s track record, you can look at how the manager is trading and to what extent the manager’s trades are similar to the trades of other managers. And then, when you do that, you’ll be able to use thousands of track records instead of just one in order to evaluate a manager.

So, let me give you an example. Suppose that you’re evaluating the manager. You look at their trades over the past quarter and you find that they bought the same stocks as Warren Buffett did and they sold the same stocks as Warren Buffett did. Well, we know that Warren Buffett has performed very well over a long period of time. So, Buffett’s track record then becomes informative about the skill of this manager who trades like Warren buffet. And this is just an example. But we do this by averaging across all managers out there. So, effectively, to evaluate a given manager, evaluate the skill of a given manager, you end up using thousands of track records of all other managers and you weight them by how similar the trading of those managers is to the trading of the manager you’re evaluating. And the proof is in the pudding. Essentially, we find that the skill measure that we come up with is helpful in predicting manager returns going forward.

Benz: Success tends to be fleeting among managers. It’s certainly something we’ve observed at Morningstar. So, why would a technique of favoring managers whose holdings resemble those of other successful managers work over an extended period?

Pastor: Good question. Again, it could be arbitraged away in principle. If enough people did this kind of research and put their money in the best-performing funds, they would flood those funds with money and they would depress their future performance. It doesn’t seem to have happened. In fact, there’s some research out of Morningstar suggesting that our results continue to hold over a 15-year period after the end of our sample. So, good question. It wasn’t guaranteed that it would continue to work, but it seems to have worked.

Ptak: And I’m glad that you reference it. We’ll put that Morningstar study in the show notes so people can take a look at it. I wanted to ask you about another piece of research that you did, this one on the trade-off between manager skill and scale. And I think this concept came up earlier in the conversation. Can you talk about that research at a high level and what your key findings were?

Pastor: There’s this question about whether scale of investment is somehow related to future returns, and there’s this notion of decreasing returns to scale in active management. Most of the time people talk about fund level decreasing returns to scale—the idea being that as the size of my fund increases, then my fund’s performance gets worse. The idea there is that if I manage a larger fund, then my transaction costs go up and that’s why I’m going to have to end up trading less and my performance will suffer as a result.

My research with Rob Stambaugh and Luke Taylor on this topic talks more about the decreasing returns to scale at industry level. So, instead of fund level, we look at industry level. What we find is that as the size of the active management industry increases, then everybody’s performance declines. So, when there’s more money that’s actively managed, when more money chases mispricing, loosely speaking, then every active fund will find it more difficult to deliver alpha. Essentially, there’s more competition and that decreases everybody’s performance. So, that’s the gist of it. Instead of decreasing returns to scale at fund level, we emphasize decreasing returns to scale at industry level. And I actually believe that both of these channels are in play.

Benz: I think you’ve said that there could be a tipping point where the active industry shrinks enough to make its size less of an impediment to its own success. What would signify this, and is there realistically a way to predict and maybe even exploit this event?

Pastor: Great question. Let me first start with laying the background here. We all know that there has been a shift from active to passive investing over the past few years, mostly motivated by the relatively poor performance of active funds. But what Rob Stambaugh and I point out in our work is that as money continues to shift from active to passive funds, then the degree of competition in the active industry continues shrinking as well. There’s less and less competition. So, slowly but surely the expected performance of active managers is increasing. And if you take it to the extreme, if all but a couple of funds, active funds, disappear, then surely the remaining couple of active funds will find it very easy to outperform because they’ll be picking low-hanging fruit. If there are only a few managers paying attention, then they’re going to make a lot of money because they face very little competition.

So, what you’re asking is an excellent question. Where is that equilibrium? Where is the point at which the expected return on active managers going forward essentially is the same as the expected return of passive managers? Because that is the equilibrium point when investors become indifferent between investing actively and passively. And in our paper, we actually solve for this equilibrium. We have a mathematical expression for this is the optimal size of the active management industry. We also explain that it’s very difficult to predict it because essentially the quantity depends on the degree of decreasing returns to scale in the industry. Essentially, if you move $1 out of active funds, how much does the alpha of each manager go up? And that quantity is extremely difficult to estimate it turns out, even with like 40, 50 years of data that we have available, we show that it’s very difficult to estimate that quantity and that makes the equilibrium size of the industry very difficult to pin down. So, unfortunately, I can answer your question conceptually. We’ve done it in our paper. But coming up with a precise number is very difficult.

Benz: As you note, active investors have become more skilled, but competition has also ramped up and assets have swelled, and those things make it harder to outperform. Nevertheless, we’ve seen a lot of managers hug their indexes. So, wouldn’t that serve to partially offset rising skill and scale and give the more daring sorts of active managers a chance?

Pastor: That’s a good point. We have indeed seen an increase in what some academics refer to as closet indexing. Effectively, when active managers become less active, it’s essentially similar as if money were migrating from active to passive. And yes, I agree that should make it easier for the daring, as you put it, active managers to perform better going forward.

Ptak: What do you think of the notion that some investors are willing to pay a premium to avoid experiencing bouts of short-term volatility? Private equity is an example of an asset class that some have argued investors prize, mainly because of the way it smooths returns, so they don’t have to deal with those bouts of short-term volatility. What do you make of that argument?

Pastor: I have heard the argument and I agree—if you look at private equity returns, they appear to be smooth, but they appear to be smoother than they really are, is my perspective on it. Private equity is just equity, just like public equity. And the only reason you don’t see it fluctuate as much as public equity is that you don’t have prices minute to minute, hour by hour. So, it is fluctuating more than its smooth returns, reported returns suggest.

I personally am unwilling to pay a cent for this kind of smoothing. So, why should I pay anything at all for this type of smoothing? Perhaps there are some institutional investors out there who do appreciate this. But there’s a question of price. How much would you be willing to pay just to feel better. Because your reported volatility is lower than it really is. So, I wouldn’t pay much for it.

Ptak: I wanted to follow up on that. I suppose that one argument that you could make, and I think others have made this argument, is that investors are willing to pay these premiums because they know doing so will allow them to avoid the far larger costs of mistimed purchases and sales that they make amid market turbulence. Maybe they make those purchases and sales on impulse. In a sense, they pay a premium so as to not hurt themselves by misbehaving at the worst times. Do you think there’s any merit to that argument?

Pastor: If I understand it correctly, investors are making one mistake in order to avoid a bigger mistake.

Pastor: The worst one, yeah.

Pastor: Yeah, perhaps. I do think that there is a better way of fixing the big mistake. I think the big mistake you’re referring to is that when prices drop, like, I don’t know, 2008 or March 2020, then many investors panic and sell.

Ptak: Right.

Pastor: And that is indeed a big mistake because it’s precisely in those periods that expected future returns tend to be higher. But I would try to fix that mistake differently, just through education, or maybe through target-date funds that you’ve mentioned earlier. Investing in private equity just so that I don’t see those negative returns—it doesn’t seem like the optimal solution to this problem. There could be other reasons to invest in private equity, so, don’t get me wrong. Just, I’m not sure this is the reason I would pick.

Benz: One theme that you’ve returned to in your research is the intersection of politics and investing. For example, you’ve studied the relationship between stock returns and the party that held the White House at various points in time. Can you talk about the key findings from your research and how it could be applied in practice?

Pastor: There’s this fascinating finding that others have found before Pietro Veronesi and I started working on this, and that’s that stock market returns in the U.S. are significantly larger on average when a Democrat is in the White House than when a Republican is in the White House. So, specifically, under Democrats in the White House, the stock market returns have been 11% per year higher than when a Republican is in the White House. And we’re talking 11% per year over a long period of time, something like a century, almost a century.

That’s a big difference in returns, and people have been scratching their heads. What is it that Democrats are doing that’s causing high returns or what is it that Republicans are doing that’s causing lower returns? And we basically argue that it’s not that Democrats are causing higher returns or Republicans are causing low returns. We have essentially a nonpartisan solution to this puzzle.

We argue that what matters it’s not what presidents do but when they get elected. And Democrats tend to get elected in times of trouble, whereas Republicans tend to get elected in good times. And we also explained why that makes sense. But let me give you some examples. So, take November 2008. We’re in the middle of the financial crisis. Barack Obama, a Democrat, gets elected. Or take an even bigger crisis in 1932, The Great Depression. FDR, again a Democrat, got elected. And we could keep going and we would actually find that when we’re in a crisis, in a recession, for example, we tend to see higher likelihood of Democrats getting elected.

Well, it so turns out that those are also times when expected future market returns are high. In November 2008, there was a very high risk premium in the market. So, expected future return was very high. So, Obama came into the White House precisely at the time when expected future return was very high. And so, not surprisingly, under Obama, realized returns were also high. But it’s not necessarily that Obama caused the high stock market returns. We argue that the causality is different. The causality runs from the financial crisis, which caused Democrats to get elected, and it also caused high expected returns going forward when there was a rebound. And vice versa. When Republicans are elected, that tends to happen in good times, because in good times people like to vote for the pro-business party. Their demand for social insurance is a little lower. So, in good times, people elect the Republican. Republican comes in, but the expected return going forward is low because in good times stock prices are high. So, the equity premium is low and that’s why under Republicans we tend to see lower average returns. So, again, that’s our story for why stock market returns are higher under Democrats than under Republicans, and it’s a nonpartisan story.

Ptak: You’ve also done some recent work on quantitative easing, comparing the way central bankers analyze its efficacy to the way academics do the same. I think what you found is that the central bankers tend to find QE to be more effective than academics. And so, what are the reasons for that you found in your research and what, if any, implications do you think it has on monetary policy and the way participants in the market might react to monetary policy and the way it’s communicated?

Pastor: What we did was look across all papers that have analyzed the economic effects of quantitative easing in the U.S., Europe, or the United Kingdom. We found 54 studies like that, and a bit more than half were written by central bank economists, like people working at the Fed or the Bank of England; and less than half were written by academics, people working at universities. And we found that when central bankers were evaluating QE, the effectiveness of QE, they found QE to be significantly more effective. So, they found larger effects of QE on output, positive effects. They were more likely to find QE to have a statistically significant effect on output as well as on inflation. We don’t have a definitive smoking gun as far as the mechanism is concerned. But one possibility is that there is some pressure on central bank economists to deliver the desired result.

Let me give you an example from outside economics. Take a pharmaceutical company. When a pharmaceutical company does research evaluating the effectiveness of its own drugs, it often finds that its own drugs are effective, and that’s perfectly fine. That’s valuable research because the pharmaceutical company knows a lot about its own drugs. But in addition to taking that valuable research, I think it’s also helpful to do some additional research, objective research, if you will, to verify the claims of the pharmaceutical company. So, in the same sense here, I think if central bank evaluates its own policy, I think that’s very helpful because the central bank has top people doing this research and they have excellent information, better information than most others. But at the same time, it’s also good to complement that research by some additional work. I personally find central bank research very highly credible, and it’s only a small part of central bank research that aims to evaluate central bank policy. That small part, I think, we need to do more work on, maybe some additional verification. But most central bank research is not about evaluating central bank policy. So, that I think is perfectly credible.

Ptak: Well, Dr. Pastor, this has been a very enlightening conversation. Thanks so much for sharing your time and insights with us. We’ve enjoyed the discussion.

Pastor: Thanks for having me. This was a lot of fun.

Benz: Thank you so much.

Ptak: Thanks for joining us on The Long View. If you could, please take a minute to subscribe to and rate the podcast on Apple, Spotify, or wherever you get your podcasts.

You can follow us on Twitter @Syouth1, which is, S-Y-O-U-T-H and the number 1.

Benz: And @Christine_Benz.

Ptak: George Castady is our engineer for the podcast and Kari Greczek produces the show notes each week.

Finally, we’d love to get your feedback. If you have a comment or a guest idea, please email us at TheLongView@Morningstar.com. Until next time, thanks for joining us.

(Disclaimer: This recording is for informational purposes only and should not be considered investment advice. Opinions expressed are as of the date of recording. Such opinions are subject to change. The views and opinions of guests on this program are not necessarily those of Morningstar, Inc. and its affiliates. While this guest may license or offer products and services of Morningstar and its affiliates, unless otherwise stated, he/she is not affiliated with Morningstar and its affiliates. Morningstar does not guarantee the accuracy, or the completeness of the data presented herein. Jeff Ptak is an employee of Morningstar Research Services LLC. Morningstar Research Services is a subsidiary of Morningstar, Inc. and is registered with the U.S. Securities and Exchange Commission. Morningstar Research Services shall not be responsible for any trading decisions, damages or other losses resulting from or related to the information, data analysis, or opinions, or their use. Past performance is not a guarantee of future results. All investments are subject to investment risk, including possible loss of principal. Individuals should seriously consider if an investment is suitable for them by referencing their own financial position, investment objectives and risk profile before making any investment decision.)