The Biggest Lie in Investing: More Risk Doesn't Mean More Return

This episode reveals why low-risk stocks can outperform when markets get ugly, and how quant strategies turn academic research into real-world results. If you want smarter investing insights without the fluff, this one’s for you.

 Low Volatility Investing in Financial Markets: Insights from Pim Van Vliet

Explore the fascinating world of low volatility strategies with Pim Van Vliet, chief quant strategist at Robeco. Discover how academic research translates into practical investment strategies that perform across various market regimes, especially in uncertain or declining growth environments.

In this episode: Part One

• Pim Van Vliet’s background, academic credentials, and practical experience in portfolio management
• The origins and historical context of low volatility factors, inspired by Robert Hogan’s work
• The misconception that higher risk equals higher returns, and how low volatility stocks challenge this notion
• The mechanics of multi-factor quant models combining risk and return factors like beta, volatility, credit spreads, momentum, and value
• The importance of absolute versus relative risk approaches in portfolio construction
• Strategies to avoid sector and market cap concentration risks
• How low volatility strategies perform during different macroeconomic regimes, especially in downturns and high inflation periods
• The impact of market shocks and macro regime shifts on rule-based quant systems
• The role of human emotion dampening factors like low vol, profitability, and quality in long-term investment success
• Practical advice for aligning multi-factor strategies for resilience over decades

Timestamps:

00:00 - Introduction to Pim Van Vliet and his background in academia and practice
01:21 - The challenge of explaining complex investment concepts simply
02:12 - The pioneering work of Robert Hogan and low volatility anomalies
03:37 - The inspiration for Pim to pursue a PhD and challenge conventional risk-reward notions
04:20 - Market non-linearity and investor irrationality as key factors in low volatility success
05:18 - Practical implementation: transitioning academic insights into multi-year strategies
06:42 - Building a systematic, rules-based multi-factor portfolio focusing on absolute returns
07:37 - Misconceptions around risk, beta, and volatility in portfolio construction
08:56 - The significance of risk as probability of capital loss and how volatility predicts risk
09:44 - Multi-factor screening process based on risk and return metrics
10:43 - How combining risk and return factors like momentum and value creates robust stocks
11:37 - Market cap considerations and sector neutrality in factoring approaches
12:57 - Academic vs. practical constraints: sector and liquidity controls
13:24 - Historical backtests of multi-factor strategies from 1926 onward
14:46 - Cross-pollination risks when combining multiple factors and controlling for size effects
16:00 - Sector concentration controls and the importance of risk constraints in practice
17:25 - Performance resilience and the influence of macro regimes on low volatility factors
18:38 - How low volatility strategies behave through business cycles and market shocks
19:15 - The impact of macro regimes like inflation and deflation on factor premiums
20:43 - Long-term stability of low vol and the importance of patience
21:38 - How income and dividends contribute to the resilience of low volatility strategies
22:37 - The role of income in maintaining investor confidence during turbulent times
23:36 - Historical market environments and the macro regime dependency of low volatility
24:28 - How low volatility outperforms in macro regimes characterized by high inflation or deflation
25:55 - The benefits of low volatility in "bad" regimes, including financial repression scenarios
26:29 - Managing shocks and regime changes with quantitative rules-based models
27:37 - The reflection of financial history in low volatility performance during crisis periods
28:05 - Future macroeconomic risks, including inflation and government policies, and their implications
29:36 - How quantitative strategies respond to shocks like financial repression and interest rate regimes
30:47 - The importance of systematic models in exploiting human behavioral biases
31:44 - The advantage of momentum for short-term sentiment response and trend following
33:10 - Dampening human emotion as the core strength of factors like low volatility and quality
33:59 - Strategies for combining low volatility with other multi-factor approaches in future regimes

Transcript

SPEAKER_03 0:00

So it's the probability of losing money and it's also the amount of money you lose. So it's really drawdowns and capital loss. That is risk. That's also what I've worked on in my thesis. And then the question comes, so that's in the end what risk is about, but how do you predict risk? And there are uh simple metrics like volatility, which measure upside and downside deviations, are uh as good or sometimes even better than uh downside risk measures. To predict, so in the end risk is losing money, and how do you predict that? And then volatility can be used to predict risk, which is capital loss.

SPEAKER_02 0:34

There is an anxiety amongst young kids today around, you know, am I gonna have a job? What am I gonna do? What are the skills of tomorrow? What advice would you give them?

SPEAKER_03 0:44

Make sure you got one thing really good so that you're really good at one thing, that's trial and error. AI won't replace your job, but someone who's better at using AI does something, embrace this new technology. It is very fair, so even if you don't have a high expensive education, you can learn so much yourself. The the the knowledge which is in your computer on the internet with tools like AI is enormous. So my advice is adapt it, uh embrace it, learn from it, and see the opportunities. Don't waste your time learning something you think you should learn but you're not excited about. Just forget about that and follow your talents and develop them.

SPEAKER_00 1:31

Welcome to the most important thing. The podcast where leading voices in finance, economics, investment, and geopolitics share the one idea they believe matters most. Renowned broadcaster Ivan Yeats and finance expert Dr. Alan O'Sullivan will uncover for you what actually matters. In a noisy world, clarity is power. Here, we focus on the principles and insights that endure long after the headlines fade. This is the most important thing.

SPEAKER_02 2:02

So, welcome back to the most important thing. I'm Alan O'Sullivan, and today we're getting into something that I genuinely think is one of the most fascinating puzzles in all of finance: the low volatility anomaly. My guest is Pim van Vliet. He's the head of conservative equities and chief quant strategist at Rubico, one of Europe's most respected asset managers. With over 200 billion under management, Pym holds a PhD from Erasmus University Rotterdam, and he literally wrote the book on this stuff, High Returns for Low Risk, published in 2016. Here's the paradox at the heart of it all. Boring, low-risk stocks, the ones finance theory says should underperform, have actually beaten the market over nearly a century of data. That's not a quirk, that's a structural feature of markets, and Pym has spent his career proving it. There was so much in my conversation with Pym van Vliet that I've broken it into two parts. In this first part today, we talk about factor investing, the low vol premium, talk about financial history, and how Pym on a day-to-day basis thinks about markets. So huge learning in this part one of my conversation with Pim van Vliet of Rebico Asset Management. Buckle up for this one. It's a fascinating episode.

SPEAKER_01 3:23

On today's episode, Alan interviews Pim van Vliet. He's speaking to us from Rotterdam in the Netherlands. And he is overseeing a huge portfolio of literally hundreds of billions of investment. And there's a number of terms that I haven't heard before. So maybe you just talk me through this. First of all, what's conservative equities?

SPEAKER_02 3:52

When we hear conservative, we hear probably think defensive, probably think cautious. It's it's lines up with Pym's research, his lifetime research of low volatility. So volatility is this word that the industry Ivan uses for risk. Okay. So what Pym van Bleet has studied for years and actually is a practitioner of is low volatility. There's a low volatility premium, which basically means that if I invest in assets, most let's say equities that don't move around a whole lot or say less risky, I will do better than investing in high high-risk assets.

SPEAKER_01 4:31

So I've seen some of the interview and he talks about low vol the whole time. So, like when I think low vol, I'm thinking of, okay, well, I'm gonna go for government equities, I'm gonna go for bonds where I know over 10 years what return I'm getting. Is he talking in the context of equities? And then when I think in terms of equities, I'm thinking of maybe big utility companies. What exactly is a low vol stock?

SPEAKER_02 4:55

It's a good question, right? And you're right that it's not just equities. The first 30 minutes of the interview is focused on equities, right? Because that's most of their shop is uh equity investors, right? But that premium or that um that that low vol premium that he has identified can be replicated in other assets. But back to equities, okay? Low volatility says that there is a premium by investing in assets that aren't volatile, okay? And the logic is that if something doesn't move around a lot, particularly in a stressed environment like 9-11, 2008, COVID, it won't fall very steeply. And if it doesn't fall as steeply, it recovers quickly. But give us an example of a low vol investment. A low vol, you mentioned utilities, okay? But a low volatility investment could be a tech company, right? And there is a, well, why is that? Look at Microsoft. Uh, when we look at the most recent uh uh spout of volatility, okay? There's a flight to quality. What low volatility to me says profitable companies, strong balance sheets, strong leadership, maybe a history of paying a dividend. Okay. So some companies like Microsoft that have hundreds of billions on their balance sheet are actually safer bet than some sovereigns, you could argue at the moment. What's a chief quant strategist? So that's his title in Rabico. So chief quant strategist is sure for chief quantitative strategists. So it's numbers. It's not, I think, I feel, I believe, it's let the data speak. I run my regressions, I run my data modeling. They use uh very high-frequency machine learning uh models, but essentially you're using data to inform your investment process.

SPEAKER_01 6:42

For viewers of this, would it be true to say this is slightly more technical? Like it might be beyond my pay grade. It's probably beyond my pay grade as well, Ivan, right?

SPEAKER_02 6:50

So, yes, it absolutely is technical, it's a systemized process. So essentially, you've got a lot of uh data inputs feeding the model, okay? Uh, and we talked in about okay, but is if your if your model is fed by past historical information, how relevant is that to today? Okay, that's a big criticism. Uh, but we get into a bit of that in the interview, and it's insightful because he talks about other inputs. He didn't give me all the secret sauce, but he gave me a bit.

SPEAKER_01 7:19

All right. So Rubico is a huge investment fund, trusted by many clients and investors across the world. Here is Alan talking to Pim van Vliet.

SPEAKER_02 7:36

Delighted to say that joining me now, all the way from the Netherlands, is Pim van Vliet. He is the head of conservative equities and chief quant strategist for Rubico. Rubico is an international asset management firm headquartered in the Netherlands, founded in 1929. That's an interesting date for those financial historians. It specializes in active investment strategies for institutional and private investors. And Pym is responsible for a wide range of global regional sustainable load volatility strategies. He joined the firm in 2005. And what I like about uh Pym Van Bliet is he's an academic but also a practitioner. He puts money to work. He holds a PhD and a master's cum lauda in financial and business economics from Erasmus University Rotterdam. He's the author of numerous academic research papers, including publications in well-respected um academic journals, including the Journal of Banking and Finance, Management Science, and Journal of Portfolio Management. And finally, he's a guest lecturer at several universities, author of investment book, and speaker international seminars. I I was reading the book you wrote for your father, and I think that was a nice touch. Uh and I in previous interviews, Bim, you've said that it could be very easy to explain to talk about something that's complex. Try to explain uh something in the common man's uh language uh in a simple terms is very, very difficult skill, and very few can do it. But you're very welcome. How are you?

SPEAKER_03 9:07

I'm fine, thanks for having me, and uh looking forward to a good in-depth conversation. So thanks for having me.

SPEAKER_02 9:15

In terms of your early career, you were reading the works of Robert Haugen. I actually have one of his books here, Modern Port or Portfolio Theory, and it just occurred to me when I was listening to that, he spoke about low volatility himself and Heinz in 1972. But what what were you looking at that that made you identify uh and and and see this low volatility factor? How come you saw it and no and uh and not many other people identified it?

SPEAKER_03 9:48

Yeah, it's good that you mention uh Robert Hogan, so he is one of the pioneer cons. He basically in 1975 he found l that COPM didn't hold um that was confirmed by PharmaFriends themselves in 1992. So basically I did my uh undergrad studies in the late 90s, and then I run on to this work of uh Hogan. So uh and yeah, he basically showed that you could be the market with lower risk using uh low volatility, low beta. And I was really surprised, uh yeah, shocked is too bit of a big of a word, but more is this really true? Because yeah, the the lesson one in finance is that risk is rewarded. And what I like about Hogan's work is that Hogan, as you said, had a way of making complex things simple. He also has a book called The Inefficient Stock Market, and that's really fun to read. Uh, it has this uh umbrella uh with rain on it. I don't know whether it's still uh uh sold, but I read it and it's so great fun. He makes the soap opera out of uh academic debate where he boxed Chicago economists uh for their their efficient market uh belief, uh very dogmatic, he makes fun of it. So basically he inspired me to do my PhD, which started in uh in 2000. Yeah.

SPEAKER_02 11:18

So I mean if if you spend any time studying financial markets or being a practitioner, you you understand that markets are not linear, investors are not rational, and uh nothing is constant. So I mean, you started out, you you you you did you read the works of of Hogan but and Heinz, but what was it uh in terms of the appeal to you from a practitioner's perspective? I mean you you're starting out, you're looking at this and you you identify something. Did it take you long to realize that yes, this is this is something that's that needs further study?

SPEAKER_03 11:57

So like I mentioned, I I did my PhD to Dick further because you can read a paper a bit or study, so I was not directly convinced. So I replicated everything, updated data because the the old studies had all data. And I again confirmed this. So for for the listeners, the people who are in, you can lead the market with lower risk. That's you could say the biggest anomaly, the biggest alpha out there, yet the most difficult to grasp and to understand. So when I did my PhD, I uh I was giving risk still a chance. So I said, could it be that maybe those factors like low vol or value investing that it might pick up some risk, uh which is maybe non-linear, uh downside risk, fat tales, uh long story short, uh no, uh not really. So then it was 2004, and then I said let's let's go to uh practice. So I joined uh a great team of uh Kans researchers, very clean data, uh like it was like a candy shop for a for a little kid, and there I said, Hey, let's let's take a look at uh low risk again, and there I was finding out that in practice risk is measured as relative risk, which means not losing money but lagging the benchmark. That sounds similar, but it is a big, big difference, and there yeah I I was almost shocked because yeah, trackinger as it's called, or uh benchmark deviation, active risk. There are three words for it, that that low-vol or low-risk low beta stocks have a problem that they deviate a lot. The when markets go up the lag, when markets go down they outperform. So it's huge active risk, huge huge relative risk. And there the businessman or the entrepreneur inside me, or the contrarian, said, Hey, if this is difficult to arbitrage in practice, maybe that's why the the alpha is there in the first place. And that's where we started building a whole new strategy, not based on uh relative outperformance, but on absolute performance. So that means forget about the benchmark, just take your cash as a starting point and then have a stable return, uh, and then automatically you will get a huge loading on uh low volatility, low risk, but also a loading on other proven factors like value momentum. If you combine that, you've got a beautiful strategy. And yeah, I've done I've been running this strategy now almost for 20 years. Next year we celebrate it. And uh yeah, it's great to turn academic insights uh papers into real-life multi-billion uh strategies for clients.

SPEAKER_02 14:46

So so you've mentioned low beta, low vol, low uh low risk. Okay, so for the layman, we're talking you from a plain English perspective, we're saying that modern uh modern portfolio theory, everything you learn in 101 portfolio theory is you need to take uh more risk to get a higher return. And what your um research and your work is saying is that's completely wrong. You can actually uh generate uh positive returns by getting exposure to low risk. Now, this is where I have a bit of a issue, okay, and feel free to push back. We do are you defining risk, low risk in terms of volatility and beta? Because your volatility, as we know, is the dispersion around a mean value, and beta is the sensitivity correlation to a market index. But how is that risk in in the concept of risk?

SPEAKER_03 15:44

For me, it is uh loop uh rule number one of Buffett is don't lose money. So it's the probability of losing money and it's also the amount of money you lose. So it's really drawdowns and capital loss. That is risk. That's also uh what I worked on in my thesis. And then the question comes so that's in the end what risk is about, but how do you predict risk? And there uh simple metrics like volatility, which uh measure upside and downside deviations, are uh as good or sometimes even better than uh downside risk measures. So to predict, so the in the end risk is losing money, and how do you predict that? And then volatility can be used to predict risk, which is capital loss.

SPEAKER_02 16:32

Okay, so in terms of your your process then, you mentioned multi-factor models, so I'm assuming you're talking about combining low vol with uh profitability, maybe momentum factors. So maybe give the listeners, viewers an overview of how the process works, Pin, please.

SPEAKER_03 16:51

Yeah. So uh for those listening indeed, so uh to you you could think about it as a screener. So you take a global universe of say 5,000 stocks, or you take a regional universe, uh, it can be for the the Irish stock market or the European stock market. Basically, what you then can do is give scores to all of those stocks, and then we score them on risk, uh, and then we use several metrics. So volatility, as you mentioned, so that's the the price movement. Then we use beta. Beta is very simple. If you have a beta of one, you have the same risk as the market, systematic risk. Then you've got low beta and high beta. So we also use that, so you get a score for that. Then we also use distress risk, so that's for example credit spreads. Uh that's uh the the bond market gives you information on the risk of a company. And what we find is if you combine those three, so uh beta volatility and you uh and um and credit spreads, and then you say uh I score them so a stock which scores well on all those three risk measures is most likely to be very stable, very low risk, and not lose money. And that's what I mentioned, that's the aim. Don't lose money, that's that's the the most important thing. And then on top of that, we add uh other factors which are more return factors, uh, and there you can uh think about to make it very simple because the models we run uh are using many, many metrics. But very simple, you take the the shareholder yields and you take momentum, and then basically you've got your value quality uh momentum metrics. You also give scores on that, and then a stock, a super stock, as Hogan calls it, a super stock is a stock which scores well on all those metrics. So it's low risk, it's cheap, it has good momentum, and those are the stocks you then systematically buy, so it's rules rules-based, what we're doing, and those are the stocks you put in a portfolio of say 100 to 150 stocks, and that portfolio is what you then automatically every month rebalance because some stocks get uh their scores go down, their rankings go down, and then you replace them with uh stocks which work well. So this is in a nutshell a quant rules-based systematic strategy using multiple factors.

SPEAKER_02 19:14

Is there a market cap um rules rules-based approach to this as well? Um, do you factor in the size of of the stocks?

SPEAKER_03 19:24

Yeah, so usually when you do this at scale, you want liquidity. And so that's why I said if you take globally the la the largest 5,000 stocks. But if you're uh a retail investor and you do it with your own money, there's no need for a liquidity constraint, or at least not as high. So you can do this there, you have an advantage compared to the uh the big the big investors because you have less market impact with your trades. So market cap um yeah, so I don't have a strong vision on it. You see now that small caps tend to be more cheaper, uh more distressed, so we're a bit agnostic there. What the beauty about small caps is that there are many of them, so you have lots of uh possible uh stocks to choose from, and with a quant approach, it is all a numbers game. So you don't if your uh if your hit ratio is like 51%, and if you then do that 100 times or 200 times, you have a better probability of winning. So that's why quant portfolio tend to have more an uh a tilt towards uh mid-small caps, uh away from the mega caps. Uh, but that's more of an outcome of the uh the process.

SPEAKER_02 20:33

I I was reading something you you you you published and where you looked at a study you did, I think it ended in 2020 twelve, began in 1926. Does that sound right? Something something like that. And but you had screened from market, you started with uh thousand largest US stocks, and then you you you you control for dividend yields and quality, and then you you you reduced it down to the hundred by list. But I was just wondering, you know, obviously with factors, multi-factors, you've got this kind of cross-pollination concern, you know, that you know you're looking for value, but if you're buying, if you're controlling for size, then you're obviously getting some momentum there because mark structure of the market is such that market cap drives momentum. So how do you control for uh that cross-pollination impact with mar with factors?

SPEAKER_03 21:31

Yeah, so you refer to uh this study which is published in the book you mentioned. I uh wrote it with John DeConing. So for those listening, that's where the details are. We also did an uh study more uh in the Journal of Portfolio Management. And that's if you Google that, it's called the Conservative Formula. If you Google on that, uh there's also a weekly on that, and uh then listeners can really see what it is about, both in the book as in the academic paper. We also showed that this formula. Can be applied to the largest thousand stocks, as you said, but it can also be applied to more small mid-caps. And you what you see is that the formula works in both market segments, and you're right that if you take larger stocks, those tend to be more momentum once they get in. So it becomes then a bit technical. But what we then do is that once a stock gets in, you don't throw it out directly when it uh passes the the top thousand threshold, otherwise you get a mechanical tilt towards momentum or away from momentum. Uh but that can be fixed in uh and that's what we show in the paper, and that's not driving results, but it's something to be aware of. That's indeed uh right, Alan. Yeah.

SPEAKER_02 22:47

And the other the other the other obvious thing is you know, how do you avoid sector and industry concentration? I mean, if you're looking at quality, low volatility, you're going to be pushed, you know, a naive screen might look at utilities type type type sectors. So how do you control, I presume it's through minimum weights, that type of thing?

SPEAKER_03 23:08

Yeah. So in the public uh papers I share, so that is uh to inspire uh people to consider low-risk investing, and that's where we have the paper, the book. That's very simple. In those studies, we don't control for sector concentrations or sector effects. So these are all um yeah, bottom-up stock selection, and then you could have 40% in one sector. In the daily job I do uh at Rubico with our quant strategies, that's where we obviously have risk controls in place. So uh one way to do it is to have relative risk constraints. So if um IT is uh 30% of the market index, then you allow the strategy to have a bandwidth around 30. At that bandwidth, it could be five, could be ten. So that you basically say uh I want to compare stocks, uh apples to apples, I want to compare uh IT stocks with uh IT stocks. Uh with these bandwidths, you do allow for some uh sector allocation, and yeah, that's the cool thing about being part of a big quant group, we have uh more than 50 professionals, then you can fine-tune these parameters where you can say, hey, what if we put the bandwidth at zero, what if we put it at three, what if we put it at seven? We look at all the data, you look at the US, you look at uh emerging markets, you look at countries, and then you find uh the most robust setting which works and which is also also easy to understand because you can there also have the most complex rules or you can have the more simple rules. So that's how we do it in practice. So we do have sector constraints in place, and in the more the academic uh world, and that's interesting because I stand in both worlds. I write papers uh and and I do daily investing. That's you hit on exactly the point that most academic studies don't have sector constraints, hardly any. And in practice, hardly any strategy doesn't have uh sector constraints. So that's that's very yeah, how do you say that remarkable to see that there the two approaches differ and like fifth value or quality, you mentioned quality, yeah, that's the that makes a big difference whether you do that sector neutral or not. Well, if you dig into the literature, like uh you take the QMJ from uh from Cliff Esnus, or you take the uh Novi Marx uh or the Pharma French quality uh profitability definitions, it's all there are no sector uh constraints um in there, and that matters.

SPEAKER_02 25:48

In terms of the um performance of low volatility, we all know that staying invested uh is one of the best things you can do. You write through the business cycle if you have time in the markets. So does the low volatility premium struggle, presumably on a shorter term time frame? I mean, do you need an elongated time series for this to consistently show positive results? Uh and how and maybe a second question will be how does it look on a rolling three to five year uh performance basis?

SPEAKER_03 26:23

Yeah, it's a good one. So I've I've been having experience with Lowville, or we call it conservative investing, for 19 years, which contains multiple cycles. Uh, I could roughly split it in two parts. So the first part were the GoGo years, where the uh the the Lowell was doing terrific, outperforming, um offering protection. Basically, the second part is is more mostly uh a strong bull market in which Lowell tended to lag. The funny thing is in both subsamples we had uh a return which was eight, nine percent, so the return went up, but the the benchmark in the first period was way lower, it was like four percent, five percent in the second part uh the benchmark, the market did more than ten percent. Uh and then that meant we were lagging. Now the question you pose is is very relevant, so can you stay invested? Uh because that's that's what matters. And there's tons of papers showing that people's uh uh investors tend to be bad at market timing or style timing, so they enter at the wrong moment, they leave at the wrong moment. Um if you look at the rolling period, you could compare it with equity investing, for example. Uh, if you look at the equity premium, hardly anybody doubts the equity premium nowadays, and why is that? Because it did very well over the past decade. I'm a bit worried here. So is there still a big equity premium going forward? It's not big, it's there, but uh and it can be absent for a decade. And here the interesting thing is that realized returns are inverted related to expected returns. So Anti Ilmana, uh yeah, quant uh I can recommend his work in his books. He has a book Investing in the World of Low Returns, and that basically he's really working out his thesis of uh that returns might be very low going forward, especially uh when corrected for inflation. Now let's go back to low volatility. So on an absolute basis, uh excellent, extremely stable, 8-9%, no matter what market cycle, but relatively it was uh very time-varying. Uh, if you do this on a rolling basis, it the the rule tends to be the longer your rise and the more stability you get. So patience is rewarded. What also helps is that I mentioned the benchmark already a couple of times. You can also say, and we see investors do that, they say I make a strategic call for low volatility, and then I do a tactical call for a manager, and I tell the manager to outperform the low-vol benchmark. So many of our clients also do that, and then the time span becomes shorter because then these huge market swings are not uh influencing your relative performance. It's really how you whether you have to write low vol stocks, uh, so you you have bad low vol stocks and good low vol stocks, and then you see that it becomes easier to stay invested if you organize yourself like that. Now, that's more for the institutional investors how they can crack it, and then more the mom and pop investors. Um, yeah, for them it's important to say, hey, let's uh let's not do FOMO, uh, don't listen to this uncle who made uh 100% with a single stock, and that they just say um yeah, I'm happy with my 8%. And there, what also helps is income. So um, in the long run, most of the equity premium is coming from dividends and share buybacks, and you see with Low Vol, that's even higher. So Lowell uh is basically outperforming the market by one and a half percent per year, and that's all contributed to that they also have a higher income. So the price appreciation is the same, dividends is higher, and what I also see uh yeah, investors in Lowell, if they focus on their income, which is uh why they do it, they that also helps them, it's sort of a life hack to stay invested in stable stocks, so where they say, Hey, I focus on my income, others make tons maybe tons of money, but I I want capital protection and I want income. These are the sort of the yeah timeless uh benefits of investing, uh capital protection, capital growth, uh, and income. Um comparing yourself uh that can make your life really, really hard.

SPEAKER_02 30:44

J just looking uh back again to um Haugen, he's his study was between 1929 and 1971. And you know, I think when you've studied financial markets, you become very skeptical of the starting dates and end dates. Um you know, 1921 was the start of the worst financial crisis in history. We know that the 60s was very problematic for equities as well. So I'm just wondering in relation to the underlying or the overlying macro regime, have you found any evidence in your research of the low volatility effect uh being more pronounced in a particular market regime? Um, and perhaps maybe it's not as obvious as what I think it might be?

SPEAKER_03 31:35

Um yeah, it's a good question. So uh there is dependency of uh the low vol premium, depending on the regime. The the most easy one is uh a bull market. So if you have a stretch of 10 years, and that happened in the past, in which markets are really strong, that's when low vol stocks are also strong, but less strong, and so they lag on a relative basis, and they shine more in uh like the 30s, the the first decade of the the Hogan Heinz study, so that's 1929, 1939, that's when the low vol premium is really big because equities are flat uh while our low vol stocks uh offer a decent uh return, like seven, eight, and then in uh more of the like the 60s, uh you see uh stocks to do better, so there's a clear uh macro regime. You could say inflation, if inflation is around one-two percent, that's also tough for low vol stocks. Uh you see uh because that's really good for equities, that's the sweet spot, Goldiloc. Anything else, so either deflation or inflation, uh that's when low vol, but also quant strategies in general factor premiums are much bigger. Uh, and that's also what we wrote down in a financial analyst journal study where we looked at regimes explicitly. Uh, stagflation is the worst of the worst. With stagflation, you don't make money with equities, you don't make money on your bonds, and that's when uh yeah, there's hardly anywhere to hide, except for more the the low-val stocks which are not ex which are cheap quality, that's where you can still find some return. Whereas a classic 60-40 portfolio is a really uh down yeah down big time. So that's the environment where you hardly can protect your capital or grow your capital. So basically the bad regimes, that's when they shine. The good regimes is when they don't shine, and yeah, from a personal perspective, you and me and people listening, yeah, that's also when you don't need the money. So if markets are doing 20, uh, you're rich anyhow. Uh you have a job, things are fine, there's political stability, uh all fine. Yeah, you you need you need your money when you need it most, and that's more in the uh when there's high inflation uh prices going up, when you lose your job, when and that's when you want your capital to be uh safe or even grow.

SPEAKER_02 34:06

It makes sense. I I've spoken to the likes of uh Professor Russell Napier, Edward Chancellor, Dr. Lacey Hunt, some you know fabulous conversations about financial history. And I mean Russell Napier has this financial repression theory that he's been talking about for a while, but we're starting to see evidence of it now. And I'm always reminded of the quote from uh Jeffrey Gunlack a couple of years ago where he said, What if um our understanding of financial and markets is informed by a set of economic relationships that no longer exist? So that is just climbing interest rates over the last 40 years, falling inflation, meant the correlations between stocks and bonds, and it worked. And his view, amongst other people I've interviewed, is that it it's we're heading into, as you said, the bad environment. So low volatility to me seems something a lot more interesting in such an environment. Uh would you agree with that?

SPEAKER_03 35:12

Uh yeah, it basically confirms what I said. So in bad times, low vol stocks shine. Uh the study I mentioned uses 150 years of data, so it goes back to 1870 to 2021, so that's uh 150. And the thing with macro and long-term cycles, if you mentioned the 40-year regime of uh declining interest rates, yeah, if you split up your 150 years, you have like three, four of those regimes. That's why I really like financial history, I like common sense, and as you see today with aging populations, um high depths, yeah. The dominant scenario is that governments inflate their way out. Uh, you see that now happening in the Fed with a dual objective, but they will look more at labor markets than at uh inflation going forward, that's already mentioned. So that's really bad news for uh investors who uh have basically I see two big risks that is either inflation or stock crashes, and um yeah, with financial repression, as it's called, uh yeah, you're really running lots of inflation risk, and low fall stocks offer better uh protection in that environment than the broad stock market and also bonds, because some people say, hey, let's go to bonds, but that's also not a good protection in an inflationary environment.

SPEAKER_02 36:42

How does a rules-based systematic system that you have deal with a shock like that? I mean, when we talk about financial repression, we talk about kind of national capitalism. Let's say that countries are mandated to buy their own government bonds. We let's just hypothesize that Japan Japan, you know, you're you're no longer buying US assets, you're buying Japanese government bonds. So if I want to buy something, I have to sell something else. And the obvious one is US equities. Say for Germany, huge buyer of US equities. So what happens? How does your system, I suppose, how does your models deal with that uh in terms of a rules-based system?

SPEAKER_03 37:26

Yeah, so the strategies are always invested in stocks, so we don't make cash equity decisions, uh, we leave that to the to the client. Now, with low volatility strategies, there's less need for that because the mar uh there's less timing necessary. The beauty about quant strategies is that they react to uh uh they exploit human behavior, and through time sometimes people say, Yeah, you've got uh tests of low vol or quant strategies going back to the 19th century, but it was a different time, different regime, different technology. And then we say yes, uh, but what is constant is our human emotions. Uh, that's free fear and greed, envy, over optimism, underoptimism. Those cycles are though they rhyme, they come and go. And what is constant uh what quant models are good at is taking the emotions out and uh basically taking positions contrary to the emotions in a systematic way. Uh that it means profiting from underreaction, overreaction. Um people overpay for growth, people overpay for risk. And if you and they that has always been the same. That's the book of Morgan Housel, uh always always the same. Uh and with quants, it is not that we make predictions on financial repression or whether we go in inflation or not, but more we can predict a little bit, so 51% I mentioned that on a stock basis people tend to make a small mistake, or a small, how do you say that, due to the bias overprice a security. And but by picking that up in a systematic way, we can uh make profit out of that, whether it is inflation, whether it is deflation, and the most uh and we we make uh money consistent uh across all the regimes, and like I mentioned, the most difficult one is when markets get concentrated, and basically uh this cross-sectional differences between stocks, they basically all merge to one bet, like AI or not, that it becomes or is the Fed gonna increase or decrease? So if we're all gonna look at the same piece of information, that's when quant models uh still work but uh uh work less well than in markets where there's lots of information affecting lots of prices. So, long story short, how does our model respond to that? So with momentum uh we have in place, it's really good to play short-term sentiments. So if if a trend's building up, momentum is great in adapting and just saying, okay, let's let's do this until it gets really expensive, their value kicks in, and then you start selling because it's expensive. And that's uh model you build on a rainy Monday afternoon, and then when the practice starts and the going gets off, then you then you see beautifully how all these rules interact uh and respond to anything. Because like we had Trump moving markets, then people say, Hey, how does your model respond to Trump? And then the answer is yeah, the model doesn't respond to Trump, but more how people uh price securities and then exploit this human bias of over and underreaction to news.

SPEAKER_02 40:47

As I'm listening to you, I'm thinking that the best performing factors are the ones that dampen or dilute human emotion the best. Um, like maybe low volatility, it dampens human emotion, maybe profitability, quality, you know, dampens the human emotion because it's fundamental based, it's it's tangible. Um so we're looking out to the next uh 20-30 years where we're in a new regime, and if you're trying to dampen the fear that's going to be there, more or less fear, I would say, it seems to me that a low volatility uh approach would be the way to go. But what would you combine on a multifactor approach? What would you combine with low vol? I know there's multiple strategies, Pim, but what generally might work well there. The Most Important Thing podcast is sponsored by Priya Wealth Management. For those watching or listening who are serious about building, protecting, and structuring wealth, professional advice can make a meaningful difference. At Pria Wealth Management, we work with individuals and families who want a considered, discrete, and highly personalized approach to investments, pensions, and long-term financial planning. If you would like to discuss your own circumstances in confidence, you can book a call directly with me using the details in the show notes. Now, back to the show. So that wraps up part one of my interview with Pym Van Bliet over at Rubico Asset Management. So I hope you found that useful. The low volatility premium is something that always interested me and first came across at Pym Van Bliet's work when I was doing my own uh PhD research on optimal acid allocation. So we've loads more to come and part two will be released next week. Thanks for staying tuned. Keep an eye out actually on Friday for a new series that we're going to do, which is taking some of the highlights from some of viewers and listeners' favorite episodes where we're going to highlight some of the key learnings because most of the conversations are pretty dense. We do go into the most important thing of some of the best and brightest in the world. So, as a consequence of that, they're going to be technical, and I want to make sure that ever this is accessible to everyone from young graduates, students, to retirees to career professionals. So, in saying that, we're going to start with Dr. David Kelly, who's one of our favorite guests, chief strategist with JP Morgan, and look at the key takeaways from that interview. I'm going to try and cap these insights series to maybe 15 minutes max. So taking the key lessons from one of the best operators in the world. Thanks for tuning in. We appreciate your support. We could always get more support, and how I keep getting these guests is if you give us a review or give us a star rating. If you find you're getting value from this, leave a rating please on Spotify or Apple. It does help us out. And I should say that any information given here is for information purposes only. It's not financial advice. You should seek professional financial advice before you put any of your hard earned capital at risk in these perhaps crazy markets. Until the next time, thank you.