April 23, 2019 NYC

Victor recently sat down with NYU Professor Aswath Damodaran to hear his views on some of the most passionately debated topics in investing today, from the rise of indexing and what it means for market efficiency, to the origins and theoretical underpinnings of factor investing, to why investors ignore momentum at their peril.

Aswath Damodaran is Professor of Finance at the Stern School of Business at NYU. He received his Ph.D. in 1985 in Finance from UCLA, and has been making important contributions to our understanding of finance ever since, earning myriad awards for his research and his teaching along the way. His excellence in the classroom has resulted in many teaching awards and has been voted “Professor of the Year” by the graduating M.B.A. class five times during his career at NYU. Professor Damodaran is the author of eleven books, including several widely-used text books on Valuation, Corporate Finance and Investment Management.

You can follow Aswath on his Musings on Markets blog, his website, his incredibly popular YouTube channel or @AswathDamodoran on twitter.

Victor Haghani: Going back 30 years to when you were starting out as a professor, do you feel that there has been a change in the makeup of market participants? I mean, have we seen a change towards people being much better trained in thinking about value? Do you feel like it’s tougher to beat the market today than it was 30 or 40 years ago?

Aswath Damodaran: Well, let’s start with the easy one: it is definitely more difficult to create a competitive advantage in this market, simply because areas of competitive advantage are slipping away.

I’ll give you a simple example: thirty-five years ago, if you were an investor, you had an advantage just being in New York City over being in Des Moines, Iowa. Why? Because the SEC offices were here, and if you wanted to look up a filing by a company, you could physically go to the SEC offices and check out that filing. You had a competitive advantage based on location. And if you worked at a major investment bank, you had access to a computer. Most people in the world did not – so if you had access to computing power and you had access to data, it gave you a leg up.

Now the investing world has become a lot flatter, especially in the US. I can’t think of too many competitive advantages that you would have at Goldman Sachs as an equity research analyst over some person sitting at their own computer. If you’re going to create value in this business now, you’ve got to think of what else you bring to the table. It can’t be that you have better data, it can’t be because you have a more powerful computer – it’s got to be something else, and that’s made investing a lot more difficult than it used to be.

VH: A lot of people are worried about the rise of indexing. Do you think that indexing has gone so far as to make markets less efficient? Robert Shiller has said that indexing is un-American, that we’re losing the ability to value and to price assets. Others have compared indexing to Marxism, arguing that indexing is worse. What do you think?

AD: Well, let’s start out by noting that many of these people who critique indexing have a very selfish reason for doing so – it’s taking away their living. And that’s for a very good reason, which is they’ve not been very good at what they do for a living and indexing has exposed that.

If I thought more of equity research analysts, I would worry more about indexing. If I really thought equity research analysts actually went out and collected information and did research and unearthed stuff about companies we did not know, then I’d be worried about indexing taking away that research. But unfortunately, that’s not what I see equity research analysts doing. They listen to management spout platitudes about the company, and mostly they take them at face value. They take an adjusted EBITDA, they slap a pricing multiple on it, they call it research. That’s not digging up anything about a company, so nothing is lost by those equity research analysts being pushed out of the business.

And let’s face it, most active investing is built on mean-reversion. It’s very lazy investing, there is no research that goes in. You just buy stocks with low PE ratios and high growth. Again, we have this vision of analysts as being people who dig for the truth – and that is still there. In fact, I would argue the payoff to doing research is probably greater with indexing than without it. I think there is this false vision of indexing becoming 100% of the market, and I refer people to the Grossman-Stiglitz Paradox proposed in their 1980 paper, On the Impossibility of Informationally Efficient Markets, which states that because information is costly to obtain, if the market were informationally efficient there’d be no compensation for obtaining the information needed to make it informationally efficient in the first place.

That said, there is a potentially dark side to indexing. It has made momentum much stronger, because the nature of indexing is you pile on to whatever’s going up.

VH: Gene Fama has said that momentum is the “premier anomaly.” Do you agree with him, and why do you think momentum has historically worked so well across so many asset classes, both cross-sectionally and in time series?

AD: Because it reflects the reality of pricing, in that the biggest factor in pricing is what other people are doing. Investing has always been a momentum game, at least on the pricing side, and it’s about momentum and momentum shifts. Pretty much all of trading can be summarized into those two groups: you can either be a momentum player or a player who detects shifts in momentum and tries to go against momentum just before it changes. So, all of trading is built around momentum or anti-momentum. When Gene calls it an anomaly, what he means is we cannot explain it using fundamentals. It’s an anomaly –

VH: Right. So is that to say that there’s not a good risk argument behind it?

AD: No, there’s not a risk argument – but that raises a broader question of how the pricing process can be very different than the value process. The pricing process is all about mood and momentum. On any given day, it is the biggest explanatory variable for why price is moving. It’s not that cash flows change, or growth rates change, or the price of risk changes – it’s just momentum shifts.

VH: It does feel like if you were going to base a trading strategy on any one thing…

AD: It’s got to be momentum. In fact, you cannot devise a trading strategy which ignores momentum. It’s impossible.

You can create an investing strategy that’s momentum-free – but that basically means you value something and then you sit there and pray and hope that, eventually, momentum fixes the gap for you. Even those people who believe they’re value players are far more dependent on momentum than they realize, because ultimately, for them to make money, the price has to move to its value. And that may require a momentum shift, which is what we call the catalyst, something that changes the momentum of the game.

VH: Do you think there’s a distinction between momentum – which has a clear definition and has been found to be very helpful in investing – versus return chasing, which has a really bad name and is often put forward as the reason that investor returns are so much lower than fund returns.

On the surface, both of them are buying something that’s gone up and selling something that’s gone down – but there’s got to be an important fundamental difference between the two things that allows one to be the best thing that you can do, and the other to perhaps be the worst thing you can do?

AD: Because, in a sense, momentum has a light side and a dark side. The light side is when you’re riding momentum, you make a lot of money. The dark side is, eventually, momentum does shift – and if your entire investing was built on riding momentum, and the momentum shifts, you can essentially lose everything you gain plus more.

I don’t have a problem with the return chasing, if you know when to stop. And I think part of the problem is if all you do is chase returns, and you don’t even think of it as momentum, you’ve forgotten that momentum does shift. That’s why I have more respect for pure traders than I do for portfolio managers who claim to not be traders who chase returns, and then say, “Look, I don’t play the momentum game.” If you’re going to play the momentum game, play it. Play it openly.

Return chasers are more delusional. They’re delusional because, while they’re playing the momentum game, they keep telling everybody that they’re not playing the momentum game, that they’re really investors. So what they do is they chase returns and they dress it up as a value strategy, that they’re doing it because of X, Y and Z, because these companies are going to be the forefront of future growth, etc.

If you’re going to chase momentum, just chase it. Be open about it. If you’re going to chase momentum, you’ve got to get the timing right, and the problem with return chasers is they don’t realize that.

VH: Let’s talk more generally about the world of factor investing – or smart beta, as some refer to it. Can you give us your perception of the history, and what Fama and French were doing back in the late 80s and early ‘90s, and how a few trillion dollars have come to be allocated to this type of investing?

AD: I think it’s interesting. There is no way that you could have sat in on Gene Fama’s class and walked out of his assessment of factors saying, “That’s a way of making excess returns,” because I can guarantee you Gene would not have framed it as such. He’d have said, “Look, we found price-to-book and market cap as factors that drove past returns,” and the way he’d have concluded would be something like, “That must mean our risk-and-return models are flawed, that price-to-book and market cap are proxies for risk, that this is not something you’re getting an excess return for.”

I like to think of the roots of factor analysis as following two different pathways. One is that when you find a factor, what you found is not a way of making excess returns, but it’s a missing risk factor that’s going to be built into your expected return analysis. The other school of thought is, if you found a factor, that’s a way in which you can build a portfolio and deliver – at least on the surface – higher returns and essentially you can attract more money.

And I think we go back and forth between these two groups, and sometimes I think we pick and choose what we want out of those. I think people have to decide what factors really are. Are they really just missing risk variables? The other school of thought basically says, “We’re going to assume that any factor that has delivered more than required is, in fact, something I can make excess returns on.”

But you can’t have it both ways, and it becomes interesting when you get a paper that treads in that grey area. One such paper, for instance, is the AQR paper on the size factor: that even though the small cap premium has disappeared over much of the last 37 years, if you screen it for really bad companies, what they call junk, then small cap companies still have excess returns. Now we’re dancing on the head of a pin, because if I really treat it as a factor in the spirit of Fama-French, there’s extra risk associated with it so here’s what I should be doing: when I value a small company I should first assess whether it’s a high quality or low quality company. And then for the high-quality companies, I should use a higher cost of capital than in discounting the cash flows for low-quality companies. That’s a really tough intuitive sell. That if I get a bad company, I should use a lower required return – but this is what happens when we don’t draw the line, when we use those factors to build this premium into a cost-to-capital. This is why I’ve never used the small cap premium in 35 years of valuation practice – because I think the minute you do that, you’re opening the door to including things in your cost-of-capital that really should not be included in there.

VH: If you were given a choice between either investing in an equity portfolio that was built around five or six of the most popular factors today, or you could invest in a portfolio that’s chosen by a hundred of your favorite valuation students selecting individual stocks, which would you prefer? Assuming all the costs are the same for both.

AD: I’m a great believer that the less activity you need to put into creating a portfolio, the better. To the extent that there are 100 different people involved, no matter what I think about them – I worry about all that activity that they did, and I’m not sure that those things are going to actually pay off in returns, because the good stuff and the bad stuff might all get averaged out. So given the binary choice I would go with the factors, but you know what? I’d go with a pure index fund over the factor one. Because here’s the thing about factors: they have existed, obviously, over the last hundred years. We can see it in the data…but I really think the world is shifting under us. There is a point to make about mean reversion: mean reversion works until it doesn’t. And much of what we do in investing now, we learned in the US on data from the second half of the 20th century. And in that time period the US market was a unique market. If you look at the history of markets over time, it was the most mean reverting, stable market of all time. And when you take the most mean-reverting, stable market of all time, all kinds of mean reversion are going to work for you.

So my concern is that maybe we’re taking rules that were developed for the most mean-reverting, stable market of all time and trying to apply them in a new world order where markets might be reverting, but we don’t know to what. And so, I have a concern with any kind of tilted approach where you’re tilting based on past data. I’m not sure the payoff is there. Maybe twenty years ago, my answer would have been different. For me, 2008 was the dividing line where I think there was a structural break in the global markets. I am less and less trusting of mean reversion on a daily basis.

VH: You write annually about long-term expected returns of the market as a whole. Can you give us a brief description of how you come up with your long term expected return for say, the US equity market, or the global equity market?

AD: I do it on a monthly basis, and I think again this goes back to what I said earlier about mean reversion. In the past the way I would compute those future expected returns was to look backwards: look at the Ibbotson data to 1926, and look at what stocks made on average over T-bonds, and make a leap of faith: if that’s what I made over the last 75 years, that’s what I should expect to make over the next seventy-five.

But as I said, so much of what we know came from the US in the 20th century, but starting about 25 years ago my faith in using historical returns started to get shakier and shakier, so I said we’d be much better if I could get a forward-looking expected return for the market. So I stole from the bond market an idea that’s been around forever: that yield to maturity is basically an internal rate of return. You take the price of the bond today, you take future cash flows, you solve for what kind of expected return you’re going to make, given what you pay.

So at the start of every month I take the S&P 500 and I look at what people are collectively paying for stocks. I do have to make projections of expected cash flow, but that’s not difficult because these are, after all, the 500 largest market cap stocks. So I solve for an internal rate of return every month, and that becomes my expected return for stocks.

VH: What do you think about what’s often described as the ‘Equity Risk Premium’ puzzle, i.e. that some econometric models suggest that the equity risk premium should be much smaller than it seems to be?

AD: I love Jeremy Siegel’s work, but I think his basic notion that ‘stocks always win in the long run’ is at the basis of this puzzle. Because if stocks always win in the long term, you know what should happen to your equity risk premium as your time horizon extends? It should go to zero.

We know stocks don’t always win in the long term, that there is this catastrophic risk. But then people point to the US and say, “Show me where it is.” You’ve got a survivor market, you take the most successful market of the 20th century and you ask me, “Show me the evidence of catastrophe.” You’re not going to find it. You’re going to have to go look at the Austrian market to find it. We think of one hundred years as a lot of data. But in the longer scheme of history, when looking at the US we’ve just caught a very, very unusual country in an unusual period of time, and we’re extrapolating from there.

VH: We only have time for one more question, so I’ve got to ask you: how you have been so prolific? Eleven books, I lost count of all the articles you’ve had published, a massive online presence, trying to get ideas and valuation techniques democratized, and hundreds of thousands of people reading and watching your teaching. And then on top of it, being a professor and getting all these awards for best professor at NYU, best business school professor in the whole country. It’s really remarkable, can you give any tips for people that are trying to be more productive?

AD: I have to tell you, I’m a pretty lazy person, I don’t work more than 40 hours per week. What I’ve discovered helps me is to not compartmentalize – because if I thought of my life as, “there’s teaching, there’s research, there’s writing on my blog, there’s X, Y and Z…” then you very quickly run out of hours in the day. But almost everything I do spills over into almost everything else I do. So I’m constantly looking for ways to take whatever I do and get it to serve three or four or five purposes.

I’ll give you an example: about five years ago I read The Wall Street Journal post on Uber. It was a Thursday afternoon, and I said, “This will be an interesting company to value.” I did a very rudimentary valuation, because I knew very little about ride sharing; it took me about three hours to do the valuation, about three hours to write the blog post. I put it up on Friday afternoon. That blog post took a day and a half of work, but it essentially became part of my classes, it became an entire seminar that I do on valuing young and startup companies, it became a book called “Narrative in Numbers.”

VH: Thank you so much for making time to share your clear and insightful thoughts with us.

AD: You’re welcome!