Angelica Montagano

November 12, 2020

Active Management, Advanced Topics, Economics, EMH, Expected Returns, Historical Data, Inflation, Portfolio Management

Episode 124: Prof. Lubos Pastor: Equilibrium Models vs. Intuition

Angelica Montagano

November 12, 2020

Active Management, Advanced Topics, Economics, EMH, Expected Returns, Historical Data, Inflation, Portfolio Management

Lubos Pastor is Charles P. McQuaid Professor of Finance at the University of Chicago Booth School of Business. He also serves as a member of the Bank Board of the National Bank of Slovakia, Director of the Center for Research in Security Prices (CRSP), member of the CRSP Indexes Advisory Council, Research Associate at the National Bureau of Economic Research, and Research Fellow at the Centre for Economic Policy and Research. He has served as President of the Western Finance Association, Director of the American Finance Association, and Co-Director of the Fama-Miller Center for Research in Finance. He is an Associate Editor of the Journal of Financial Economics and a former Associate Editor of the Journal of Finance and the Review of Financial Studies.

Professor Pastor’s research focuses mostly on financial markets and asset management. He has written on a broad range of topics such as liquidity risk, political risk, sustainable investing, stock price bubbles, stock volatility, return predictability, technological revolutions, income inequality, populism, portfolio choice, performance evaluation, returns to scale in active management, indexing, learning, and IPOs. His articles have appeared in the American Economic Review, Journal of Finance, Journal of Financial Economics, Journal of Monetary Economics, Journal of Political Economy, Review of Financial Studies, and other outlets. His research has been awarded numerous prizes, such as two Smith Breeden Prizes, three Fama/DFA Prizes, Goldman Sachs Asset Management Prize, Barclays Global Investors Prize, the NASDAQ Award, the QMA Award, and the Q Group Award.

Professor Lubos Pastor’s brilliant and varied research has been consistently referenced on this podcast. From how politics impacts stock returns to measuring the skill of active fund managers, Lubos joins us today as we explore some of the ‘greatest hits’ of his research. With Lubos’s position on the board of the Slovakian Central bank, we ask him about how quantitative easing can be used to strengthen the economy. His answers highlight how easing can prop up asset prices and raise inflation — and why inflation is the “least bad” option to deal with post-pandemic debt. We then discuss Lubos’s research on how political cycles affect stock returns and why stock returns are higher when a Democrat is in the White House. After diving into how stock prices respond to political uncertainty, we look at why green assets tend to generate higher stock prices but low expected returns. While talking about his research on measuring volatility, Lubos argues against the conventional wisdom that stocks are stable, in the long run. We touch on how this can affect your retirement asset allocation before chatting about whether young people should use leverage. With so many people moving from active to passive and index fund investing, we analyze the relationship between the scale of active funds and the skill of active managers. Near the end of the episode, we talk about the effect that market-wide liquidity has on stock prices and why you cannot diversify away from liquidity risk. Our conversation with Lubos is filled with insights, each of which could inspire hours worth of discussion. Tune in to hear more from our discussion with Professor Lubos Pastor.

Key Points From This Episode:

Introducing Professor of Finance and today’s guest, Lubos Pastor. [0:0:15]
The role of central banks and the goal of quantitative easing. [0:05:06]
Whether quantitative easing props up asset prices. [0:07:58]
Exploring different findings on quantitative easing by central banks and academics. [0:08:51]
Why inflation may be the “least bad” option to deal with post-pandemic debt. [0:10:27]
How increased inflation helps shift the burden of debt from those who are most impacted by lockdowns. [0:11:17]
Lubos explains the relationship between political cycles and stock returns. [0:13:39]
Hear how political uncertainty affects stock returns. [0:18:46]
Why green assets tend to generate higher stock prices and low expected returns. [0:20:38]
What factors would cause green assets to perform well, and how long this might last. [0:25:22]
The link between sustainable investing and firms pushing to turn green. [0:27:48]
Dispersions among ESG rating organizations and issues related to ESG scoring. [0:29:45]
Why green assets will, most likely, never outperform in the long-term. [0:32:37]
Exploding the conventional wisdom that stocks are less volatile in the long run. [0:34:26]
How long-term stock volatility affects your retirement fund asset allocation. [0:38:30]
How human capital and mean reversion should factor into a young person’s decision to use leverage. [0:39:54]
Analyzing the skill and scale of active fund management. [0:42:49]
Why consumers moving towards passive investing will increase active fund performance. [0:46:37]
Answering the question: Did active funds do well over the pandemic? [0:52:39]
Ways of selecting a good active fund manager. [00:54:28]
Buffett’s alpha strategies of replicating the decisions of top fund performers. [00:56:55]
How market-wide liquidity impacts stock prices. [01:10:42]
Whether current liquidity betas can predict future liquidity betas. [01:02:32]
Lubos shares how he defines success in his life. [01:05:18]

Read the Transcript:

What is quantitative easing trying to accomplish?

Well, its ultimate goal is to strengthen the economy. It makes sense when the economy is weak and when the central bank has already run out of traditional ammunition. So short-term interest rates are already so low that they cannot go any lower. And the way QE aims to boost the economy is by producing the custom borrowing for firms, governments, households. The idea is that a lower cost of borrowing will encourage firms to invest, governments to do the same households to spend to keep the economy going. I should also note that QE is about longer-term interest rates. Central banks are pretty good at controlling short-term rates, but longer rates tend to be determined by supply and demand for capital and QE tries to get at these long-term rates.

QE gets described in the media as money printing? Is there any evidence that QE is inflationary?

Good question, because we tend to think of money printing as being inflation. There all these things, episodes. Weimar Germany post-war Hungary, recent Zimbabwe where central banks printed massive amounts of money and that resulted in hyperinflation. QE is a little similar, but not quite. It's a little different because it's printing money in a different sense. It's creating bank reserves. So instead of printing bank notes, they're really printing electronic money. That's different because you can pay interest on electronic money. Okay? So it doesn't have to be inflation. We can create all this money as a central bank, and then you can raise the rate that you pay on that electronic money. You can raise the rate on the pay on excess reserves and that can in principle, reduce inflation. So I just want to start with that.

It's not obvious that QE is inflationary, but actually there is some evidence that it is. I actually had a recent paper where my coauthors and I reviewed 54 studies of the effects of QE on output and inflation. And we found that on average, the studies find that QE increases the price level by about 1.4% of the peak. So there's some evidence of inflationary pressure, it's modest. And by the way, this is a feature, not a bug. Central banks want you to be inflationary because central banks have been unable to achieve their inflation targets in some years, at least the big ones. So QE is helping them get closer to those targets.

Does QE prop up asset prices and potentially creating a bubble that might cause those prices to detach from business fundamentals?

QE does prop up asset prices, but I'm not sure I would call it a bubble. I don't think prices are detached from the fundamentals under QE. And you think about the present value formula, prices are always attached to the fundamentals from discount rates. What QE is doing is that it's pushing down, it's helping push down long-term discount rates. So prices remain attached to the fundamentals, but through a lower discount rate. So then it amounts to whether you think that QE is pushing these rates artificially too low, if you think so, then it's pushing prices artificially too high. But again, I would say it's a feature, not a bug. The idea is to get households to spend more, and if they feel wealthier, they're likely to spend more.

You mentioned your recent paper, 50 shades of QE conflict of and economic research, which is fascinating, fascinating work. But you compared the academic findings about QE, about the effect of QE to the central bank research on QE. What did you find in terms of the differences?

We found that central bankers are more optimistic about QE than academics. But maybe I should back up a little bit. We look at all studies, we could find on the effects of QE on output and inflation. And there were 54 of them, which is why we call our study 50 shades of QE. And yeah, we found that central bank economists, if a study was written by a central bank economist, then the paper ended up being on average, more optimistic about the effectiveness of QE. So central bank economists report larger magnitudes of the effects for both output and inflation. And they're more likely to find the effects to be statistically significant. That's our main finding.

So we didn't know what to expect. I mean, there are conflicts of interest in various areas. In economic research, they haven't really been explored very much. So we were excited to find a meaningful difference.

Were the differences in magnitude or also in direction? Did both sets of studies find positive impacts from quantitative easing?

Yes. So regardless of whether you look at academics or central bankers, they found QE to be effective at raising both output and inflation. But again, central bankers were significantly more optimistic. In fact, in the case of inflation, the effect found by academics was quite modest.

Do you think this debt load will result in governments pushing or encouraging central banks to allow a higher than normal inflation going forward?

I think it might, it's unclear, but I think it might because we'll have to pay for these COVID induced debts, somehow. We could raise taxes. That doesn't seem desirable because it could impede the post-crisis recovery. We could cut spending. That doesn't seem desirable either because it's not clear what to cut and we actually need to spend more on healthcare and things like that. So inflation might actually be the least bad option letting inflation exceed expectations, at least for a while, could reduce the real burden of public debt.

Would it be a bad thing to have some inflation after the pandemic?

Not necessarily. As long as the level is reasonable and under control. In fact, I have argued in a recent oped that some inflation could be desirable from the perspective of intergenerational fairness. Because inflation could shift the burden of debt away from those who are suffering the most from the lock-downs. When you think about it, the costs and benefits of government imposed lock-downs are not distributed equally across age groups. The benefits accrue, especially to the old whose mortality rates from COVID are much larger than for the young. So everyone benefits from the health perspective, just not equally. And in contrast the costs of the reduced economic activity are born largely by the young. As we know, millions of the young have lost their jobs, they've lost their businesses, whereas, retirees can not lose their jobs.

Let's not forget about students whose educations have been disrupted. So I would say that as a group, the old derive large benefits from the lockdowns, while incurring relatively small costs and the young derive somewhat smaller benefits while incurring significantly larger costs. That is perfectly appropriate. I think it's fine to have intergenerational insurance, to be fair. If we simply rolled over these COVID debts, then we would essentially be asking the young to pay twice. They're paying today by losing their jobs and all these sacrifices we've mentioned. And they'd be paying again tomorrow in the future because of the higher debt.

So if central banks and governments decide to let inflation run a little bit about expectations for awhile after the crisis, then, I think the burden might be shifted the other way, at least for a bit. The idea that is very simple because unexpected inflation hurts creditors and helps debtors and young people tend to be debtors. They tend to have large mortgages, for example. Older people tend to be creditors. They have paid off their mortgages and they tend to hold large amounts of amounts of bonds. So if you allow inflation to run a little bit about expectation, then it's going to be effectively a transfer from the old to the young, and that could somewhat at least, balance the transfers that have taken place.

Can you describe the puzzle as you described it in the empirical data and how the model that you developed explained this puzzle?

Yeah, sure. So the puzzle is that stock market returns in the US are higher under democratic presidents than under Republican presidents. I mean, much higher. The difference is about 11% per year since 1927, when good quality stock region data begun. In fact, the whole of the equity premium since 1927 has been earned under democratic presidents. And we're not the first ones to discover this. We're the ones who try to explain this. So let me give you a bit of a background. In 2003, two gentlemen, Santa Clara and Valkanov wrote this very nice paper where they documented the puzzle. Well, they documented the phenomenon and concluded it was a puzzle.

Quite often the financial markets phenomenon, that seems puzzling, turns out to be a fluke. You tested out a sample and it vantages. We added 17 years of data all the way through the end of 2015. And we found that not only does it not vantage, it actually becomes stronger. So it seems to be a genuine fact. And how do we explain this? Well, you might be tempted to find an explanation that's based on different economic policies of the two partners, right? You would have to argue though, that democratic policies are good for the stock market and Republican policies are bad. At first sight, it's not obvious, right? We attempt to think of Republicans as being the pro-business party that cuts taxes and reduces work regulations. And moreover, that explanation would require a lot of irrationality because investors would have to ignore this information.

So our explanation is different than our explanation is that it's not about what presidents do, but it's about when they get elected. We argue that Democrats tend to get elected in times of trouble when expected future returns are high and Republicans tend to win in good times when expected returns are low. Let me give you some examples. Think about 2008, we had the financial crisis and Barack Obama, a Democrat got elected George Bush, a Republican lost the election. Take an even bigger crisis in 1932, The Great Depression, a Republican Hoover was kicked out on the office and FDR a Democrat was elected.

You can look at more examples. You can look at Clinton, Kennedy Carter, and they were all elected either during or shortly after recession. So we don't think this is a coincidence. We think that there's a pattern here. In times of crisis, people are more risk averse. So they elect a party that they believe will provide more social insurance and that's Democrat. So we argue that when people are highly risk-averse, two things happen. They're more likely to elect Democrats. And on top of that, because risk-aversion is high, expected returns are also high going forward. Okay? So in crises, stock prices are low and expected returns going forward are high, and that's creating both of these things. So it's not that Democrats are somehow causing stock returns to be high. It's a high risk aversion that's causing those returns to be high and Democrats to get elected. That's our story and we're-

Should investors be worried about this election based on what you found in the paper?

Well, investors should be worried about this election for so many reasons. But I don't think economics is the only thing on people's minds. Right now, there are various non-economic issues that loom large when people choose between Trump and Biden. It's going to be really exciting to see who wins. But from the economic perspective, there are really two separate phenomena. One is the phenomenon that we've been discussing. The fact that under democratic presidents, expected returns, more average returns have been significantly higher. There's a separate phenomenon, which is how do stock markets respond to the election outcome.

I think that's closer to what you're asking and there's a nice paper by snowbirds, I think that's in 2007 QJE. They found that when a Republican gets elected, that tends to raise stock valuations by about two to three percent. So they looked at many different elections in the past. They found that electing a Republican tends to raise stock prices by two to three percent. And it kind of makes sense. Republicans tend to be pro-business. They tend to cut corporate income tax, to loosen regulations. These are all good things for business. But not that this phenomenon is much smaller in magnitude than the phenomenon that we address in our paper. We're talking about 11% per year. That's like 40 to 50% per presidential term, as opposed to a two to three percent stock price reaction at the election outcome.

You also have a paper about political uncertainty and stock returns. Can you describe what you mean by political uncertainty and what effect does it have on stock returns?

Yeah. So we model political uncertainty as uncertainty about what the government's going to do. Okay. Very broadly speaking. And that includes electoral uncertainty, because if you don't know whether Trump or Biden will be elected, you don't know what policies will be undertaken. We model this, we might actually, I think we build what seems to be the first model in which stock prices respond to political news and in which political uncertainties price. The main results are that when political uncertainty is high, risk premia are higher and the intuition is very simple. Political uncertainty is non-diversifiable. Anytime there's risk that's non diversifiable, it tends to price. So it shows up in risk premia.

Interestingly political uncertainty increases risk premia, but only in bad times. So only when the economy is weak. And the intuition based on our model is that it's when the economy's weak, that markets expect the government to do something. If things are going well, politicians can talk and markets don't really care because they don't expect governments to change. Why would government change things that are working, right? It's during crises that we know the government's going to feel compelled to do something, but we don't know what they're going to do. Some markets listen very much to what politicians have to say, because they're looking for clues as to what these future actions are going to be.

So our main result is that political uncertainty increases risk premia, but especially in weak economic conditions. And on top of that, it also makes stocks more volatile and more correlated. And we found some empirical evidence consistent with those predictions.

Another topic that comes up a lot is sustainable investing. Now, you had an excellent paper on this that we talked about. I think we've talked about a few times on the podcast, but the model in the papers suggest that green assets have low expected returns. And this is a big point of discussion with our podcast audience. Have low expected returns because investors enjoy holding them and because green assets hedge against climate risk. Could you just describe the model in the paper and how it arrives at those predictions?

And I have an hour. No, I'll try to give you a 45 second summary. So in our mind, it's a very simple model. It's the simplest model that we could think of for capturing the effects of sustainable investment. I was thinking of a textbook type of model. So we have farms and we have investors. Firms differ in their sustainability. So they range from green to brown. You think of green farms as being green and brown as brown. I guess I don't have to get into the details there. And investors differ in the degree to which they care. So some investors care a lot about sustainability and others don't care at all. Those who care, derive pleasure, not only from financial wealth, but also from a holding shares of green firms. And they dislike holding shares of brown firms. Okay. What happens in equilibrium is that there's extra demand for green stocks. As a result, green stocks are going to have higher stock prices and as a result, they're going to have lower expected return. So think of it this way, you have two streams of cash flows. One is green, one is brown.

If investors want to hold a green stream of cash flows because they derive pleasure from it, well, then the price of that green steam of casuals will be higher, which means that you're paying more for the same stream of cash flows and that means the expected return is going to end up being lower. And then the second channel, which you've mentioned is that green stocks seem to be a better hedge against climate risk. And that's why they can offer lower expected returns. Sometimes when I talk to students or practitioners, they make the following point. They say, "Well, I expect green firms to do better, because they're not exposed to all these ESG risks."

I'm always surprised by that argument, because if it's true that green firms are less exposed to ESG risks, well, that means they're less risky. That means that they can afford to offer lower expected returns.

And still be attractive to investors. Right? And if you say, brown firms are more exposed to ESG risks, that's true, but that's precisely why people are willing to pay less for brown firms. So that going forward, brown firms have to compensate investors for this ESG risk. And I think the channel, the channel we emphasize in the paper through which this happens is that if we a bad climate shock, okay, suppose there are wildfires in Australia or something, governments will feel compelled to act, and they will pass regulations that will favor green firms and penalize brown firms. So essentially what happens is that in these states of the world, in which climate gets worse, and these are bad states of the world, we want something that hedges against those bad States of the world, right? But what happens in these bad states of the world is that green firms will do well and brown firms will do poorly. So, it's effectively green firms are going to have a negative beta with respect to climate shocks. They will be the hedge, whereas brown firms will be exposed to this.

Precisely. As long as investors are rational, as long as investors truly care about ESG risks, and they understand what's going on, then yeah, they will pay more.

And I find that like a touch arrogant. It's like saying, "I understand this, but markets don't. Warren buffet doesn't understand this. EQR doesn't understand this." I find it simpler and more humble to assume that these risks are already embedded in prices.

Now, having said that it's not clear that green firms must underperform over all periods, right? So in the long run, our model makes this prediction. But you can have shorter periods of time in which enrich green firms outperform. In fact, in our model, we derive what we call an ESG factor, which captures shifts and tastes during a period in which these tastes shift unexpectedly. In particular, during periods in which investors tastes shift towards green firms and or customer stays shift towards green products, as long as these shifts are unexpected, green firms will outperform brown firms. Because green funds have positive betas with respect to the ESG factor. Brown firms have negative betas. So if the ESG factor has a positive realization, then green firms will be pulled up and brown friends will be pulled down. But of course, this can only last for long as these tastes are shifting unexpectedly.

It can go on for as long as there are unexpected shifts. I actually believe we have lived through such a period in recent years. I couldn't have imagined 10 years ago, how much people would care about our planet and the environment. I'd sounds very happy about this, just I didn't foresee it. So I would say that there has been an unexpected shift in tastes towards green products and green assets. And for as long as this shift lasts, unexpectedly, things are good, But note, it's not enough to say, "Oh, going forward. We know people will migrate from fossil fuel cars to electric cars." That's not enough because that's also embedded in prices, right? We all know that there's a move towards electric cars. The question is, is that move going to be faster than what the market expects or not. If it's faster then green firms will continue performing well. If it's slower, then the opposite will happen.

When you model, does sustainable investing result in a positive social impact?

It does. It does. We have a very simple definition of social impact. Essentially. It's a product of the firm's greenness and how big the firm is. We show that sustainable investing has positive social impact for two reasons. One is it makes firms greener. So firms choose to become greener and interestingly firms choose to become greener not because managers somehow care in our model, managers simply want to maximize market value. Okay? So our managers are like Milton Friedman managers. They want to maximize market value. And yet they make their firms greener because that's the way to increase the market values of their firms.

So that's the beauty of this. You don't need any engagement, don't need any activism, don't need any proxy voting, even without them, sustainable investing will make firms greener. And then there's a second channel, which is a cost of capital channel that in our model, sustainable investing pushes up the cost of capital for brown firms and reduces the cost of capital for green firms. So some projects that would have been positive MPV for ground firms are no longer positive MPVs. So there will be less investment by brown firms and vice versa, there will be more investment by green firms. That's a second channel.

It's a financial trade off. You can still be happy, right? Even though your expected return is lower as a green investor, you do derive pleasure from having made these investments. Otherwise, you wouldn't have made them in the first place. If somebody else is a brown investor, they may earn a little bit more wealth, but they don't derive the pleasure that you are deriving. So it's not obvious that they're happier, not at all.

Now, in the model, there's this definition of green firms and brown firms, which is very clean. But in practice, there's this massive dispersion in what constitutes a green asset, even within or across ESG rating agencies, there's no common definition of what is green. How does that reality affect the models predictions?

In the model, we just assume that each stock comes with an ESG score that everybody knows, and everybody agrees on. And that's the beauty of doing theory. If something in life is inconveniently messy, then you just assume it away. But more seriously, if a firm is considered green by some and brown by others, then I think it's a wash and there will be no effect unexpected returns in our model. Because think about it, if you think of a firm as green and you tilt your portfolio towards that firm and I think of it as brown, because I have a different ESG score provider, I tilt my portfolio away from that firm and it's going to be awash. So, essentially I think what matters, what ends up mattering some sort of an average score across these providers. I haven't thought through the possible convexity there.

That would make the effect on expected returns go away, but it would also make the social impact go away, theoretically.

Indeed. And if you want more social impact, it'd be good to get some convergence in these ESG ratings. If everybody understood that this particular farm is truly good for the environment, then that would be great, but it's not clear that convergence is coming anytime soon, for environment, for the E in ESG it's perhaps possible. But for other, like for us, for example, it's close to impossible. You've mentioned we have elections today, you can vote for Trump, Biden. These people have very different views about S what seems socially, just to you, may seem socially unjust to me. One voter likes guns, another voter dislikes guns. So how do you assign ESG ratings to firms that sell guns? You're making a political statement. So I think it might actually be useful to have some polarization, even in ESG ratings. Some product differentiation could be useful going forward so that people who have different social views can find portfolios that suit them.

Is there a case professor where you can be both exercising your preference, your taste for green assets and also have long-term out-performance?

Well, not in our model. So we have a simple model in which the answer to your question is an unambiguous no. If you want that to happen, you would need to add some additional assumptions, which are not necessarily plausible, but in a risk-based model, you would have to, you need green assets to be riskier in some dimension, in order for them to offer a higher expected return. So if you can think of a good reason for green assets to be systematically riskier then that would be the assumption you would have to add. I don't find that particularly plausible.

In a behavioral model, if you wanted green assets outperform in the long run, I think you would just have to argue that there are some investors out there who don't understand that the green revolution is coming. How plausible is that? I don't know. I think most people do understand that there is growing demand for sustainability on the ground.

Politically, we are moving in that direction, but yeah, I think you'd have to assume that people don't understand that there's some confused investors out there. And with that assumption, green assets outperforming.

As long as markets are rational, that's the case. If there is a rationality, if some people don't understand that the world is going in the green direction, then yes, green assets can outperform.

Just for some context, we talked about leverage a while ago, maybe a year ago. We talked about it for the first time and talked about the Ayres and Nalebuff research on time diversification and how young investors, it's rational for them to use leverage because it actually decreases risk over the long run. Your 2012 paper kind of throws a wrench in that whole idea of stocks being less risky over longer horizons. So how in the paper, how did you arrive at this conclusion that stocks are not really less volatile in the long run, like the conventional wisdom thinks it is?

Yeah. So again, we have an hour, right? So, as you mentioned, there's this conventional wisdom that stocks are less volatile at longer investment horizons on a per year basis. And this wisdom is based on historical data. Historically stock volatility at the one-year horizon has been about 17% per year. The 30 year horizon has been more like 12% per year. So historically indeed long horizon investors have faced less volatility per a year than short horizon investors. But this result is based on historical estimates of volatility. What we argue in this paper is that investors making portfolio decisions should be looking into the future. They should care about forward-looking, not backward looking measures of volatility. Okay. So, that's the key point. We take the perspective of a forward-looking investor rather than a backward looking historian, if you will. A forward looking investor cares not only about the historical estimates, but also about the uncertainty associated with those estimates.

That's key. Because that uncertainty drives a wedge between historical estimates based on which conventional wisdom is based on and these forward looking estimates that we believe matter to investors. Because this uncertainty about parameter estimates is growing with the investment horizon. Okay. In fact, we show that our forward-looking measure of volatility, which matters to investors has two components. First, historical volatility, which conventional wisdom is based on. And second, this uncertainty about the parameters, especially about the mean of the return process, about the trend around which stock prices fluctuate. That second component is increasing with the investment horizon. The first component is decreasing. That's the conventional wisdom. The second component is increasing. When you add them up, you actually get an increasing pattern in forward-looking volatility.

So we do get long horizon investors facing more volatility than short horizon investors. And the intuition is that, why is this uncertainty increasing with the investment horizon? Because just think about uncertainty about the mean, okay. If I compute historical volatilities, I'm computing volatility around a known mean. I know that the historical average was let's say seven percent real. I know exactly what I'm computing fluctuations around. But the forward-looking investor is computing fluctuations around an unknown mean. Okay. And that uncertainty is compounding over time. Think about it this way. If you compound at four percent a year and I compound at five percent a year, there's been a little difference one year out, but there's more and more difference as we go further out. And you see how, if we go 20 years out, 30 years out, there's going to be a gigantic difference.

That's why this uncertainty about the parameters matters, especially at long horizon. So as an investor, looking to buy whole stocks over a long period of time, I actually face more uncertainty than I had thought, based on these historical estimates.

What are the implications then for someone managing the retirement account that has a long-term horizon? What in this means for their asset allocation?

What I've learned from this as an investor is that I should slightly reduce my stock allocation. Stocks are simply more risky in the long run than I had thought before I wrote this paper. It doesn't mean that for example, the target date funds are incorrect. It just means that stocks are riskier in the long run, than we had thought. Take target date funds because they are so popular nowadays, right? Young people have more invested in stocks than old people. There are two popular justifications for target date funds. One is based on human capital. We can talk about that. The other is based on mean reversion in returns. It's the idea that over the long run, stocks are less volatile than over the short term.

I like the human capital argument. I don't like the mean reversion argument. I think that mean reversion evidence is swamped by the uncertainty evidence that we document in our paper. So I still think it makes sense for the young to invest heavily in stocks. I think that makes perfect sense, but they should do it for the human capital reason, not for the mean reversion reason that is often put forth, as well.

What are the implications of the research that we're talking about right now for that concept that young investors should use leverage?

Well, if they want to use leverage, I recommend a little bit less leveraged than you would, just based on their argument. I think their point is different. They're making a valid point. I think we're making a valid point too. If you put them together, I think you get that young investors should be aggressive with their stock allocations, but maybe not as much as they would if they just conveniently forgot about parameter uncertainty. And also it's not for all young people. Right? I do want to bring back to the human capital argument because I think it's such an important argument here. Not everybody's human capital is super safe. So the traditional human capital argument is that if I have a safe job, then my paychecks are steady and they look like a coupon payments on a bond.

So then my human capital is effectively a bond. So as a young person, I'm sitting on this huge bond and any little financial wealth I have, I should invest in stocks in order to have a more balanced portfolio. I do think this is in the spirit of the study you've mentioned as well. Whereas if I'm already retired, I don't really have any more human capital left in the sense that I'm not collecting paychecks. So I should be heavily invested in bonds. I think that is a fair argument, but not all young people are like that.

If you're an investment banker, for example, if you're an entrepreneur, your human capital does not look like a bond and you should be cautious about using target date funds, because remember there are two reasons, two justifications for target date funds. One human capital, one mean reversion. And if the human capital argument is not on the table for you, and if the mean reversion argument is not on the table because uncertainty swamps mean revert. And as we show in our paper, then it's not clear why you should be going for target date funds. So target date funds are suitable for many investors, but not for everyone.

You had a paper back in 2013 that looked at the relationship between scale of these funds and active management. I'm curious if you've found any evidence of skilled active managers.

Yeah. There are various papers, including some of mine that do find evidence of skill. So active managers do appear skill, but there's a big difference between skill and performance. You can be skilled and yet you don't necessarily have to be able to outperform, right? I can be a skilled tennis player. And yet, if I only play other tennis players who are more skilled, I'm not going to perform well, right? I'm going to lose all of my matches. I'm not going to be able to beat the market. I think that, that's very important. People used to think of skill as performance as alpha, but skill is not alpha. Skill is alpha, adjusted for scale. That's how I think about skill. And in this paper that you've mentioned, we do precisely that we measure skill. We define skill as alpha adjusted for scale. And in particular, there are two types of scale and matter in investment management. There's the size of your fund and there's the size of your competition and our paper emphasizes the latter.

The first one, the size of your fund. This is what we call slammed level decreasing returns to scale. And people typically credit. There's just things said by that makes these ideas very clear. The idea there is that when your fund gets larger, it's going to be harder for your fund to perform going forward. That makes a lot of sense. We emphasize a different thing. We have a size that, what do we call it? Industry level, decreasing returns to scale. When the size of the industry gets larger, you're going to have more competition and it's going to be harder for everybody in the industry, including you to perform.

So then we define skill as alpha adjusted from both of these types of scale and we find that active managers are skilled and moreover their skill has grown over time. So active managers are increasingly skilled over time.

Can you talk about what do you mean by different parts of the market that are scaled? Is it large cap growth funds in the US for example, when you look at it differently than that?

So, because this was the first study that talks about sort of industry level scale, we just lumped everyone together. So we literally looked at the size of the active management industry as a whole, but you could do precisely what you said. You could break it up and segment is and argue that, "If I'm a large growth manager, what matters to me is the size of the large growth segments."

I'm a private equity manager. What matters to me is the size of the private equity segment. So, that makes a lot of sense. We just did the first natural thing. We just lumped everyone together. And like you said, it's not just skill that's increasing scale is also increasing, right? Because if I tell you active managers have become more skilled over time, the first reaction of many people would be "Well, does that mean their performance has improved?" No, it has not. It has not because their competition has become larger, stronger. It's like, think about athletes. Okay. So 100 years ago, if you ran 100 meter dash in 11 seconds, you would have won the gold medal in the Olympics, but the athletes have become more skilled today with the same level of skill you might win a junior, regional perhaps. But you're not going to place well in the Olympics with the same level of skill. So yeah, again, the key takeaway here is that skill is not performance.

If the active management industry now shrinks where we've got these highly skilled active managers that are skilled, more skilled than ever before, and scale is decreasing, does that mean we should expect to see actively managed funds start to outperform?

Yes, we should. The only question is when? But absolutely. So as money continues shipping capacity what's going to happen is that it'll become easier for active managers to outperform. Maybe more and more people are dropping out. Your competition is shrinking. At some point, if you're the only person out there picking low hanging fruits, your office going to be really high, right? So it's just a matter of when this happens. So I think as soon as we see a couple of years of out-performance by active managers in aggregates it'll be a hint that perhaps we have achieved that equilibrium state of the world in which investors should finally be roughly indifferent between active and passive. For a long time, we've been in a state where active as being too big, relative to passive. That's why it's alpha has been negative. Active has been underperforming because it's been too big, too competitive, too many people, too many skilled people doing all the good things.

What are the implications for investors? Should people be looking out for the time when active starts to outperform and then start shifting dollars away from passive? Or have we basically just arrived with the Grossman-Stiglitz Paradox, where as soon as soon as that happens, the alpha goes away anyway?

Yeah. I think what you said makes a lot of sense. I'm personally, in index funds and I'm waiting for that moment to arrive. When I do see a couple of years of out-performance by active, it'll be a sign that too much money has shifted from active to passive. At that point I think more investors perhaps myself included will reason, "Okay, now I'm leaving alpha on the table. I'm going to move some money back into acting."

Does that, does that action by its nature though, make the alpha go away again?

Yes. In our model, that's precisely what happened. So in our model, what you get in that paper you've mentioned. What you get is that alpha is inversely related to the fraction of all money that's actively managed. So as that fraction grows bigger, alpha gets smaller. As that fraction shrinks, which is the case we're discussing alpha gets larger and equilibrium is achieved when there's a sweet spot, where there's just enough money invested in inactive management that alpha going forward is about zero. It should be really slightly positive to compensate investors for the slight amount of risk in active management. I mean, residual risk, but if it gets close to zero, then investors will be roughly indifferent between active and passive.

Why do you think the active fund industry remains so big, given all this information?

You can tell a rational story or an irrational story. The typical stories are the ones based on investor mistakes. So perhaps investors don't understand how to interpret performance data, or perhaps they get confused by slick salespeople or by marketing campaigns, or perhaps everybody thinks they will be better than the average. That's the irrational story. I think some of that is going on. In the paper, you've mentioned though, we tell a rational story where even without anybody making any mistakes, active management industry is large. And that story is based on these industry level, decreasing returns to scale. Because people understand that if they move money out of that, if they move too much money out of the active management industry, they will be leaving money on the table. Because alpha of active managers is going to go up as soon as I move money out.

So it's just a matter of how much should I move out of active management in order to achieve a respectable alpha going forward. And what we discuss in the paper is that it's been very difficult to learn about the right amount. We take all data going back about 40, 50 years to the sale of the data back to the 1960s and we try to estimate to this, essentially the extent of returns to scale, we try to estimate how much alpha is going to shift when I move one dollar out. And it's very difficult. There's so much uncertainty about that estimate that it turns out it's possible to rationalize the current size of the active management industry, even with data we have. So it's not even clear that we are in the wrong spot right now.

You had a paper looking at the performance of active funds during the COVID 19 crisis, which when you listen to active managers, you would presume that this would be the kind of time where they would do well. So how do they do?

Not well. Yeah, you're right. I mean, you would expect them to do well because this was a crisis in which there have been some dislocations in financial markets. In bond markets, people have documented multiple dislocations. In equities S&P 500 dropped by a third in five weeks. The fastest descent ever. You'd think active managers be able to outperform, but no, we found that unfortunately they have underperformed their passive benchmarks, however you look at it. So about three quarters of them underperformed, the S&P 500. Now you might argue that's a high hurdle because there's a lot of big tech in the S&P 500. But even if you look at the style specific benchmarks, you find that significantly more than 50% of managers, active managers have underperformed their passive benchmarks. And by the way, that goes against a story that some people sometimes tell in order to justify active management.

There's a line of theory in academia that argues that yeah, active managers have underperformed over long periods of time. However, they tend to do well in times of trouble, which is when investors particularly appreciate performance. There's some theory on that. There's even some of that based on older data. Well, that theory does not seem to have held up well in the COVID crisis because active managers had the opportunity to perform well precisely when investors needed them to perform well. And unfortunately, on average, they were unable to do that.

Is there a reliable way ex-ante to select a good active manager?

Well, it depends on what you mean by reliable. If you're looking for a 90% success rate, then absolutely not. If you just want better than even odds, then there are multiple papers that propose schemes that you could use to select outperforming active funds. I actually have one of those papers. So with my co-authors Randy Cohen and Josh Coval, we have a paper called Judging Fund Managers by the Company They Keep. And essentially for each manager, we look at what kind of securities is the manager holding? In particular, if you're over-weighting the same stocks that Warren Buffet is over-weighting or more specifically that managers with good track records are over-weighting, we're going to call you a skilled manager. If you're under-weighting the stocks that again, the skilled managers are under-weighting, we're going to call you a skilled manager.

So if you're in good company, if your portfolio looks like the portfolio of others who have performed well, then we will call you skill. And then we sort managers based on our measure of skill, which we called delta. And we indeed find that high delta managers out-perform going forward. So, this is one way of getting better than even odds in choosing active managers.

It's how similar their holdings are to a manager that has been determined to be skilled?

Correct. There's a bit of a recursive feature here. So in the first pass, we basically just look at your track record, just look at your historical alpha to decide whether you're skilled or not. Historical alphas are very noisy. What ends up happening here is that our recursive procedure ends up amounting to judging each manager by the weighted average of the alphas of all managers. Okay. So your skill is not just your historical alpha. Your skill is a weighted average of alphas of all managers, where everybody's alpha is weighted by how close their portfolio is to yours. So what ends up happening is a lot of diversification, essentially. There's a lot of noise in individual track records, but a lot of it gets washed out and you end up with relatively precise measures of performance that had predictive power. And by the way, just last year morning started an update on our study. They call it the ownership lens and they found out of sample that this approach has held up. So we were glad to see that.

If holding similar to skilled managers determines future performance makes me think about papers like the Buffett's alpha paper that the AQR guys did, showing how you sort of systematically recreate Buffet's performance. In the case of you described, would it just come down to costs? If similar holdings to a skilled manager predict out-performance, you would want to find the manager with the most similar holdings, but also the lowest cost. Does that make sense?

Yeah, it makes perfect sense. There have actually been people who have tried to emulate Warren Buffet and do everything he does with a bit of a delay and charge a fee. In fact, I had a PhD student, Lukasz Pomorski now works for AQR and his PhD dissertation is very much in that spirit. He asks, "Is it possible to emulate the trades of top active managers just by trading the way they trade?" And his answer was, yes. Even if you act with a one quarter delay, do everything that they do with a bit of a delay, there's a bit of an alpha there, going forward. It's not large, but again, if you just want better than even odds, there's a lot of things that you can do around the margins.

The last topic we want to cover relates to the paper that you've written with the most citations, which is on liquidity risk, the paper is liquidity risk and expected stock returns. And then you also did not update last year liquidity risk after 20 years. The finding is that stocks with high price sensitivity to market-wide liquidity, which is a concept that's maybe hard for people to think about when market-wide liquidity decreases stocks with high liquidity, betas would have their prices decrease more, and you show there's a premium for these types of stocks. Could you talk about why the findings in this paper are significant to the study of asset pricing?

I think it matters because we simply show that there's a previously unrecognized risk factor that people should take into account. You guys know all about pharma fringe risk factors. We simply propose another one. Aggregate liquidity, and we show that it's priced. Essentially before we wrote our paper, the role of liquidity in asset pricing was viewed in kind of static terms. People used to think of liquidity as a static thing. One stock is less liquid than another. So the less liquid stock has to offer a higher expected return to compensate for that lower liquidity. What we brought to the table is a dynamic view. We think of how liquidity varies over time, and we made the simple point that when liquidity dries up, it's a systematic event, you cannot diversify away liquidity risk. Because when it dries up in one pocket of the market, it tends to dry up in other pockets too.

So, liquidity seems like a good candidate for a risk factor. Because first of all, you can diversify and second people care about it, right? Usually liquidity dries up in bad states of the world. You would love to have something that hedges against liquidity drying up. So then what we set out to do was construct a measure of market-wide liquidity. We constructed one, and then we computed betas with respect to this measure. So for each stock, we computed its liquidity beta. Just like you would compute the market beta or the size beta or profitability beta, you can compute the liquidity beta. And we found that stocks with higher liquidity betas have had higher average returns over a long period of time. This was from 1966 through 1999. So, that was our main finding.

And did those findings hold up when you updated the paper 20 years later?

Yes. And in fact, it's not us. Two other papers have updated ours. What happened was that this journal called the Critical Finance Review has commissioned two studies, two teams to replicate three papers and look on liquidity. And ours was one of them. Both of those papers, we were very happy. Both of those papers found similar estimates of the liquidity risk premium in sample. What was even more gratifying is that post sample sort of since 1999, which is when our sample ends, they found an even stronger liquidity risk premium. Okay. So, the liquidity risk premium remained significant even over a longer period of time.

What I also found interesting is that out of sample, our measure of liquidity captured the drops in liquidity in 2007 and 2008, pretty well. So you see big drops in our measure in 2007 and 2008. So the measure that was constructed before the financial crisis, wasn't able to capture the financial crisis, which I view as sort of a little bit of a validation as well. And it makes sense by the way, because it's during crises that liquidity betas become easier to estimate. Okay. So when I talked to my colleague about this, he basically told me, "Yeah, in fact it makes sense, but it's hard to find anything that loads on your fact. It's hard to find securities with liquidity betas."

The reason is simple. Because most of the time liquidity is sleeping, right? Liquidity is a factor that does nothing most of the time. You can go for years without liquidity doing anything. Now, that liquidity dries up, boom, and you have to use those liquidity crisis to estimate liquidity betas. If you just run the regression of stock returns on a factor that those realizations are zero, zero, zero, zero, you get nothing. During the 2007-2008 crisis, there was a lot of variation in liquidity. So liquidity beta estimates were presumably more precise. And that's why when we sorted on liquidity betas, we were able to get a genuine spread in liquidity risk, which indeed generated the same results as our originals.

Are current liquidity betas predictive of future liquidity betas?

Yes, they are. And that's an important feature of any risk factor. So in our paper, we have tables that show that if you sort stocks on liquidity betas, highly liquidity beta stocks continue having high liquidity betas going forward and vice versa. Many papers in the asset pricing literature, don't perform this test. So, I'm very glad you're bringing this up.

It sounds really important from an asset pricing perspective, from a portfolio construction perspective, what can we do with it?

I agree with you that, that Vanguard ETF does not capture our liquidity factor. As far as I know, there is no ETF that does. We do publish our factor online and we update it every year. So it is possible to use the data and many people do. Yeah, the fact that there is no ETF, you can just invest in, makes it a little harder to earn this risk premium. But I think of it as an opportunity, not a problem. Think about these risk premia that have been commoditized like value and size and momentum. It's easy to earn those risk premia, perhaps it's no coincidence that they haven't performed all that well, recently. Look at value, look at size, look at momentum, recently. Risk premia that had been commoditized should be compressed at least to some extent by investor demand, whereas risk premia that are harder to earn such as the liquidity risk premium, well has a better chance of persisting in the long run.

Are the premiums lumpy or the premiums kind of consistent over time and then you just end up with bigger draw downs when there's a liquidity shock?

It's the latter. The way our liquidity factor behaves is that it performs well most of the time, but it suffers a big draw-down during a liquidity crisis. So for example, out of sample in 2007, 2008, our liquidity factor performed very poorly. And that's exactly what you would expect. You would expect to make money most of the time. You're essentially selling insurance against liquidity draw-down. Your pay off pattern should correspond to that.

How do you define success in your life?

Oh, I wish it was a simple question. But in my case, I got to start with family. I'm very fortunate to have an amazing wife and four wonderful children. So if my wife's happy and my kids end up turning into good human beings, then I'll consider myself successful. At work, I just try to have something meaningful, something insightful, something constructive to say in my research papers, in the classroom, in policy circles. I think that ideas can be powerful. So, if at some point some people find some of my ideas helpful, I'll call it success.