Episode 264: Pim van Vliet: The Volatility Effect, Revisited
Pim van Vliet is Head of Conservative Equities and Chief Quant Strategist at Robeco. As Head of Conservative Equities, he is responsible for a wide range of global, regional, and sustainable low-volatility strategies. He specializes in quantitative investing, asset pricing, and quantitative finance. He is the author of numerous academic research papers including publications in the Journal of Financial Economics, Management Science, and the Journal of Portfolio Management.
Pim is a guest lecturer at several universities, author of an investment book and speaker at international seminars. Pim joined Robeco in 2005 and holds a PhD and a Master’s cum laude in Financial and Business Economics from Erasmus University Rotterdam.
Pim van Vliet is on a mission to put the low volatility factor on the map. In his role as Head of Conservative Equities and Chief Quantitative Strategist at Robeco, he focuses on leveraging the effect of low-risk investing. Pim has also published a book, High Returns From Low Risk: A Remarkable Stock Market Paradox, where he unpacks some of the key aspects that guide his work and underpin his success. During this conversation, Pim shares his insights on volatility, the changing market, and combining low-risk with other traditional factors. He equips listeners with key considerations for evaluating strategies or products when allocating low-risk and offers his perspective on out-of-sample-testing, distinguishing between global-factor and cross-sectional premiums, and more. Listeners will get Pim’s perspective on the pros and cons of the Sharpe ratio, and we examine risk-adjusted returns on long and short legs before hearing his Fama-French Five Factor Model analysis. We touch on inflation and gold, and finally, Pim shares his inspiring perspective on success in his financial and personal life. Tune in today to hear more!
Key Points From This Episode:
Introducing Pim van Vliet and his mission to put low volatility on the map as a factor. (0:00:41)
Defining the low-risk effect with reference to volatility and its impact on other asset classes. (0:04:47)
Low-risk portfolio performance in relation to the changing market. (0:12:02)
Combining low-risk with other traditional factors. (0:21:43)
Considerations for evaluating strategies or products when allocating low-risk. (0:24:35)
Out-of-sample testing. (0:31:28)
Distinguishing between global factor premiums and cross-sectional premiums. (0:35:18)
Weighing the pros and cons of the Sharpe ratio as an evaluation tool. (0:40:19)
Examining the risk-adjusted returns of long and short legs. (0:41:20)
Issues with the Fama-French Five Factor Model. (0:44:37)
Why factor premiums vary through inflation regimes. (0:50:41)
How an allocation to gold holds up as a downside hedge. (0:52:53)
Pim’s definition of success in his life. (0:56:31)
Read The Transcript:
Ben Felix: This is the Rational Reminder podcast, a weekly reality check on sensible investing and financial decision-making from two Canadians. We're hosted by me, Benjamin Felix, and Cameron Passmore, portfolio managers at PWL Capital.
Cameron Passmore: Welcome to episode 264. And then one of our goals this year was to bring back the basics for our audience. This week, I think we're going back to the red meat for the people who do appreciate something a bit more tactical. However, great insights, I would argue for anyone. We've talked about factors a lot.
This week's guest is Pim van Vliet. And his mission, he wants to get low-vol, low-volatility as a factor on the map. And he is so articulate. Such a nice guy. Makes a pretty compelling case. And then we also dove into other items in the discussion. Really good discussion.
Pim is the Head of Conservative Equities and Chief Quant Strategist at Robeco. He holds a Ph.D. and a master's in financial and business economics from Erasmus University in Rotterdam, which is where he joined us from. He's also the author of a popular book on low-risk investing called High Returns From Low Risk: A Remarkable Stock Market Paradox.
Ben, you got to tell us the story behind this conversation.
Ben Felix: Listen it's another story of – I was familiar with Pim's research and it came up enough times that it was time to have him on the podcast. He had just come up with a new paper on gold, which we did talk about at the end of this conversation, which is a great paper. But since I'd seen his name so many times before that, it just seemed like, "All right. Let's see if he'll come on."
And as much as it was that gold paper that got me to reach out, regardless to have him on the podcast, it's the low-volatility work or his low-risk investing work. That's where he spent a ton of his time. He actually told us at the end that the gold paper was their attempts to diversify their, "Let's not do another paper on low-risk."
This work on low volatility is really fascinating. The way that they've looked at it empirically in historical data. They've done some kind of like what we had with Scott Cederburg, where he's really done some sort of archaeological work on getting pre-sample, out-of-sample data, to look at factors in general, including value momentum. But also, low-risk. That part of his work on low-risk is fascinating and on other factors as well.
But they've also done some really interesting research where they pull apart. Because academic factors are long-short portfolios. They've done a paper where they pull apart factors and look at the long leg and the short leg independently and ask how that affects the characteristics of factor portfolios. And that's relevant for investors because most investors are not investing in long-short academic premiums. They're investing typically in long-only portfolios that tilt toward a factor.
Lots of interesting insights related to that. But we also talked about factor investing more generally because he's done a ton of work on that. Really interesting empirical work with both the pre-sample paper that I mentioned on the cross-section of returns. But they've also looked at global doctor premiums, which is instead of looking at US stocks and finding the cheapest stocks within the US and seeing how those perform, they look at, for example, the cheapest country or the country that has momentum. And so, you can look at premiums from that perspective. That paper is in the Journal of Financial Economics. Also very interesting.
And then, he's got a couple other papers. We talked about the gold paper and we talked about a paper that they've done on inflation regimes in the Financial Analyst Journal. And that looks at both asset class premiums, like stocks and bonds. But it also looks at factor premiums through different inflation regimes. Just fascinating. A really nice collection of research. But the low-volatility stuff really gets you to think.
We've had a couple of guests who had, I would say, pretty strong views on low-vol. And Pim has polar opposite views to those. But very well-informed, which I think – to the collection of information that we've presented to people on this podcast, I think this episode adds a lot.
Cameron Passmore: Good to go?
Ben Felix: Yeah, I think so. In practice, maybe I'll just mention, for Pim's job, his research and practice is specialized in low-volatility investing, asset pricing and quantitative finance. He's got a ton of papers, which we discussed today. Published papers. And some great working papers as well. He's also a guest lecturer at several universities.
Again, I think this is a really nice conversation to include in a collection of information that we've presented to our listeners on factor investing. And this gives us a whole new perspective on low volatility.
Cameron Passmore: Terrific. All right, with that, let's go to our conversation with Pim van Vliet.
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Pim van Vliet, welcome to the Rational Reminder podcast.
Hi. Nice to be here.
Pim, to start off, what is the low-risk effect?
The low-risk effect is a big paradox as we call it. It is the fact that, over the long run, low-risk stocks have higher returns than high-risk stocks. That's a puzzle which haunts me basically my entire career. The question is how can this be?
And when we're talking about low-risk, how is risk being measured?
Yeah, that's a good one. The risk can be defined in multiple ways. The volatility effect is one of the papers I wrote. In that sense, you can sort and define risk on past volatility. But you can also sort and define it as market beta. That's systematic volatility.
If you take a few steps back, then you could say these definitions of risk give you similar exposure to similar stocks. But they tend to be defensive. And of course, there are differences between the two. But overall, you can define it in different ways, mostly on statistical fluctuations of the stock.
How much lower risk is a low-risk portfolio?
In general, it's difficult to get your risk out completely because you're still low in the equity market. But you can get your risk down by about one-third. The beta can go to 0.65. Volatility can be reduced by 20%, 25%.
Crazy. Now there's this anomaly or premium. How does the magnitude of the low-risk premium compare to other premiums like value or profitability?
You could say it's the biggest. And it's a pretty big claim so let me explain this a little bit. First of all, it's not the risk premium. In finance, when we find premiums, some people say it's compensation for risk. Others say it's maybe data mining or it could be behavior. These are basically the three main explanations.
With low-volatility, you get a higher return than high full stocks and a bit similar than the market. The outperformance first to market is like 1%. It's not that big. But if you do this beta corrector, so you long low vol and then you lever up because it's low vol. You bring it to market risk. And then you short high-vol. Then a bit less because it's high risk. You do this in a beta-neutral way. And that's also popularized by the betting against beta paper. That's also where they do this technique.
And the beauty is that you then get a [inaudible 00:07:09] premium where you take this risk out. It's long low-vol. Let's say 1.5 long. And it's short 0.5 high-vol. What you then get is a premium of about 8% per annum with pretty low risk. The Sharpe ratio is one of the highest. It's higher than HML. It's higher than S&P and the other [inaudible 00:07:32] and quality.
The momentum is at par. But the problem with the momentum factor is that it's very difficult to implement. And so, academics usually ignore implementation. Usually ignore transaction costs. That's something often ignored I think for the wrong reasons because it's very important to translate growth alpha into net alpha.
The volatility premium is basically the biggest if you also consider transaction costs and implementation costs if you take that into account. And then the evidence, I think 90% of all academic studies on pure cost pricing focus on US stocks. And, of course, it's a big market. But there's also international stocks, emerging market stocks. And also, other markets like corporate bonds is also a huge market.
If you didn't test the low-risk effects, also international equity – and we did in our '07 paper in Journal Management where we also saw that this premium is very big in Japan and Europe. And then Japan is interesting. Because in Japan, we know that momentum has problems in Japan. That's the exception of the momentum premium. It fails there.
The same in China. In China, momentum is not working that well. While the volatility premium is very big there also in China. It's a very consistent international premium. And it also works across sectors. We know that value has problems as a factor across sectors. Volatility does not.
It is the biggest. Also, one of the most consistent and robust premiums out there. And on top of that, academics have most trouble getting their head around it. Because, if anything, it cannot be a risk premium because you short on risk.
How pervasive is a low-risk effect in asset classes other than stocks?
Yeah. So the lower risk effect is pervasive as shortly mentioned the evidence for international equity markets. You also see it to be present in bond markets. One nice way to look at it is a look at the term premiums or the yield curve of bonds. You already see that the slope is non-linear. Usually, it's upward-sloping. At this day, it's even inverse.
There you also see that, for each unit of duration, you get a lower compensation. And in, you even get a negative compensation nowadays. That's in one of you, you see the low-risk effects to be present in the government bond markets.
If you go to corporate bond markets, you also see that if you sort on rating, you see that the Sharpe ratio of AAA's is better than the Sharpe ratios of BBB's. Also, within corporate bonds, you see the same. Of course, rating is just one way to look at risk there. So you can also look at the spread volatility to see which bonds are more risky than others. It's also there. There's a maturity effects and a low-risk effect in corporate bonds.
Then, it's a bit funny. It's like when your wife is pregnant, you see suddenly pregnant women all round. If you bought a red car, you see red cars. It's the same with the low-risk effect. Once you see it, you see it everywhere. And even, also, we did quick tests on cryptos. Also, on crypto, you see a low-risk effect. You see it in the betting markets. If you go to the horse race, the long shots give you a lower return. It's amazing to find an example where it's not working and that low-risk effect is not there. There's, yeah, massive evidence. That's fascinating because it raises many questions.
You touched on it exists in other asset classes. It's geographically persistent. How persistent has it been through time?
From an absolute point of view, you see the low-risk stocks. If we go back to US stocks, you see that if you go back to the 1860s, that's 150 years, we've created maybe the longest ever that we extended even the CRSP database. And there we see that, in each decade, low-risk conservative stocks, as we call them, outperformed speculative stocks. It is very time-consistent. We say never a lost decade. And every decade, you also beat cash.
However, if you look at the markets, the market's portfolio, there can be periods when even over long stress periods that the market outperforms low-risk stocks, that can be a decade stretch. Still at a higher risk. But, there, you need the leverage components to really see the premium coming back. Over time, it's consistent. But sometimes you need either leverage or some patience to really outperform.
How does a low-risk portfolio perform relative to the market in up and down periods?
That's a simple rule. You could say if the market is up, you tend to lag. And when the market is down, you tend to outperform. Very simple. If the market would be purely fully efficient, this would always be the case.
Here's the trick. The money – what you outperform in the down markets is more than what you tend to lack in an up market. And overall, that's where you can outperform the markets. But it doesn't feel like out-performance. Because most of the time, markets go up on a day or on a monthly basis. It's like 60%, 70% up. That means that the feeling of outperformance is not there. Most of the time, you're lagging.
On top of that, you also are not happy when you outperform in a down market because you're still losing money. You see? It is really emotionally a tough trace to do, low-volatility investing. Especially, because what you say to payoff pattern is asymmetric. People like a positive view and where you can be rich [inaudible 00:13:08] lottery tickets kind of payoffs. This is the opposite.
It's like, many times, you're losing a bit. And then when the market goes down, you outperform but you're still losing. It's really only when you look back over 5, 10, how look at your wealth, and you look only infrequently, then your well-being shoots up. But if you look at this at a daily basis, that it doesn't feel very good.
Interesting. You may have just touched on the answer to this question. What are the theoretical explanations for the low-risk effect?
I think there are many. This is one. If you formalize this, what I just said about how emotionally tough it can be, we have benchmarks which are very useful to see how is a manager doing relative performance. However, it has become a normative starting point for any strategy, which means your performance is driven by relative performance. And that means the tracking error is not good.
To give an extreme example. Suppose I could give you a stock which goes up every year by 10%, for sure. Still, the stock would have a huge tracking error compared to a benchmark. Because if the S&P is up 30%, we're lagging 20%. And you might be unhappy.
If the market goes down minus 5, of course, you're happy with the ten. But the interesting thing is that tracking error is something really tough. And in that sense, we have institutionalized this. Because 40 years ago, benchmarks were less important. And nowadays, they have become more important.
In fact, information ratio is the key objective for many strategists. Information ratio is outperformance per unit of tracking error. If you take the low-risk anomaly and you say, "Now, there's a little bit of outperformance. Now, there's a lot of tracking error because of this payoff pattern," that means the information ratio is very low. It's like close to zero, which means that if you have a multi-factor strategy and you add low-volatility, your information ratio tends to go down. You add lots of risk, relative risk, to your strategy.
There's serious limits to arbitrage to this. That's one important explanation for why it exists. A second one is we're drawn to risk. We live in extreme. Either we put everything in cash and savings or we put everything in one stock. And where is the middle? The much priced virtue in the middle is often lacking. Many investors on the stock markets have different objectives than high volatile capital growth and capital protection. They want to get rich quick. And then it's not wise, not smart, not rational to invest in low vol stocks. It wouldn't make you rich quick. You know for sure you're not going to hit [inaudible 00:15:48] if you go in low-vol stocks.
From that perspective, it's an anomaly which we tend to understand pretty well. There are many rational explanations for it, which means rational. But they should teach people about it that it's still there. Many institutional clients we serve, they are tracking our constraints. They know about the low vol anomaly. And still, they say, "It doesn't fit in my tracking arbitrage." I will only go for value, momentum and quality. And I'll leave low vol on the table. And that's fascinating. Because if you don't have these benchmark constraints – and, often, retail investors, they're in advantage, you can profit more from this anomaly than if you're a sophisticated institutional investor.
In fact, that's also a fascinating result, hedge funds. You would expect that hedge funds don't have a benchmark. They have cash. You would expect that they could profit from this anomaly. They want a good, absolute return.
And what's striking finding, we found that hedge funds are betting against low vol anomaly. So there are long high-vol stocks, short low-vol stocks. Exactly the opposite. That's fascinating. And also, understandable again. Because if you're a hedge fund and you say, "Hey, I buy some low beta and I hedge it," which that people could say, "I don't pay 220 for that." It is not rational or understandable to do that.
Many explanations for why it exists, that also makes it going forward one of the biggest alphas for the future. Because as researchers, as investors, we look at the past. As I said, even going back 150 years. You can also look back at this year. The future is uncertain. And it could also always be that factors have been arbitraged away for this low-risk factor. And we can also touch on the other factors later. It's pretty sure that it's not arbitraged away. In fact, it's more undercrowded nowadays that it's overcrowded. And the reasons for why it's like that are, as I said, understandable and rational.
We've had past guests argue that the low-risk effect is subsumed by the factors in the often-cited Fama-French five-factor model. I'm sure you have a response to that.
It's good. There are factor fives going on. We started with one. And then we had three. And then we got five. More and more factors are added. And then the question is which factor explains which one? I think the most serious critique could be about profitability being related to low-volatility. And then it's interesting to respond to that, that if you do a long-short analysis, you find that quality or profitability and low-volatility are related and explain each other.
However, that's done in a long-short analysis. Basically, 90%, 95% of all academic papers work with long-short investing. Well, in practice, more than 90%, 95% of the money is managed long only. What we did is we took a look at this critique and we said that factors drop their shorts as the paper is called. And what we found is that, on the short side, so the high volatility stocks, are highly correlated with what they call junk stock. Improfitable stocks. These explain each other.
Quality can explain high-volatility in the other way around. However, on the long side, the long leg, which for most investors are putting their money, it's not the case. Low-vol stocks are not necessarily high profitable firms. And there you see that if you combine those factors, they support each other and complement each other. That's a big critique. But it's not even a critique to this finding. But also, to the fact that most asset pricing studies blankly assume that we can all short stocks and even short highly illiquid small stocks. But in practice, it's really hardly possible for most investors. And that's something, a challenge for the academics to bridge that gap. Because this is a serious issue. Because it could mislead investors if you have such a long-short paper that is not necessarily translating to a long-only space where most investors are. That's would be my response to this.
And finally, it's also about how many factors do you throw in if you say you take Buffett's strategy and you throw 20 factors at it? And then you say, "I can explain it with 20 factors." However, in practice, can you do 20? And why those 20? That's also a critique if you explain.
One rule should also be about simplicity. The beauty about the low-vol effect is that you don't need accounting data or whatsoever. You just look at observed price changes. So you don't need comp your stats. You don't need accounting standards. Also, easy to compare across countries and through time. And it's also something you cannot quantify. But low-vol is simpler than other metrics. With quality, you kind of operating or caching profitability. Can you compare it with Chinese quality stocks? Can you compare quality back in time to the 20s? All those kind of questions pop up. Where a full volatility, this is not the case.
You mentioned earlier that adding low risk to a portfolio can reduce its information ratio, which is maybe more of a commentary on the information ratio than it is on low-vol. But does it make sense to combine low-volatility with other factors?
Sure. It depends on your objective. So if your objective is to have capital growth for your clients and also capital protection, so the twin objective, put forward by any utility or prospect theory, this is normative. Then it makes fully sense to do a multi-factor approach and have low-volatility in the factor mix. The information ratio is a secondary object. It's an in-between objective. And it's fine. It works in most cases. However, not for this one, for this particular factor.
You could combine low-risk with other traditional factors, or value and profitability, or quality and all that kind of stuff?
Yes. In a long-only setting, as I said, you see that low-vol adds to value. They're not the same. Low-vol adds to quality. And low-vol adds to momentum. You could say these are the big four. Combining them makes very good sense. Then the question is how much weight should you give to all of them? A good starting point is to equal weigh them. Then you can say, "Hey, I want my turnover to be a bit lower." And then you give a bit with less weight to momentum.
If you want to go maximum Sharpe so you don't do one over n where you say, "I've got four factors." But you say, "I want the highest Sharpe ratio," then you should lean into low-vol bit more. So then you create a defensive multi-factor strategy.
And one of our studies is on the conservative formula, where we basically mix a couple of factors. So very simple three. And then you get exposure to all the big factors out there, which is S&B, HML, RMW. Excuse my acronyms for your listeners. But the size, value, momentum, quality factors are all captured with one simple strategy. In everything I do, we do, we believe to use multiple factors and don't go single. You can vary the mix. And you can also vary the complexity and the simplicity.
I don't think you need to apologize for the acronyms. Our listeners are fairly well-seasoned in especially those acronyms. I just want to come back to what you talked about earlier with the Fama-French factors and the long-short stuff. Just to reiterate for listeners, in a long-only setting, low-risk is distinct from other factors and adds something to a factor portfolio. In a long-short setting, shorting the junk is sufficient to get something similar to the low volatility effect. That only works in a long short setting. In a long-only setting, low risk is a distinct factor. Is that right?
Yeah, it is right. To add on the short, it's pretty – easy is not the right word. But it's easier to detect a short [inaudible 00:23:46] because they tend to be the same. They tend to be high-volatile. They tend to be unprofitable. So if you have one characteristic, basically you've got the others as well.
On the short side, if you take high-volatility, then usually it's also impossible. And then the problem comes in that the short candidates factors like, like losers, high-vol and profitable, they tend to be pretty risky to short. Because on the short side, it's asymmetric. If something shoots up 400% and you're short, you can lose more than your principles. That's even trickier. So you can rely less on diversification of factors on the short side. And you should be more careful when you do shorts. That's also the reason why most investors don't enter the short game. That's a good summary.
Speaking of most investors, let's assume a listener decides they want to allocate a low-risk. What should they consider in evaluating different strategies or products?
That can be with three ways. Either if they have their own portfolio, they can screen. They can take a look at the beta of their stockholdings. Especially watch out for the most cyclical ones. Beta of 1.5, 1.3, be careful. And then you can already implement alpha of low-risk strategy. And then you can push this further and say, "Hey, I'm a hardcore believer. I throw out everything with the beta above one. I fully go defensive." That's one way. And then you can add screens on top of that, like value or quality. All kinds of screens you have.
I spoke with quite of my investors who work with screens and do it yourself. And they usually didn't have the risk screen. So that everything, that like tens, 20, 20 screens. But not risk. That was interesting. That's also why I decided to write a book for the broad audience to bring low-risk as a screening factor more top of mind.
Also, some screeners even didn't have a risk screen. There's also information. That's one. Do it yourself. You can screen low-risk. Two, if you can say, "Hey, let's buy an index, an ETF," there are pretty good low-vol ETFs out there. So I started very early on, '08, just before the global financial crisis. But also, S&P has good ones. That's two.
And then three, you can also go for an actively-managed [inaudible 00:26:04]. Could be ETF or mutual funds. And what you dare then get is usually a blend of factors in a defensive strategy. You get a multi-factor defensive strategy. These are three ways to do it.
I want to move on to factor investing more broadly. We've touched on that. You mentioned your paper looking at the cross-section returns back to 1866. How did you assemble the data for that paper?
First of all, it's a joint effort. As you might have seen, all my papers are collaboration. And this was with a professor in behavioral finance. And Bart van Vliet. Same name as me. Not family related. Credits to him. And also, other students and interns before him.
What we did, we started with a global financial data. There you have stock returns. That's doable. And you have to really clean this. But you can do this in a systematic way. But then the issue is we had to hand collect for Bart and the others the market cap values of the stocks by looking at the shares outstanding.
Going through the newspapers one-by-one. They don't change every month. How he did is let's look at intervals and then see if the number of shares outstanding changed. If it didn't, you would know that they were constant. By having the outstanding shares there, we had two things. We had market caps. And that's very important when you do asset pricing studies if you offer micro caps distort results. Lots of things going on there. And not really representative. You need market cap. That's what we hand-collected and added to the global financial database. And therefore, we added 60 years of new data in a cross-section.
To recall Eugene Fama and French with their big seminal paper, they had about 30 years of data when they found size and value to be factors and beta not to be working. This has been confirmed out of sample, pre-sample. But it was only 30 years of data.
With this 60 years of data, it opens up whole new opportunities with, as I said, market cap values and also share issuance. Because we knew the shares are standing and that's also a factory you can then test.
Which factor premiums did you look at?
So we looked at the common factor premiums for which it's, for some people, clear that they are positive and significant. However, for others, they're not. We looked at the main factor premiums, which is value, momentum, low-risk and also size.
But for size, we know that there's debates. Whether it's a factor or not. Because out of sample and post bonds, the results have been mixed. Not very strong. The consensus view is that size is a catalyst factor. Other factors were better for small caps. But standalone, it's not that strong. It's a good way to test. Is there a size effect in this markets? We did have market caps. The answer was, again, not significant. Small but not significant. So very much in line with 20th century.
Then momentum, strong one. There was already some evidence. Other papers looked at momentum but then equal-weighted. Because there was no market cap data. Strong equal weight is also strong valuators.
Again, transaction costs matter also in the 19th century. Good to mention is transaction costs were not that high as people think. People think transaction costs were crazy back then. And it's not. Our ancestors, we should be a bit proud of them. They were pretty sophisticated and knew how to deal with slower days of information.
But still, pretty efficient. Because transaction costs in the 19th century were not much higher than the early 20th century. They only came down really mid-20th century. And with the optimization, they went down.
Also, momentum. Now you're trading frictions. But gross, it's a strong premium. Value we found difficulty of defining that because, book-to-price, there were no standard accounting practices. And that's what I mentioned before about. Because with quality, it's even more difficult. We couldn't test quality.
For failure, we took dividend as a proxy. Div to price, which is a value factor. It's not perfect. That comes close. And we found a value premium. But weaker than the momentum premium and also weaker than the low-vol premium. Low-vol, again, was a strong premium. The risk-return relation was flat in that period. Not much inversion. But flats. And then if you then take the risk-adjusted approach as I mentioned in the beginning, you see the factor coming out very strong. And also, it's a low-turnover strategy. And so, it's also easy to implement. That's the factors we tested. And these were the findings.
And I can tell you, when we first ran the tests, I was really a bit nervous. Because what would I do if low-risk came out to not be working then? Because that would be a falsification basically. You could still rationalize anything you find, of course. But that was tricky. And more important than if low-vol is doing good in the past quarter. Yes or no. Because that's more of a short-term markets movements. Whereas, yeah, six years of data. Although it is 19th century and it's not the same as today, of course. But it is six years of data and it says something about asset prizing.
It's pretty cool to see the consistency with value momentum, low-vol being relatively strong and size being not significant. Can you talk a little bit about why you think it's important to run out-of-sample tests like this?
Yeah, it's very important. Like I said, for some people, [inaudible 00:31:46] are a feature of markets. They're convinced. Whereas others are not convinced yet. For example, we know that value had troubles in the US last decades. And low-vol is also in this strong rally. Bull market. Which, as I said, that's the nature of low-vol, of course. Some are not convinced.
And then, also, in academic circle, there's the p-hacking debates. And that's all the factors come out. Everybody has an incentive to publish t-stats above two. Could it be that it's data mining? Even out of sample is not enough. Because if you do an out of sample, you could say, "Hey, the factor was there. But it's arbitraged away." It's not a proof that it didn't exist. Whereas with pre-sample evidence, so going back, it's untouched and it's not influenced by the researchers. That's why it's important to take a look at that. And that's why it should be known to everybody, to the researchers. And it should hopefully land in a nice journal and still a working paper.
But we have high hopes. But also, expectations that it will land and that people are aware of this finding. Because it's very important in the p-hacking device. So our stance there is that, yeah, these common factors, which are easy to compute. Just just return data. There are no degrees of freedom. No complexity. They are features of markets. They are, therefore, not resulted data mining. There's still a very, very, very small chance that this chance is pushed down closer to zero because of this new six years of evidence.
What did you learn from your sample about the economic explanations for the factor premiums?
That's a bit more difficult. Because you want to disentangle different explanations. What we do now in the 19th century, there wasn't hardly any delegated portfolio management. The investors or owners. And apparently, they're not the – but we do know that this is not the only factor, driving the low-risk effects. Because, otherwise, we know it's not there. And still, there's a low risk anomaly. That's a negative, negative finding.
But low-risk is caused by other effects. One of them is the lottery [inaudible 00:33:53] payoff-chasing, risk-seeking investors. Now these are everywhere. They've been in the past. They come and go. Usually, they lose money. But they enter the markets every time. So that's one explanation. The second one could be envy. Even if you don't have a benchmark, you still want to outperform your rich tycoon neighbour.
Also, back then, you had all those guy-smoking cigars near post offices. You see the pictures. Pretty actively trading. And I'm pretty sure that they were not buying the most boring stuff. That they were buying back then these Russian railroad stocks. And I think it's a human baked-in that we like risk. If it moves quickly, we're attracted to it. Like flies to a light bulb.
And we know how we dance. If you take too much risk, it ends in tears. And that's basically what this history lesson tells us. Basically, these laws of nature and laws of human behavior were also back there. That also means that if, suppose, everybody's – the benchmark starts to fade away, that doesn't mean that the low-risk effect will be gone. That's an implication of this deep historical research.
Super interesting. You've got a paper. And this one did land in a big journal, in the Journal of Financial Economics, that looked at global factor premiums from 1800 to 2016. Can you talk about the difference between a global factor premium and the cross-sectional premiums that we were just discussing?
Good to make that distinction. So when we talk about factor investing, usually, we mean US stocks, but you can apply factor investing in international equity markets. You're still selecting stocks. You couldn't do this in Europe and Japan, emerging markets.
In this paper, global factor premiums, we did it across markets. So we didn't look at individual securities. We looked at markets’ indices being stock market indices and then comparing, for example, the US stock markets with the UK stock market, with a German stock market index, and then applying factors to that.
So you can then test time-series momentum and cross-sectional momentum. So what we did is we took the big factors which were documented in the top A journals in the past five years before we started our study. That is then time-series momentum carry low-risk seasonal and value. We tested them across markets. By doing that, you can then also move outside equities. You can also look at international bull markets commodities and currencies. So that gives you a whole matrix of six factors for markets. So then you get 24 factors or alternative risk premia, other goals.
Again, risk premia, I'm a bit – that's not my words because I would call them factor premiums, which is a bit more agnostic. So 24 and then, yes, going back to 18 on there. So we basically out-historied ourselves. Because with the broad markets, you can go even further. And there we find positive results. So factor premiums seem to be a feature, a market feature.
Also, interesting that, individually, they are not extreme. Usually, they are basically all below one. That's also good to be aware. It’s gross, so you have implementation costs. That means they're not very visible. So you need to know what you're doing. And then, for some investors, this could mean that you can reap profits from this cross-factor premiums, which are probably a feature of markets.
This is such fascinating information. Between these two papers, how confident do you think investors should be that factors are actually a real thing?
How confident? Yes, it's good to always have some doubt. You can be wrong. Predicting markets is one of the most difficult things. So it's all putting the odds in your favor. I think you can be pretty confident that the factors which are pretty easy and not complex, which have a solid economic rationale, that they will probably be around for the next decade. So if you have your whole investment portfolio, so you have your saving, you have insurance, you start investing, then I would certainly allocate some money to these factors.
It's the same with the equity. The biggest one is the equity premium. It's also a factor. We understand it. It has a Sharpe of .4 or .3, and we allocate to it. I have some doubts on this factor, like I have with all other factors because they can disappear for decades. I hardly meet any investor who doubts the equity premium, which makes me very doubtful, by the way.
So S&P 500, it's not like mana from heaven that you will get your 10% per year. I don't know. But putting that aside, so I'm investing in equities. No worries. But it's always good to have some doubts. And then, if you go to all the factors, keep it simple. So if you look at low-risk value momentum and then the quality, you can be pretty confident that if you take these four, in some way, you get four positive way that they will give you a risk-adjusted performance in the next decades.
Again, it's not 100% certainty. But you're putting the odds in your favour. It would be also – ignoring this could be a rational decision where you say, “Hey, I've got my insurance. I've got my savings, and I just have some money I want to play with. That's it. I don't invest. I only secure my money, and I do speculation.” In that sense, it can be rational when you say I ignore it. I think those people hardly exist. You always have this middle part where you say, “Hey, I want to put money for my 401(k), or my kid’s colleagues, or some long-term goals.” Then I would definitely take a big confidence that these factor premiums exist and then think of a way of getting positive exposure to them. That can be done in multiple ways.
I agree that some level of that is important. But your pre-sample findings in those two papers are pretty incredible. I mean, the cross-sectional finding and then also the low-vol premium finding. How far you went back and the fact that they were so consistent with the time periods after your pre-sample is kind of amazing.
So you can say I'm more confident on multiple factors. The alpha for the next decades, I think the Sharpe ratio of the alphas of the four factors I mentioned, I'm pretty sure that that will be higher than the Sharpe ratio of the one factor, which is the equity market.
I've got a question on Sharpe ratio because you mentioned it a few times. So you mentioned that low-risk is a negatively skewed strategy. Skewness doesn't work well with the Sharpe ratio. What are your thoughts on using the Sharpe ratio to evaluate a strategy, like low risk that has a negative skew?
Sharpe ratio has its pros and cons. It's at least better than information ratio, as I said. It's closer to utility. Yes, better is like to do a [inaudible 00:40:28] or get closer to what we call risk, which is losing money and the depth of loss. However, for practical reasons, I'm not that worried about skewness. That is inappropriate. So I did my PhD on downside risk, and the conclusion is to predict downside risk. Past volatility is pretty good. To describe risk, it's better to move to more what risk actually is, which is losing your money, chance of losing, and the amount of money you're losing. So the lower partial moments.
Cool, yes. I saw your couple papers on downside risk, and I didn't work them into these questions. But I'm glad it came up in some way. You mentioned your paper on when factors drop their shorts earlier. So when we talk about factor premiums, we're typically talking about long-short portfolios. You obviously did that paper. Why do you think it's important to examine the long and the short legs separately?
Yes. It's important because they are different empirically. If things would be symmetric factors, then it would be nice. But we found that you can come to wrong conclusions even on things like quality being – spanning low-volatility, whatever. Especially given that most money is managed in a long-only setting. That's what you observe. So that's why it's very important that academics bridge this gap between theory and practice.
We published this in the z, which is the journal who has the same aim of embracing the gap between academics and practitioners. It's a pretty cool finding. When I saw the first results, I discussed them with David, my co-author. I was like – I really like, “Wow. Jaw-dropping.” [inaudible 00:42:08]. So sometimes, I tell my wife I found a high R-squared, and then she's like, “What's wrong with you?” Well, I was really happy with my high R-squared.” So that's also happened with this. I was like, “Wow. This is amazing.” Then we decide to do something with it because it's relevant for investors and also some of our clients who are long-only investors. And they need to know this.
Does the long leg or the short leg tend to offer better risk-adjusted returns?
On a standalone basis, you see some single factors do better on the short, like momentum. It's really great on the short. But then when you start to combine factors, then the shorts don't add much to each other. And then, on the long, diversification starts to pay off. And this is before implementation. Because in the short, you have a higher implementation costs and even also risks; repetition risk, shorting risks, short squeezing risk, all those kind of things.
If you take all that combined, you could say also to all the listeners that if you're in factor investing, long-only, multiple factors are pretty good. Well, the first five. There is something to gain, of course, on the short side. It is not that it's much higher and much bigger out there for factors.
For other short strategies, so some people say, “Okay, so shorting doesn't matter for individual stocks. We say no.” It could add value, shorting individual stocks. But not if you combine quality, momentum and risk.
So it's a bit nuanced. People sometimes don't like nuance. But for factor investing, it's pretty clear that on the long side, also in taking implementation issues into accounts, you're doing better on the long sides with the first five factors than on the short side. That's pretty fascinating and a bit counterintuitive.
On the long side, factors are less correlated with each other. On the short side, they're more correlated?
Yes, that's what we find.
Interesting. What are the practical implications of those findings?
The practical implementation is that if you're not shorting them, there's less urge or that you're leaving alpha on the table. So that's one implication. Another one could be that you say, “Hey, let's do long factors, short indices.” Then you basically get pretty easy absolute returns without a hassle of short squeezes, reputational risks and implementation costs. So these are just to name two of the implications of our findings.
What concerns would you have with the Fama-French Five Factor model?
A couple of them. So we wrote – in the paper, we had five. But let's just talk about this, what pops up. One of them is that Fama-French don't include the low-vol factor in their five. So they have five, and low-vol is missing. In fact, they kept one factor in, which is the market factor. So they stick to factor being the rewards for risk. And that's the Chicago Paradigm. Anything with a return should have risk. So they don't include low-vol or beta as a factor. So that's one problem.
Another one is that they keep sizing. It's a good explanatory factor. But it's not giving a premium, as we discussed. Momentum is clearly not in. It's lacking. So the momentum factor is one of the biggest ones. So together with low-vol – so low-vol and momentum I would say with failure are the big three. In that order; low-vol, momentum and failure. Yes. There are five factors. Two of them are lacking, which is low-vol and momentum. In that sense – and still, you have five, which is adding complexity. So it's a bit complex and not fully descriptive. That's some of the issues I have.
The other one is that it's long-short in which the way it's constructed. It works pretty well. So they do the median size, and then they do value-weighted. The problem is that if you're a small company, but then within the smaller stocks, you're a big one, you have a big weight on the factor returns. If you just switch sides, you become a large-cap stock but very small. You basically have zero weight in the factor definitions.
That is a flip-flopping thing most people don't know. It's also on a 60-year basis not a big problem. However, if you zoom in, it is sort of problematic. This is pretty random. So this can be done in a better way. But that's a bit nitty-gritty maybe but still could distort results. Most important is it's five and two of the top three are lacking. That's low-vol and momentum.
How would you put low-vol into an asset pricing model? Like how would it fit into another version of the Fama-French model?
If you try to do that, then basically lots of things collapse. For example, if you start modeling from a relative utility framework where you say, the [inaudible 00:46:51] as you have in the CAPM, is not concerned about mini variants but about access return and tracking error. That's fine. But then equilibrium doesn't exist because the equity premium goes away, and your model breaks down. So it's not general equilibrium. It's pretty tough.
If you say, “Hey, let's assume – let's forget about the benchmark." Let's say people are gamblers. Then you also have a problem. So I had one paper on that where we said people are gambling for games. That's not even fully risk-seeking but partially risk-seeking, which means they really love a positive skew and then start modeling.
Then the problem is that the markets is very inefficient. Also, if you like a good skew, you shouldn't diversify because you throw away your upsides at a much faster rate than you reduce your losses and your downside.
Somebody who likes a positive skew doesn't diversify. And that's also what you observe, that people who like a good skew, they don't buy thousand stocks. They buy like three or five. Well, that's rational. However, to reach to an equilibrium model and have a CAPM or any skewness CAPM or any relative utility CAPM, you need to have the outcome that the market is efficient. For those examples I mentioned, it's not.
This is basically the whole problem of [inaudible 00:48:06] for finance, which is really good explaining individual behaviour. But it's very difficult to bring this to macro and market level. That's basically the struggle our whole finance research is struggling with.
Maybe a good reason that they didn't try and put it in the model, because it would have blown up finance altogether.
Yes. So you can put it in. From an APT point of view, so that's agnostic from utility, you can throw in a full factor. You can also create – because then you do a long-short. Then you throw it at the right side. But there are more statistical and very light assumptions. And Cochrane calls this the factory phishing license. Anything goes. Just throw in long-shorts. Then you're really far away from what we call economics and first principles.
That's what we did as well. So in the paper conservative formula, we create a conservative minus speculative, factor, a long-short, and it blows away many factors in a very elegant and simple way. Then we say by sticking to a couple of rules, so you don't have the problems I mentioned with Fama-French Five. You only stick to a thousand large caps. It's pretty simple and clear. Then you can get access to all those multiple-factor premiums. And you can explain others as well.
Blows away in terms of explanatory power.
Yes.
Wow. Super interesting. All right. We're going to move on a little bit from factor investing to a paper that you did on inflation regimes in the Financial Analyst Journal. You did this, another really incredible historical data expedition, I would call it, for asset class and factor premiums through inflation regimes historically. How did you define inflation regimes in that paper?
We defined it as buckets. We took 12 months rolling, and then we looked at CPI above four, two to four. Also, deflation. Sometimes we forget. But that can also be a future scenario. So very data-driven. Just observing it and then putting into four pockets.
And how do asset class premiums vary through different inflation regimes?
Basically, what's really bad for the investors is high inflation. If you go to 4%, above four, hardly anywhere too high, stocks do offer some protection. But bonds are really bad. Any, yes, 60/40 investor who had a great three decades in the US will really suffer. That's a bad one. If you then add stagflation to it, so that means that economic conditions, it gets even worse. That's a scenario investors should be fearful for.
How do factor premiums vary through inflation regimes?
They hold up pretty well. We find factor premiums to be persuasive. So very robust. Whether it's deflation or inflation, factor premiums are there. We do see some variation. It seems that momentum and no risk tend to be better in high inflation. But we do strict statistical tests. It's at the edge.
What's good is that the size of those premiums is also very stable, which means that if equity returns are really low on bonds and equities, you do get this 3%, for example. Which is, in a low return environment, 3% percent is more than when equities go up by 20%. A relatively factor premium become more important than a source of capital growth.
And what economic environment did you find to be the worst for investors?
The worst would be high inflation with that economic growth. We looked at several measures for that. For example, recession or other measures of economic activity. And it's bad, especially if you adjust for inflation, that's also findings so nominally. It doesn't look that bad. But in real numbers, it's really bad. And that's giving some repercussion. Because also, the last year, 2022, we had inflation going towards them. Markets going down almost 20. But still, most investors sells minus 20 and not minus 30.
In a way, behavioural biases help us a bit feel less of the pain in a bad scenario. But however, the truth is your wealth evaporates, your purchasing power in this deflationary regime. And you should be aware of that could happen. And also, try to take measures to limit those losses.
And the factor premiums are still consistent in those brutal periods?
Yes, they're very consistent also in those periods. This cannot be an explanation. Also, from a rational point of view, you cannot say, "Okay. So this is the rational experiment why it's there." Because the premiums are found in all [inaudible 00:52:40] areas.
That was a cool paper.
Thanks.
You got another recent paper that I also like on gold. Can you talk about how an allocation to gold holds up as a downside hedge in the 1975 to 2022 sample?
Yeah, maybe some explanation on the gold paper. It was sort of an idea that gold comes – and you have the gold haters, the gold lovers. It's the crypto of the ancients to store wealth, blah-blah-blah. There's something with goals. It's also known [inaudible 00:53:07]. And I did lots of low-vol papers. I think more than a dozen. We thought let's take a look at gold because gold also is seen as a safe haven like low-vol. That's sort of the reason we looked at it.
Also, reactions to the paper, some gold folks have been, "Don't you like golds? "Gold haters said, "Pim, why are you promoting gold?" I got the same paper completely different [inaudible 00:53:28] which means we found a middle way, I think, if that's a critique. We looked at the safe haven hypothesis of gold in this paper.
What are the downsides of getting downside protection from gold?
Yeah. It offers downside protection. Not fully. It offers protection with a longer horizon. That's the first thing we found. On a monthly basis, it doesn't offer much protection to start with. You only start seeing it when you move to one year or longer. And the downside is you give up return. It's you get protection but it costs you money.
And I guess, since you looked at low-vol on the paper, is the suggestion that low-vol is a better downside risk protector than gold?
Yeah. In this period, for me, it was a short period. We only started in 1975, which is like how many cycles are we talking about then? But still. And then for that period, we found evidence that low-vol was indeed much more effective. If you want to protect your capital, gold is a way to do it. But it's more of a second-order effect. Because if you do it, do it carefully. Don't overdo like 5%. It's like putting it's like salt on the foods. Don't overdo it. So a bit of gold.
But with low-vol, you can go all-in. Basically, you can say, "Okay, let's just take my equities out and put defensive in." If you really don't want to lose money on a one-year horizon, then going defensive equities, low-vol is more effective.
The good thing is they strengthen each other. It's not either or. It's a different source of protection. In years when low-vol protection, it's not necessarily that then gold is also offering it. They correlate positively.
You mentioned the sample size. Do you think this sample is sufficient to inform expectations? And maybe to expand on that question, do you actually think a 5% allocation to gold makes sense? Or only in this sample?
In this simple, for sure. Going forward, it's very ambiguous. I do tend to like the story that, when you go back to Rome two thousand years ago, you were in the army and you had your monthly wage. You had this golden coin. You could go to a shop and buy yourself a toga, a nice suit. You throw it in the sand in the Coliseum, you put some sand on top of it. Fast forward 2000 years, you pick it up, it's still clean. It's not gone. You don't have a passport, which you lost with your crypto or your stock accounts. You go out to the shop, a modern shop and you buy yourself a modern suit because it's worth a couple of – it's like $400, this coin. In that sense, it's an ultra-inflation hedge.
However, as it quants, the observations aren't too sure. It's difficult to say something about that statistically. Also, wether should be five, one or ten, it's very difficult to say. Personally, I do have a little bit of golden coins. I like this hobby. I like to look at them, and touch them and discuss them with my kids.
So you collect an emotional dividend from your gold.
A little bit. Yeah.
Our final question for you, Pim. You've obviously done some incredible research. You've also written a very accessible book on low-risk investing. How did you define success in your life?
Yes. I think success is achieving your goals. For me, my goal is to have a long-term positive impact through family and finance. I call it two F's. Let's start with finance. I believe wealth can be enhancing your well-being. For example, it gives you more freedom. Well-being can also increase if you give to charity, something my wife and I like to do. And we enjoy it.
One of my passions is applying academic investment research and then applying it in a simple way to complexity. And, yeah, one insight close to me is the fact that conservative stocks beats speculative stocks in the long run. And I like to read a lot and do a lot of research.
And success for me is to bring this wealth of knowledge found in the literature to a broad audience. For example, low-vol or defensive investing is now one of the common four factors in the industry. Whereas about 15 years ago, it was clearly not.
An asset firm manager. I'm also managing funds based on all those scientific insights and quantitative factors. Financial success is to deliver high-risk adjusted return to our clients. And then, significantly, statistically. That's really financial success. I think the second one is the F for family. That's most important. Being faithful to them. I've got three boys. They will probably outlive me. Yeah, they're great kids. Success for me is to be a good father, a good husband and a good brother and son. Besides this, even broader, I think it's important to be a good citizen and provide positive to society.
The golden ethical rule, I like. It's simple. Treat others as you want to be treated. Simple. Not easy, I can say. For me, Christianity is a secure base. Treasure of hope. And wealth can be a blessing. But, ultimately, that you remain faithful to the people around you. If you can do this, I think those two things, you're very successful.
What a great answer. This has been a great conversation, Pim. Thanks so much for coming on.
It was my pleasure. Thanks.
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'The Volatility Effect' — https://www.robeco.com/files/docm/docu-the-volatility-effect-2007.pdf
'The Volatility Effect Revisited' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442749
'Ten Things You Should Know About Low-Volatility Investing' — https://www.robeco.com/en-int/insights/2017/07/ten-things-you-should-know-about-minimum-volatility-investing
'The Conservative Formula: Quantitative Investing Made Easy' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3145152
'Media attention and the volatility effect' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3403466
'When Equity Factors Drop Their Shorts' — https://www.robeco.com/en-int/insights/2021/02/when-equity-factors-drop-their-shorts
'The Cross-Section of Stock Returns before CRSP' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969743
'Global factor premiums' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3325720
'Investing in Deflation, Inflation, and Stagflation Regimes' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4153468
'Five Concerns with the Five-Factor Model' — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2862317
'The golden rule of investing' — https://www.robeco.com/en-int/insights/2023/04/the-golden-rule-of-investing