Today I speak with Pim van Vliet, Head of Conservative Equities at Robeco.

It will come as no surprise, to those who know Pim’s work, that we spend the majority of this conversation talking about conservative investing. Specifically, we discuss the low volatility anomaly. But rather than rehash the usual high level talking points, I wanted to dig into the more practical considerations.

For example, how are low volatility and low beta different? How do selection and allocation effects contribute to low volatility investing? Are low volatility and quality actually different anomalies? And how should we think about the influence of currency in a global low volatility portfolio?

While Pim has nearly three dozen research publications to his name, he provides the balanced perspective of a practitioner, acknowledging the practical limitations to managing money in the real world.

Please enjoy my conversation with Pim van Vliet.


Corey Hoffstein  00:00

321 Let’s go

Corey Hoffstein  00:07

Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

Narrator  00:19

Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of new found research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions and securities discussed in this podcast for more information is it think

Corey Hoffstein  00:50

Today I speak with Pim van Vliet, head of conservative equities at Robocop. It will come as no surprise to those who know PIMS work that we spend the majority of this conversation talking about conservative investing. Specifically, we discussed the low volatility anomaly. But rather than rehash the usual high level talking points, I wanted to dig into the more practical considerations. For example, how are low volatility and low beta different? How do selection and allocation effects contribute to the low volatility anomaly? are low volatility and quality actually different anomalies? And how should we think about the influence of currency in global low volatility portfolios? While PIM has nearly three dozen research publications to his name, he provides the balanced perspective of a practitioner, acknowledging the practical limitations of managing money in the real world. Please enjoy my conversation with Pim van Vliet.

Corey Hoffstein  01:49

Pim, welcome to the show. Very excited to have you here. Long overdue. I think I’ve actually been intending on trying to get you on for a couple seasons now. And for one reason or another, it hasn’t worked out my fault, mainly. So very excited to finally have you on the show. I think I just mentioned to you right before we started recording, I think this is the most questions I’ve ever prepared for our guests. So I hope you’re ready for a long one. I’m ready. All right. Well, my guess is most of the listeners are going to know who you are. But just for some table setting. Can you maybe give us a little bit of background about who you are?

Pim van Vliet  02:23

Yeah, that’s good. So my name is Pim van Vliet pronounces fleet. So that’s Dutch, I’m from Holland, we say if it ain’t Dutch it ain’t much, that’s our joke. But it’s a very small country, very international trading research rich country. My background is I’m a PhD in financial economics. I like science. And I like investing and combining the two, I work for a Dutch firm, pure play as manager 19 years old, founded in the 20s. It’s called row Bayko. There we have quite a bit of a quants, part of our business where we use rules based models, based on factors. I’m the head of conservative equities. So that’s our approach to low vol, defensive investing, multi billion strategies global emerging. And I also find time to do research. Sometimes publishing in some of the more applied academic journals, like finance analyst journal, or on our journals. 

Corey Hoffstein  03:17

Well it won’t be a surprise to any listeners, I’m sure that we’re going to spend quite a bit of time talking about conservative equities. But I actually want to start at a higher level with one of your most popular articles you’ve written called global factor premiums. And this is an area where Robeco has done a lot of research. I know you’ve focused heavily on the conservative side, but there’s a ton of articles that you guys have published about factors at large. And this piece actually looked at I think it was like, somewhere in the mid 20s of global factor premiums across equities, bonds, commodities and currency markets. I think going back as far as the 1800s, correct me if I’m wrong, there, it was, it was a couple 100 years. Let’s start with that big picture to you. What were the most important takeaways? And maybe conversely, what were some of the most surprising results of the study?

Pim van Vliet  04:06

Yeah, this look very cool study. So it’s published in the journal financial economics 2021. That is with keto and Lowrance. My co authors, you’re correct. It’s the data. Our sample starts in $18. And we test 24 factors. What is going on in science is what they call the pee hacking debate. So many of the results presented are false. I already noticed during my PhD, but back then there was not much attention given to it. The problem was that many people do if you do 20 tests, one of them is significant at the 5% level by definition, and believe me, and you know that sahkari many PhD many students be on the resource and do many tests. And then you can also cheat yourself by making up a story later on, which explains these great results. And it’s really a plague to find so many studies cannot be replicated. So that’s the replication price. And when you’re on money on behalf of your clients, you have a fiduciary duty to know what you’re doing that you’re not investing their money, their savings on noise and academics making a backtest. So this study we set up to do out of sample testing, and then mostly pre sample testing, because the problem was out of samples that it could be arbitrage away. And then still, it could be that those factors exist, that they don’t exist anymore. So is it really a feature of markets for that going pre sample is very important. And we were doing this study in the midst of what we call winter, as factors were tough like value, it was much debated. So we also test its value. And what we found is that this pre sample evidence, that’s the common factors, were also working in that century, across all those decades, especially if you combine them. Failure is one of them we tested and value was also significant in the 19th century. So that gave us the impression that failure is a feature of markets, although it’s not there every year or every month or every five years. So that’s really struck us from that study. And that gives us lots of confidence, more than whether in any given year factors do good or bad, because that’s one observation, well, 100 years of data does gives you more and more confidence.

Corey Hoffstein  06:23

So how would you address the pushback maybe that while this pre sample data is new, it might not be relevant data that there might be certain limits to arbitrage or tea costs that are unaccounted for that make it so that these anomalies persist in the pre sample data. But it’s not something that actually could have been exploited.

Pim van Vliet  06:42

Yeah, that’s a good point. So transaction costs were somewhat higher, although not as high as often issues, to dig into the files that were not much higher than in the 60s of the 20th century. So the limits to arbitrage don’t apply. We also look at long only results. So you can also use factor premiums in a long only strategy where you basically wait with buying or wait with selling for let’s turn over neutral. So also then you can exploit across Alpha. Yeah, in a way, often, people tend to dismiss old historical resource that they say, yeah, it was different. And it’s a fair point, but dismissing it altogether and not looking at it. That’s quite extreme. So of course, this one on two years of data is not one on one, the same as like last decades, however, it is quite formative, especially if you look at FACTOR premiums, which have long cycles, and are driven by human behavior. So more and more research shows that like the value premium is driven by behavior, over estimating growth, overreaction. And for that car, you need long term data just a couple of years is not enough. So yeah, it’s fair points, but still informative, and it’s a good insights coming also at the moments when some people don’t believe in some factor premiums anymore, then this is really a good sign of confidence that the chance that all factor premiums are just a result of pee hacking is very small. And that gives confidence for the future.

Corey Hoffstein  08:12

One of my favorite tables in the piece is a summary of Sharpe ratios. And that’s not to say Sharpe ratios are the end all be all statistic. But it shows the Sharpe ratios for all these different factor premiums over time, and they all had a Sharpe of less than point five. And Cliff Asness has a great quote where he talks about the difference between statistical time which is sort of what we see in a back test. And we know what’s possible on paper versus behavioral time, which is what we live. And I feel like this table should be something that should be I don’t know, put on every institution and advisors wall just to make them acknowledged, decade long drawdowns can happen. Curious as to your thoughts. Given that these sharps are fairly low, how much a factor Based Investing ultimately becomes a faith based endeavor in the short run as good.

Pim van Vliet  08:59

So Sharpe ratios tend to be 0.5, 0.3. The most famous factor is the equity premium. And interestingly, the Sharpe ratio of equities has been above one in the past decades, especially in the US, how can you beat the s&p 500? Interestingly, this is the moment where you should doubt the equity premium. I hardly find people doubting the equity premium, because in the long run, it’s point 3.4. So we had one. And the problem was if sharps really go above one that they become so visible, that people flock into us. So in a way in a long term near efficient markets, you need Sharpe ratios, not too high. So point five is perfect, especially if you can combine them. And I fully agree with this notion of how much time you get the good time to harvest premiums. That’s the difference between a backtest and living through performance. And yeah, that’s that’s the key insight that’s capturing factor premiums. Real life really requires character and persistence, rather than just more IQ and better back tests.

Corey Hoffstein  10:04

So we’re gonna be spending a lot of time in this podcast talking about conservative investing, I’m probably going to use a bunch of phrases interchangeably, low vol defensive conservative, they’re not necessarily interchangeable. And we’ll dig into that a little bit. But before we do the deep dive, can you maybe at a 30,000 foot level, explain what is low volatility investing, maybe you can highlight sort of the pertinent theoretical foundations and share some of the high level empirical results.

Pim van Vliet  10:32

Yeah, low volatility investing is basically buying securities which move less than the markets. So we saw less volatile, more stable, more defensive characters. And the opposite is more cyclical speculative investing. Usually the market is split in value and growth, that’s Morningstar classification levels makes a different split, akin to credit investing where you have investment grade and high yields. So that’s also how you can look at low vol investing as a way investment grade equities versus more high yields equities risky. The issue with low vol investment, and that’s why it has become so popular is that low volatility stocks outperform high volatility stocks, in the long run. And that is one big anomaly one big puzzle. And basically, this is the question of my career. So I’ve spent many, many years on this theoretical, academic, but also in practice, really getting my head around. So how can this be the case. So when I tell this to my mother, like, you know, there’s low risk stocks, more return higher returns on high risk stocks than even she says, and she has no PhD in finance or CVS, he says, yeah, that cannot be true. It’s too good to be true. And that’s a puzzle, they call it an anomaly effective premium. And interestingly, also, academically, it has been a bit ignores, especially from Chicago school, you cannot call it a risk premium. Because the value premium, you can say it’s a compensation for risks one way or the other size, the same with low vol, not because it’s low risk. So it cannot be a risk premium. So it’s really a step in the heart of efficient markets, capital asset pricing model. So yeah, it’s also academically, it has been much less attention than like value and momentum investing. And that’s fascinating. So that’s why we give a bit more weight to it to compensate for the attention it should have.

Pim van Vliet  12:27

In my case, I’ve written a couple of papers on it, also a book for layman investors, really to get his message out. And maybe I only stopped when it’s a CFA material that all students know. And it’s low risk stocks, burn high risk adjusted returns, which is a big puzzle.

Corey Hoffstein  12:44

Where does the industry stand? Maybe both practitioners, and you can provide an academic thoughts as well as to why this anomaly seems to exist.

Pim van Vliet  12:55

Yeah. So the why is the great question, we’ve given this thought if you summarize the literature, one prominent explanation is leverage constraints and aversion. This was already brought forward by black, which means that if you buy low vol stocks, you get high risk adjusted returns. But if you kind of lever up and you want to have high return, then you buy into risky stocks, basically to get your higher return, even if it’s not fully compensated for the risk. So that’s one way you can explain why the risk return relation is a bit flatter than it should be. However, it’s not a full explanation. Because the relation is not too flat, it’s really negative at a certain point, and you cannot explain that with leverage reverse. And so you need more. Another one is more broader. And that’s benchmark constraints. So many investors do have benchmark constraints, and I witnessed it when I joined the industry. tracking errors are huge relative performance, we talked about living through your underperformance. And that’s also often relative performance. So high vol stocks do have a high tracking error, high relative risk. So that makes them very unattractive. So that’s explanation I like a lot. And then thirdly, there’s a part of the market which is just speculative. They want to get rich quick, and to get there, you need to buy more risky stocks with positive scoop. They have like lottery kind of payoff, not only retail investors, but also agents. So in a principal agent, setting where you don’t own your own stocks, you also have a preference for volatility, because it can make you a star analyst or a star mutual fund manager. And that’s an option like kind of payoff. So these three explanations, leverage benchmarks and positive skew all explain why this is the case why this anomaly exists. And the beauty about these explanations is that they’re all pretty rational. So it’s not that you learn about it, and that you then correct for your mistakes. Second, you is it’s a very difficult arbitrage. Because if you want to arbitrage you, again, have this leverage constraints, many of us. And that’s the beauty that if you don’t have these constraints, and you know about it, you can exploit it. So it’s also one of the most sustainable alphas out are very difficult to arbitrage.

Corey Hoffstein  15:20

I want to go from that sort of 30,000 foot view to get into the details and the nitty gritty right now. One of the things you often hear is the phrases low volatility and low beta used interchangeably. And it’s no surprise to anyone who’s in the industry that as soon as academic research in this area got published, there’s all sorts of follow on research where people say, Well, no, let’s look at residual volatility or downside beta or semi variance. In your view, are these interchangeable measures? Or do they have distinct differences that practitioners looking to sort of exploit this anomaly need to be aware of?

Pim van Vliet  15:56

Yeah, some from 30,000 Few you can say they’re exchangeable. That all measured defensiveness in a rough way. So if you’re low level, you also tend to have low beta, you’ll tend to have lower residual fall and low semi variance. So in that sounds, you can say stop. But if you if you go more into detail, you start investing in it, or you start doing research and performance evaluation, then things start to matter. The difference between low fall and low beta is the way you treat correlations. So some stocks have a low beta, but a high volatility. So one good example of that is gold miners, gold miners tend to be very volatile because of the gold price and its mining. But they do have a low beta, because gold is often negatively correlated with equity markets, that it’s a really good example for how there can be difference between a low vol and a low beta stock. And when you start investing, it matters which approach you use, I believe you should use both. So you should both use volatility and beta. Because if you would focus on one, you could lean too much into gold miners with beta or just go out with low vol. And the truth is often in the middle. So there are a lot more dimensions to risk. And if you want to predict risk, because that’s the most important one, all those measures that measure risk exposure, you want to go x and z, you want to predict risk. And then we find that combining different dimensions of risk often gives you the best predictor for future risk. And then you basically take combination of factors not only beat and volatility, but you also add downside risk, and also more credit based risk factors.

Corey Hoffstein  17:37

One of the things we also often hear is low volatility and quality get lumped together into a more sort of global defensive factor. Curious, do you think that low volatility, subsumes quality? Does quality subsume low volatility? Or are they truly independent factors?

Pim van Vliet  17:57

There is some overlap between the two, especially on the short side, because most academic studies 99% of studies do long shorts, whereas less than 10% is managed long. Short, that’s pretty interesting. If you look at the short of that, to start there, they’re very much overlapping. So junk stocks, and profitable ones are often very volatile. If you look, look at the long legs, then you see that there’s partial overlap, some overlap, but not fully. So some quality stocks are allowable, and some level stocks are policy. So it’s difficult to disentangle but there is a difference. So the low vol stocks, which are not extremely profitable are different than stocks who are and the other way around. If you do a head to head competition, they do have their own added value to each other. So that’s why you when you play low vol. You can also include profitability factors. But saying one competition the other completely, that’s not the case. So there are distinct effects supplementing each other, especially on the long side. On the short side, they are more overlapping.

Corey Hoffstein  19:01

I want to take an interesting turn with this conversation I want to go maybe towards the realm of a little bit more practical, particularly for US investors, because as low ball and utility investing came to market, a number of funds that look to exploit the anomaly also came to market but as they will they implemented the concept a little bit differently. So for example, in the US, there’s a couple of ETFs, one of which sorts stocks on prior realized volatility, and then takes the bottom quintile and weights those in proportion to their inverse vol. Another one takes a very similar approach, but does it within sectors and keeps the sector weights neutral to the benchmark. And this brings up an interesting question to me about the importance of selection versus allocation effects within low vol. What is your research tell you about the importance of those two effects?

Pim van Vliet  19:56

Yeah, so low volatility. If you do a low volatility screen you do get certain deals to some sectors, which are more defensive like consumer staples or utilities. And then you get location effects because of consumer stocks do well. Research shows that the alpha is present in both. So there’s Alpha coming from Sector allocation effects, and there’s Alpha coming from stock selection. And if you go international, it’s even works on a country level as well. However, allocation effects are often very dominant in driving performance. So you can either shut them down as human as one of the ETFs is doing, which you mentioned. But then you give up alpha, or you go from in a way where you have soft limits, but you allow some deviation. And that’s what you see most active low vol funds doing. So some limits, and then we adhere to the 8020 rule. So 80% is coming from bottom up stock selection 20% coming from allocation.

Corey Hoffstein  20:57

There’s a third ETF that’s very popular, that takes a minimum variance approach at the portfolio level, it does apply some constraints at the security and sector level, but it is trying to build that portfolio that exhibits min vol, not necessarily pick the lowest vol securities, which to your point earlier means that they could very well pick high vol uncorrelated securities that can cancel each other out. Do you think that this is a valid implementation of the low volatility phenomenon? Or does the introduction of correlation confound the issue?

Pim van Vliet  21:33

Yeah, so from a 30,000 feet distance, it’s okay. If you want to say I want more than okay, I want the best, then I think with this approach, you tend to give too much weight to noise, because correlations are also are difficult to estimate, if you estimate them, right. They still can give you quite some tails to certain sectors, like I said, coal miners are notorious. The question Where do you want to have that this is to have to use correlations as salt. If you use too much of it, it ruins the foods. But some of it helps. And that’s how you should treat correlations. So to some degree and the minimum variance approach, I would say it’s too much salt.

Corey Hoffstein  22:14

You alluded to this a little bit earlier about the difference between academic exploration through long shorts versus the practical implementation, which 90% of the time is long only, you actually wrote part of a study that looks at factors from an academic lens, but ignoring the short side. So I believe that the title of that paper was called when equity factors drop their shorts, kudos to you for a great title, what were to you the most important and surprising takeaways?

Pim van Vliet  22:48

First, the surprising was that if you look at the literature, our little warm, only studies that have been written, and it’s all sort of one month return long, short, and then fama French style. In a way, it’s good that you have a standard, but in a way, it’s not good. Because that’s not how in practice, money is invested. That’s a starting point. But then it became interesting that if you take this Lamoni perspective, what do you see? So when they dropped their shores? What do you see? And there has been quite a severe debate or now failure, whether failure is still as failure, whether there’s alpha if you correct for other factors. And the same goes with quality versus low vol the battle. And then we found that if you take a look only perspective, this gives a different perspective. And also, you come to different conclusions. That yes, low volatility and quality are distinctive phenomena. And value is also stronger. If you take a long only perspective. And most investors are approaching value from a Lamoni perspective. So that’s pretty strong conclusion and also gives hope for most of the money managed because if the conclusion would have been otherwise, that would have been a big problem that you would have said to the 90% of the money, you’re doing it incorrectly, you should go to long short, if you want to harvest those factor premiums. So in a way that was a relief. And also a good nuance to this debate. This is very much focused on the short of

Corey Hoffstein  24:13

When we talk about the low volatility anomaly, I think we have to address the elephant in the room, which is March 2020. I think a lot of investors, those who were implementing the long, only low vol, approach, were disappointed with the realized results through that period. From your vantage point. What happened? And do you think that the sort of ex post results were actually appropriately in line with the ex ante expectations?

Pim van Vliet  24:43

Yeah, so the start of 2020 It’s very interesting and new decades, lots of things could have happened or risk means lots of things can happen, but more can happen than will happen. In the end. You’re looking at one path start of 2020 and one of the big risks was tech regulation, of wars, maybe it happened two years later, anything the end of globalization, but what has happened was a pandemic. And on top of that lock downs, and the lock downs were really affecting the old economy. So retail restaurants, those kind of typical defensive level stocks. And in March 2020, those stocks didn’t offer protection. So Marcus US market went down about 19%. And those stocks also fell similarly. So no protection or very little. And a good thing is outside the US, it was a bit better as a Europe, Asia emerging, it was not that bad. But still, US is a big part of the global market. So it can be fully explained by the measures we took. So lock downs, which really affected retail stock. And on top of that, because people were working from home, there was this big pull forward, of expanded series on Netflix subscriptions, so far zoom companies. So big tech basically profited. A bit similar to y2k. So that was the Millennium back in 2000. When we were fearing that computers crash, also back then the longer tech rally was fueled by this and that’s the same with big tech was rallying. And then it just was fueled by this working from home and lots of investments it and expenditures on all nine stocks went through the roof. So that happens in 2020, fully explainable strategy was doing what it’s supposed to be doing. However, many investors were sort of negatively surprised they sold it was a guaranteed reduction in losses, which obviously it’s not the case. And then some investors turned their back. So you saw after 2020, that’s the popular ETFs. In the oval space, you saw that the number of stocks and capital allocated to it went down significantly. So that’s about 2022. It’s basically when the boys separated from the men, you can say, because it was a bit crowded in 2016. When our markets are global, many investors flocked in maybe for the wrong reasons.

Corey Hoffstein  27:04

Maybe you can talk a little bit about that crowding period. I think it was Rob Arnott who wrote some pieces around value spreads within the low vol anomaly and said that they were at extended ranges and that it was crowded, that it was overvalued, and that people should actually not be taking low vol tilts at that time. I believe Cliff Asness fired back and said you can’t look at a value spread on an anomaly that has a turnover that’s potentially much faster than the horizon over which that value spread would collapse. Curious as to your take it you sort of mentioned there, maybe it did seem to get a little bit crowded, but is valuation spread crowding a real risk to something like low vol investing?

Pim van Vliet  27:52

Yeah, it’s a risk for any strategy. So for example, now, privates ESG, everybody flips into something, it’s just goods and not challenged, you should always be cautious. That sounds like or now that you should be contrarian, I have a value kind of approach. So also, when we do low vol, we never do single factor. Always take a look at the price you pay. If I buy a car, I also look at the price if a buy levels, look at the price. So back then in 16, they are the conservative equity strategies really started to diverge from generic low vol, which became really expensive, whereas we include value factors to stay away from those expensive overcrowded positions. Because the point with over and under crowding is how do you measure it? Because the number of stocks, number of shares doesn’t go up? So we all own the market? So it’s the only thing which how you can measure of crowding is the price sort of valuation? So back then, in 2016, I agreed with our No, I also liked it when Cliff entered, and you can take your popcorn and sequence and watch this incredible fights. And it’s you learn from it, because they’re using very good arguments. Because Cliff also has a good point about that strategy is dynamic. So you can move away from stocks if they become expensive, they are now proved to be somewhat right on effective but also wrong, that failure itself didn’t work. Because you can say that if you look through at all the anomalies through a value lens, basically, you bring everything back to value, which is one factor and that’s not very diversified. So he was right and wrong at some say moments. And where I agree with where we are today, and I think also for your listeners interesting moving forward. I think today we’re in the crowded and that’s where Cliff, Robert now, and I agree that we’re in the crowded fence a bit how you measure it, but you can say like, we’re in the top decile of valuation spreads of low vol, but also failure in momentum and like, add to that the deep historical evidence, which shows that fixed premiums are a feature of more is not some coincidence or P hacking. So yeah, nowadays you can enter factors and you don’t have to worry too much about implementation because the elf potential is so big that it doesn’t come very close how you do as well in 2016 when Arnott warned that was more important because then factor investing was really becoming very popular and crowded, which means some strategies became expensive.

Corey Hoffstein  30:27

Some critics of low volatility investing have argued that there’s a high degree of sensitivity to interest rates, obviously, very relevant and 2022, arguably still very relevant today. Curious as to whether this is something you think, holds water as a potential critique? And what happens if you actually try to control for that sensitivity and potentially neutralize it?

Pim van Vliet  30:54

Yeah, it’s a good question. We agree. So low vol stocks have somewhat more interest rate risk, or with longer duration one year to be specific, with our conservative strategies, we can mitigate some of that risk off of it. But even in both cases, it can never fully explain the anomaly. So if the anomaly is like 3% alpha, then interest rates could explain up to 30 pips. So yes, but put it in perspective. Interestingly, the whole narrative change because nowadays, tech stocks are a long duration. For some reason, five years ago, never Nobody said that. notionally growth stocks are long duration. And also, if you look at short term correlations, that’s what the market is pricing nowadays. So you can look at NASDAQ versus Dow Jones to see where the interest rate is going. Five years ago, it was with low vol and real estate stocks as well. So it’s funny how that Wall Street can also be an echo chamber, or everybody just echoes the narrative. So we’re a bit critical that for low vol, we agree that it’s partly the case they’re more bold, like, but the good thing is, it doesn’t explain the full anomaly. And it’s only like 10 to 20 pips, which is not much there last year. So 2022, interest rates really went up. And low vol, stocks did a terrific job. So they outperformed the 10%, which made many people think that this doesn’t hold anymore. However, we still think it does, because it was also when markets went down. So there are two factors driving. So what’s the market doing? And what are rates doing, because of the correlations for some correlations became positive suddenly. And that’s in that sense of low vol, stocks were exactly doing what they’re supposed to be doing. If you took the interest rate out, which we did partly, you also saw that in 2022, this also helps performance, although then it was the difference within plus 10 or plus nine, for example. But still, it added. So nuanced view, and a bit critical to the whole growth, long duration narrative, which is now suddenly, everywhere. And if you challenge them, they look at you strangely.

Corey Hoffstein  33:01

Let’s stay on that theme for a second. Because again, we mentioned that this is a dynamic strategy. It’s not like you’re buying securities and holding them for decades, there is turnover and low volatility. And maybe you can in answering this question address how much turnover you usually see. But if you have historically seen negative correlations between stocks and bonds, and it makes sense that a low vol, stock would have some potential loading on interest rates, because that would reduce the realized volatility of the security. What happens if stocks and bonds go into a persistent positive correlation period? Would we not potentially expect the reverse situation where all of a sudden you would try to potentially load on stocks that have little to no exposure to rates?

Pim van Vliet  33:44

Yeah, this is very correct. So there are some rhythms some low cycle going through. So low vol nowadays loads a bit less on long duration stocks. But it’s second order effects, because also when you go back in history, so I like deep history going back hundreds or even more years, you still see that despise these estimation periods, which are time varying, you still have the structural tilts towards more interest rate sensitive.

Corey Hoffstein  34:13

So maybe we can address sort of the turnover question right on the nose. Because again, we’ve mentioned that this is a dynamic strategy. This is something that can change and adapt to the way market conditions change and adapt. One of the ways I tend to look at factors though, is that there is, in theory, an optimal turnover horizon where you’re maximizing the Alpha subject to the different transaction costs that you might be subject to that leads into sort of a natural rebalancing cycle. Can you comment on how you see that playing out for conservative in low volatility investing?

Pim van Vliet  34:50

Yeah, so how much turnover do you need ultimately to get sufficient exposure to level? So we did a meta study on all the purposes was all over on this. And then we found that you don’t need to go beyond 25% to get efficient exposure. And we ask your manager if he needs more than 50%. Why this is the case. And on top of that, with turnover, you get also path dependency. So you see many ETFs tracking an index, but this index is can be arbitrary. So when it’s rebalanced, so it’s March, it’s September. And then we come to topic you really like that’s the rebalancing timing luck. It is very interesting. Also, here, academics basically don’t look at this sort of implementation, whatever. And it can be huge and can be huge preaching to the converted here. But yeah, we both did research on this. And that can also make or break a career and a track record of strategy. So you should be very careful about your turnover and momentum is also a case point momentum index, which is done brought forward by MSCI is really path dependence that if you buy this, you know not only by the momentum premium, you also by lots of rebalancing luck, which is just random noise going through. So turnover is something very important to watch.

Corey Hoffstein  36:16

For listeners who don’t know Pim was one of the co authors of one of my favorite papers ever Fundamental Indexation: Rebalancing Assumptions and Performance. Not the sexiest title. I’ll be honest, it took me a long time to find that. But this was published back in I believe, 2010 and looked at Rob Arnotts, fundamental indexing and asked the question, well, what happens if you change the date in which this index rebalance, there was an index that was rebalancing once a year, I believe in March was the arbitrarily chosen schedule? What happened if you had done it December, June, September, and what you found in that paper was pretty substantial performance differences off the top of my head, I’ve read this paper so many times, I think it was in 2009. The index outperformed its benchmark by 10 percentage points if done in March, and actually underperformed the benchmark, if it had to rebalance in September, which is, I think, kind of mind blowing to think about. I don’t have a particular comment here, other than to say thank you for writing the paper. I hope more people go and download it. But was that a surprising result to you? When you wrote the paper? Is that what you expected? And maybe how did it change how you think about managing portfolios and practice going forward?

Pim van Vliet  37:29

Yeah, so the sign didn’t surprise us, we were expecting differences. But it would be that it would be this huge surprise us, like you mentioned 10% difference. In reality, you cannot tell your clients is nice. 10% is not noise if you invest real money. And that’s in the lines, that implementation should be really well thought of, and any possible noise you might encounter. Please take that out. And we’re also happy to see that this paper also influenced practice that this Rafi index, I think, a year later changed and really mixed for different rebounds in various into one, which makes great sense, because then you get rid of this rebalancing, noise or luck. And truly give clients access to the the value premium in this case. And I think that’s very relevant and very important and often ignored by academics in their ivory towers who don’t look at it. And it’s also full back to staying in the game that this noise could push you out. And if you’re out, you don’t get your cell phones anymore. And that’s if you want to finish first, you should first make sure you finish. And rebalancing luck can be something which pulls you out of the race. And that’s the worst thing that can happen to you.

Corey Hoffstein  38:50

Let’s tie that back to the turnover. You mentioned seeing in the low volatility anomaly. You said you only need 25% turnover to effectively harvest this. Is that something then the presumption should be perhaps you get six and a quarter turnover per quarter, right? Or is this something that actually ends up looking much more lumpy over time, you might have very little turnover, then suddenly, there’s a big change in the market’s perception of risk and you get a tremendous amount of turnover in a single quarter.

Pim van Vliet  39:19

Yeah, so 25 years is long term average, it moves between 20 and 30. Based on the rebalancing, time and luck, it’s good to spread this throughout the year. And to do 2% A month example again, one or three. That’s most optimal. And also good to mention this is when you do single factor level. So if you add momentum for example or value, this might be a reason to say let’s do 30 or 40. If you pure one something defensive. Why do more than 25%

Corey Hoffstein  39:47

I want to take this global for a second as a US investor and I have a lot of us listeners we have a massive domestic bias most of the low vol portfolios that we can access are US low vol portfolio’s. But I want to think about this, if I want to build a global low volatility solution, how should I think about currency risk? Is it possible that the optimal global low volatility solution is actually like reference points specific to the investor? Or should we think about it from a neutralizing currency risk perspective? How does currency play into the solution?

Pim van Vliet  40:27

Yes, especially for international investors. And like I said, I’m from the Netherlands, I can bike to a foreign country in a couple of hours. So currencies are important. So we separate it. So when you do stock selection, we tend to look at local stock volatility, to see which stocks are more stable than others. But when it comes to portfolio construction, and also your currency policy, then the question is, should I hedge or not? So in that sense, we say that when you don’t take currencies into account, and when you get into international portfolio, do take it into account, and then usually Hatching is a good idea. Of course, you should take a look at the carry, how expensive is your hatch? If you separate those decisions, you can get to the most optimal portfolio? That’s interesting to see how the US dominance of the past decades it is anyone going for em, of the past decades was versus s&p got killed or international equities got kills? Like you say, if you’re an active manager, can you survive? It’s amazing how how the US and US tech dominance has been, I think it’s also a little bit of unchecked capitalism, that like Trump could have curbed big tech. However, tech, and US incorporated their joint interests. So strong Google’s from Microsoft is good for us, because the US firms so why would you take to their global firms. So yes, they hurt us consumers, but also versus European or Asian consumer. So that’s not necessarily bad for us. And you see now with the AI race, which is between Chinese government and US corporates, so that it’s also good to have strong US firms. As in the European I’m like, What’s up. So that’s basically surprise markets that these profits run so big. They have so much market power, they are not curbs held by Democrat, mobile and Republican, and also so profitable and their network effects just go to the shareholder, that’s, you saw that they just laid off a couple of people you see with Twitter, you can lay off 80% of your people and still continue. And that means that there’s so much moat and so much profits, and I think partly monopolistic profits. Yeah, that has driven this whole US tech markets, and makes anybody taking a different position, or we really have to explain the performance, where we are now. That’s very fascinating. So what will the next decade look like?

Corey Hoffstein  42:59

Well, definitely one of those unique periods in time when you look at the growth versus value performance dispersion and actually see for a large part of the last decade, the growth outperformance was actually justified the earnings, the profitability was there, it wasn’t actually just an expansion of multiples. That truly was, to your point, actually true economic growth in these large cap tech companies now, certainly seems like it’s gotten well over at ski tips at this point. As you point out, regulatory risk looms large, we’ll see what the next decade has to bring. But that actually circles us maybe somewhat into my next question. So I want to talk about your book that you wrote, you mentioned you wrote a book that’s maybe a bit more approachable for the Layperson called high returns from low risk, where you talk about the conservative portfolio. And you referenced this a little bit earlier, that you don’t just buy low volatility stocks on their own. In the book, you actually propose taking value and momentum tilts, you do an initial low beta screen, and then you take value and momentum tilts after want to ask you specifically about how you think about the interaction effects. When you use low volatility as a primary sort versus, say, a secondary sword or as an input into a integrated multifactor sword? Why use one approach over another? Do you prefer one approach over another?

Pim van Vliet  44:24

Yeah, so the book I wrote four years ago for the layman, for my dad’s to explain him what is currently investing, how to make it simple, accessible, whose metaphors and stories so that you can read it in a couple of hours. It’s available in bookstores. Also in English, of course, Dutch but also French, German and Chinese. Also doing it in local languages to get really close to layman investors who not all of them read English. What we explained there is you shouldn’t go local, single factor. So that’s also what I do professionally. You shouldn’t do thing single facts are, you need the interaction of factors. So, the pitfalls of local investing instead of can become expensive, as we discussed in 2016. So that’s why you need to value as well. But also momentum is a great factor to include, because otherwise you miss good capture. So in the book we propose to do with multi factor in the strategies we run we do in multi factor, because there are just too many benefits of integrating those factors. The great thing about the book, how we explain it is the simplicity. So as you say, we sought first of all risk, and then screen on income and sentiments or value and momentum, as you can call it, which makes it very clear that you’re always in the low risk part of the market. So that’s why you would do with double swords. In practice, that gives quite some turnover. Because you kind of flip flopping, it’s a bit technical. So in a professional setting, we prefer to have fuzzy scores when you do an integrated approach, instead of doing this slice and dice. Again, from a 30 1000s distance, both approaches are similar. If you zoom in, you want to keep it simple, then you do a double sword. If you’re more professionally, then you do an integrated approach, which is better, because it saves time. Now.

Corey Hoffstein  46:19

You mentioned a little earlier that a lot of the academic research ascribes the low volatility premium arising from the low beta phenomenon and the bias against leverage or lottery demand phenomenon, all sorts of behavioral sources of the anomaly. Given that it is behavioral, do you think that this is something that can and risks eventually be being completely arbitrage away?

Pim van Vliet  46:46

Yeah, so as a good one, when we say behavior, I think that all behavior is rational. And it’s interesting, because when people say if the behavior is irrational, I think we all make conscious decisions. So if you buy a lottery tickets, you buy hope, it’s rational, I wouldn’t say your, or if you buy a stock is very volatile. It buys even have a negative return. It’s still rational, you’re maximizing your utility, which this may be different than maximizing your Sharpe these are not the same. So the beauty is with low vol is that we understand why people are taking different positions, they have different objectives, different utility function, and they’re perfectly rational, its behavior. And if you say, Yeah, I don’t have constraints, I don’t want to gamble with my money, and my fiduciary duty, you have a different objective. And then you act in the same markets. And then you can have at the same time, get your premiums have I shall be happy, because that’s what you want to have capital growth, lower risk, and at the same time, other market participants are also happy. And that’s the beauty of the marketplace that if you trace there is always a win win, otherwise you don’t try it. So the idea of calm and efficient markets is that there is one representative agent. Yeah, that’s not correct. Of course, that’s too much simplification. And this behavior, which we observe, which is in that sense, also for predictable, is rational, will coexist with some people behaving more risk averse. And those people have a long term view, and they can make their alpha. So in that sense, the Sharpe ratio of point 3.5 can be very sustainable, because there are different kinds of behaviors in the same marketplace at the same time.

Corey Hoffstein  48:33

On the topic of risk aversion. Back in 2013, you co authored a rather heavy paper titled violations of cumulative Prospect Theory in mixed Gamble’s with moderate probabilities, some mouthful to say, at the risk of sort of diluting the work too far. My core takeaway was that prospect theory, which sort of has been the growing darling of behavioral finance for the last decade or so, is actually really only supported when experiments are performed with really extreme examples, not necessarily supported with more moderated examples. Why do you think this takeaway is really important? And what are the implications for asset pricing theory?

Pim van Vliet  49:18

Yeah, so Prospect Theory has been the Darling for decades, basically, wanting to replace utility theory. And the key differences between the two is beliefs and expectations. And what in this paper, we did this in the management science, we showed that classic utility theory is not that bad, it’s pretty good. It can explain our behavior in a rational way. So an expectations framework. And the reason why Prospect Theory sometimes wins in experiments if the experiments are really extreme, and most real life examples are not that extreme. So we basically made a case for expected utility theory and also That’s what I said about rational behavior. So actually, we’re more rational in that sense than a prospect theory wants us to believe. Of course, we have biases, and some of them are clearly irrational. But if it comes to more day to day decisions,or expectations are pretty rational, it’s more of matter. How bad do you think a loss feels, instead of that you overestimate the chance of a loss, these two are difficult to disentangle. And in this experiment, we made it very clear that many of those academic studies basically creates situations which are not really realistic. And then I’m basically pushing expected utility theory back and making a case for Prospect Theory, which, for Marcus is less strong to aggregate, because Prospect Theory is nice to describe individual behavior in very hypothetical or extreme conditions. But it doesn’t aggregate well. And then it’s just storytelling. So for each anomaly of the story, and it doesn’t really help you to better understand markets, whereas expected utility theory and rational expectations are much more powerful to do that. Can you explain what you mean by utility theory, classic Utility Theory aggregates? Well, but prospect theory doesn’t? Yeah, maybe an analogy could be like Newton’s laws that describe an apple falling from a tree. But on an individual level, if you go to sleep, at some level, you need quantum theory, which is more complex, and allows you to describe behavior at a very small level. It’s a bit with finance as well, that behavioral finance can go really to atomic level, but when you aggregate it, it doesn’t, it cannot predict how an apple falls from a tree. So that’s where it stops. If you take expected utility theory, it’s like Newton’s classic laws, it works good on a normal level, a bit, in some cases, not so good on individual micro level. But if you have to choose, then utility theory is a good workhorse still for us surprising.

Corey Hoffstein  52:01

So I’m a real sucker for taking philosophical examples to sort of logical extremes that defined where edge cases exist. And on our pre call, you said, if the market were purely efficient, nobody would ever trade, which I thought was a really interesting concept. And I was hoping you could pull on that thread for me and walk me through the logic.

Pim van Vliet  52:23

Yeah, then it gets a bit philosophical because this represents of agents, it’s a one period model should describe them this whole dynamics of markets, we all trade. One thing we know is that we trade way too much. Oh, Dean has done really great studies on that, and how much trading is going on? Trading also is alpha signal for anybody XF. So it’s an agency problem. As an agent, you’re an active investor, you should try it because people pay your money to take active positions. So there are lots of incentives, again, rational behavior to try to show you know what you’re doing. accidentally doing nothing is very difficult. So we know we trade too much. And then the question is how much trading should be going on and then in a very efficient market, the price discovery, so where the marginal buyer and seller determine the price, it could be just one transaction very, very little, that’s very extreme, and then you can round it up to say, there will be no trading round it. So very, very limited. So that means that basically everybody could go passive. And then just you or me, Cory, we would then determine the prices of the s&p 500. And that sets and then you can say there was no trading. I think we were way too far on the extra space, I think, for its turnover is above 100%. And that’s really a tax on economies. Also having intraday liquidity with all the bid ask spreads on buy ETFs. It’s a really big drag. And I think if regulators would say there’s only one, one moment of trading at the close everything that’s I think that would be a huge saver for us. Citizens, I think it will save 10s of billions at the cost of intraday liquidity. But yeah, that’s you have price discovery throughout the day. That was the birth of that in terms of GDP and efficient capital allocation. So I think having some more Kirpal trading would be a net benefit for society. Of course, brokers would hate it and hfts couldn’t make money anymore. But so that’s a bit philosophy. So we should trade less than next dream. There is even no trading anymore except you me maybe we could settle everything

Corey Hoffstein  54:36

Reminds me of an old Saturday Night Live skit. I think it was a weekend update where those fake news anchors are presenting the news and the news anchor said something to the effect of, “and on Wall Street, no shares changed hands today. Everyone finally has what they want.” And that’s like, it’s like it’s a funny way to put it. So. All right, well, moving on. I want to talk about a little bit about all the research you’ve done.  Cumulatively you have touched on research in the factor space in the utility space. You’ve been publishing research for 15 plus years. And one of the things I love to ask people who have been publishing research for that long is, which views of yours that you’ve long held have become more entrenched, because of the research? And what are the biggest things you’ve changed your mind on?

Pim van Vliet  55:22

Now, one thing I changed my mind is on skewness preference. So when I started my PhD, I saw that the if you really love this positive skew is can explain a lot like why growth stocks and perform high fall etc. But then I found out that in equilibrium that this doesn’t add up. So if you like a positive school, you don’t buy all the lottery tickets, because you will get the million dollar and it’s you lose money. So if you love school, a positive school, you don’t diversify, if you don’t diversify, the market is not efficient. And that was for me sort of a big change there. And that basically close the whole literature. So you still see papers coming out about Coach Kunis. And but it’s simply not true. Because the market is not efficient. If you like a positive school, you buy a handful of stocks. That’s it. You don’t buy the markets, or that’s the market is inefficient instead of efficient from that perspective. So that was reallya changer. Other things that struck me was this tracking error. So I did a PhD in finance, I studied downside risk. I joined the industry. And I was like, wow, benchmark relative risk. They call it an error. While it’s a deviation. Yeah, that struck me and also, that we have a complete different view on risk, and also how risk is perceived. Whereas it lacks a theoretical foundation. So utility theory, as we discussed, there’s no moral first principle why you should dislike relative performance. And that’s fascinating finding as well.

Corey Hoffstein  56:53

Curious as to your thoughts, what you find most exciting going forward, a lot of my conversations about sort of the academic, quant factor space, there really hasn’t been a tremendous amount of true, I’m gonna use air quotes here. For those that are listening innovation. Over the last decade, there’s been a lot of education to the point of, we’ve been able to look at pre sample data or contemporaneous out of sample data from different countries apply a lot of the same factors to maybe different asset classes to try to reprove whether we think these things actually do exist. But the techniques themselves maybe haven’t changed tremendously on the sort of academic factor front. What are you most excited about in the quant research space going forward?

Pim van Vliet  57:38

Yeah. So again, from a high level distance, you can say, what happened since end of the 90s with Robert Horgan, especially if you would have read his paper. He basically discusses the main factors like quality local failure momentum, the Big Four, what’s happened since then, from that level, not so much. One of the things that excites me is to roll it through to other markets. So outside US equities, international, em, corporate bonds, that’s fascinating. And then also into commodities and effects. So that’s one second is a behavioral finance is not only a lens, to look at, why those factors come through, but also a mirror like clients in quant strategies are also human. quants are human, they looking at the performance can be really tough. And learning how to get this beer for finance, to also be able to collect those premiums, that’s going to be fascinating. So maybe you can think of fun structures where you basically acknowledge your own failures, where you put upfront a structure where you enforce yourself to harvester effective premiums, bit of a similar setup as private equity, for example. Or you just lock up your money. And be sure that you will be around when those factors can be harvested. I think that’s something I’m fascinated about is still very infancy. In the US, you have some of the fitness subscriptions where you pay upfront big time, and each time you go you get money back. So from a clinical point of view, that’s very irrational. That’s you and me now that it’s very rational. Because with Christmas, you think I’m going to work out a lot. And then, of course, when it happens, you don’t feel like it. I think you can apply the same principle in investing. And you should, I don’t know, parties doing that already. Professionally, institutional money. Sometimes you can have longer fee structures where you have this penalty if you get out so you you see a bit there. That’s really taking it to the level of the fitness club, and getting a penalty when you get out or when you don’t stick to your guns. Now that’s really fascinating, because over the past five years, I’ve seen this hire and fire cycle being very dominant and affecting Alpha more than when you do volatility or beta or those kinds of things. So that’s the big picture.  Now on the more smaller picture, of course, I still fight for each bit. So any innovation going on on machine learning big data, we take it, because that’s also your fiduciary duty just to keep it pushing the frontier in alpha and new signals, and new variables.

Corey Hoffstein  1:00:17

I was expecting you to say something along the lines of machine learning. I think most quants are pretty excited about that area I love. I love the idea of innovating in structure to deal with behavioral issues. Maybe you can talk a little bit more about machine learning as it applies to conservative investing where you think some of the more interesting applications might lie.

Pim van Vliet  1:00:38

Yeah, so with machine learning is, of course, the tool nowadays. And it’s fascinating, especially since it allows for interaction effects and nonlinearities. Most of the literature now coming out is predicting return. And interestingly, not so much just going into risk. That’s always nice, as a contrarian, when you use these techniques, and you just change the objective, we found some really cool results were allowing for nonlinearities helps you to better predict risk. It’s also intuitive, that’s if you take leverage up to a certain point, it doesn’t really matter. But if leverage gets too high, then you should should really watch out. It’s like a cliff. And most models are linear, linearly structured. So machine learning does help to predict the stress risk and downside risk better than classic linear models. And it’s fascinating and excites me, because it gives you an ash on top of more simple, straightforward ways to predict risk, but volatility or beta.

Corey Hoffstein  1:01:38

Do you think, you know, again, with the idea that a lot of factor portfolios built around the conservative investing idea use sort of historical looking realized volatility, that just being sort of an output the market’s output of all these other fundamental inputs? Do you think the future of conservative investing could be to outright ignore things like realize volatility and realize beta and look at more fundamental, nonlinear drivers of risk?

Pim van Vliet  1:02:07

Yeah. So in the past decades, we’ve innovated on predicting risk, including distress risk measures, most likely adding machine learning. And then when you get excited, you think, hey, let’s throw the backward looking statistical. Just dish it all together and forget about it. And then, again, the truth is often in the middle, there is information in those historical price movements, which is an indication for investors doing this price discovery, the more investors disagree, and the more uncertainty there is, the more Alpha opportunity there is also, because all the biases about being too optimistic, overconfidence, all those kinds of things, then tend to happen more, and going into more the local area is more to save bets. So it could be that this happens, what you say that for fundamental and all forward looking and more. Other data driven approaches are better than historical for and better than they could be driven out. But so far, they have always been a good main staple in this mix of Risk Dimensions.

Corey Hoffstein  1:03:11

Speaking of forward looking one of the easiest forward looking markets for risk you might look at as the options market and it’s something we haven’t talked about yet. Can you comment a little bit maybe on the use of implied volatility, versus realized volatility and the potential benefits or limits to using that as a way to screen for low volatility securities?

Pim van Vliet  1:03:33

Yeah, what’s going on? So implies for is more forward looking, you don’t have to rely on backwards realizations. There is some value in implied volatility, but it spans by historical volatility, it seems to be the implied volatility markets a bit more efficient than the realized volatility. Then secondly, that you have less breadth so you don’t have full coverage for all stocks you want to invest in. But if you then move to the credit markets, where you have credit default swaps and credit spreads, that’s very interesting that those investors really care about downside risk, because your coupon is that the maximum return you can get. So it’s more clean. And then this information on the markets can be very effectively used in the equity markets. And that’s more powerful than implied volatilities so credit spreads beats, implied falls. Again, they’re the same question. Could you then forget about volatility and beta and only use credit spreads and salaries again? No, they all add to each other, and they complement each other.

Corey Hoffstein  1:04:38

One of the areas I can imagine credit spreads being a particularly difficult data set to work with is that the term of which the bonds are issued by these different companies could be very different, right? You could have Disney doing a 20 year bond and you could have some historically junky energy company doing a five year high yield bond. How do you think ofnormalizing for the different terms.

Pim van Vliet  1:05:03

Yeah, so credit default swaps are normalized. So that’s where you can then rely on so that you don’t get this bias.

Corey Hoffstein  1:05:08

So fairpoint that was a much easier answer than I thought it would be. Well, look, Pam, this has been a fantastic conversation. We’re coming towards the end here. And I’m asking the same question to every guest at the end of the season, and it relates to the cover art that we’re creating for each episode. As you know, I asked you to select a tarot card. Again, none of the guests so far have known anything about tarot, which has made this a particularly fun exercise. I didn’t know anything about Tarot going into this. And you actually chose a card that is not part of the standard tarot deck. So it took me a little while to figure out what card you were talking about. But the card you chose was hope. And I was wondering what drew you to that specific card?

Pim van Vliet  1:05:52

Yeah, so I like philosophy, and you have the cardinal virtues, and then you’ve got the Christian virtues on top of that. So that brings us to the seven virtues which faith hope and loveof those three. So I like hope a lot. I hope it’s something different than being optimist. Hope is something you aspire, and in a way, it’s sort of forgotten, under acknowledged virtue, which I think needs more prominence. So that’s why I picked it. Also, in today’s environments, you see lots of the public debates, being less about hope is becoming the like culture wars, but also on climate, lots of pessimistic, like, people thinking, things will get worse and find the rules is going down. I think hope is a totally different message. And it’s also a virtue. So it’s something you should do. It’s something you should train and you should share. So that’s why I picked it it’s being a contrarian, like an investing also picks one of the virtues, which people are least familiar with, at least if I look at my culture around me like hope it’s almost like a sign of weakness like the I don’t know anymore. It just hope I just hope it because now it’s something much more deeper when you think about it. And also when it comes to investing, when actress maybe this pointing here or there. Also hope is more than simple, like yeah, let’s hope it’s a conviction, which I think everybody should be aware of.

Corey Hoffstein  1:07:27

This has been fantastic. Thank you so much for joining me.

Pim van Vliet  1:07:30