My guest in this episode is Daniel Grioli. Daniel cut his teeth in the industry at Deutsche Bank in London, where he was responsible for valuing structured equity and hedge fund of fund products targeted at continental Europe. His timing of joining Deutsche, while perhaps somewhat unfortunate for him, proves fortunate for us as he retells a few war stories and lessons learned from the desk leading into 2008.
During the crisis, Daniel found himself back in Australia working for a pension fund, where he made a career in manager evaluation, selection, and combination. That makes Daniel somewhat unique among prior podcast guests, as he provides us some insight into the decision making of capital allocators on the other side of the table.
The breadth of managers evaluated gave Daniel some unique insights that he shares with us around where he believes the limits of quantitative and discretionary management lie. He also shares his framework for manager selection, which he calls Via Negativa.
Presently, Daniel is leveraging this experience to build what he calls a “best ideas” portfolio, exploiting 13F reporting data to create a high conviction equity portfolio for his clients.
Finally, we talk about the i3 podcast that Daniel hosts and some of the most interesting guests he has interviewed.
Without further ado, my conversation with Daniel Grioli.
Corey Hoffstein 00:00
Are you ready to go? All right 321 Let’s do it. 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.
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 research funds on this podcast. All opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound 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 newfound.com.
Corey Hoffstein 00:53
My guest in this episode is Daniel Greely. Daniel cut his teeth in the industry at Deutsche Bank in London, where he was responsible for valuing structured equity and hedge fund to fund products targeted at continental Europe. His timing of joining Deutsche while perhaps somewhat unfortunate for him preusse fortunate for us as he retells a few war stories and lessons learned from the desk leading into 2008. During the crisis, Daniel found himself back in Australia working for a pension fund, where he made a career in manager evaluation selection in combination that makes Daniel somewhat unique among prior podcast guests, as he provides us some insight into the decision making of capital allocators. On the other side of the table. The breadth of managers evaluated gave Daniel some unique insights that he shares with us around where he believes the limits of quantitative and discretionary management lie. He also shares his framework for manager selection, which he calls via negativa. Presently, Daniel is leveraging his this experience to build what he calls a best ideas portfolio, exploiting 13 F reporting data to create a high conviction equity portfolio for his clients. Finally, we talked about the i three podcasts that Daniel hosts and some of the most interesting guests he has interviewed. Without further ado, my conversation with Daniel grill. Daniel, thank you for joining me today.
Daniel Grioli 02:25
Thank you, Cory. Pleasure to be here in sunny Southern California.
Corey Hoffstein 02:28
So I want to start off before we even dive into your background here, you are known as the market Fox, you’re gonna have to give me a little bit of background on where the market Fox terminology came from.
Daniel Grioli 02:38
Okay, so I was thinking about writing a blog. And a few of my friends in the industry suggested that starting a blog would be a good idea that it would be cathartic. Because when you work in a large institution, the research agenda is often set by what’s happening in institution. And so there’s a lot of ideas that are interesting that you don’t necessarily get time to develop at work. And so my friends thought a blog would be a good way for me to develop these ideas and put them out there and share them with people. So the next question we had to settle on was a name. So we’re thinking about different names. And the idea behind market fox comes from a story that’s been told in several forms, but was probably made recently more famous by Philip Tetlock in his book expert political judgment. So he used this historical I think it traces all the way back to ancient Greece, this historical Allegory of The Fox and the hedgehog. And the Hedgehog is good at one thing, very good at it burrowing in the ground, whereas the fox is good at many things kind of a generalist. And the way he applied that was in his book expert political judgment, that the forecasters that tend to get the most attention. The hedgehogs, they tend to have very fixed views. And they’re very good at coming out with certainty. And people respond well to certainty. So they would get the TV guest appearances and they would be the ones in the limelight. The foxes, on the other hand, were highly uncertain. They’re always hedging their bets. They understood the opposite side to the view that they had. And they made the most unappealing guests because of that, because they didn’t have that certainty. But ironically, they were the better forecasters because they had an open mind. So I wanted to capture that idea. We talked about there being bulls and bears and sheep in the market and pigs. I want it to be the foxes. So that’s where market fox came from.
Corey Hoffstein 04:43
That actually makes a really nice segue that I didn’t intend. But I have to assume sort of that Fox like mentality for you comes from your pretty eclectic background and talking to you about all the things you’ve done in your career. Maybe you can rewind for me and talk about where your career really started and what gave you the bug for finance?
Daniel Grioli 05:01
Well, it’s kind of an accident, to be honest. And I think because it was accidental, I didn’t come up through that traditional accounting or finance or math background. And that probably has had a big effect. So, for me, investing really started watching my dad, in my early teens, so we’re talking early 1990s, Australia, my dad was self employed. So he had a fair amount of time, given his work schedule, and a friend of his who was a very successful accountant and investor helped him get into the share market. And that was actually a great time for Australian shares. Because you had a tailwind, Australia had a very bad recession, during the early 90s, double digit interest rates, property market that was down in the doldrums. And a lot of that started to unwind and go the other way. And at the same time, there were a lot of companies that were deemed virtualized, and a lot of public companies that went private. And those IPOs went on to be very successful. So my father, not through any brilliance, but he ended up owning a bunch of these and doing quite well out of it. So that piqued my interest. And then when I started working part time, I saved up a bit of money, which was a few years later, I ended up buying my first stock, which was Commonwealth Bank, I think I bought about $1,000 worth with some money I saved from after school jobs. And I kind of added I didn’t really sell much I kind of added when I had money. And after a while I started to take it a little bit more seriously. And I don’t actually remember how but I think I stumbled across technical analysis first. So I remember building some simple Excel models, measures of trend looking at things like how to calculate a moving average convergence divergence indicator. And it was I was interested in actually building these indicators for myself and seeing how they worked, rather than just going online and looking at a chart. And so that was interesting. And gradually over time, I started to build out a little bit of a portfolio. And that portfolio became a bit more serious after I dropped out of university. So I was accepted to university, I was studying psychology. And that’s something that still interests me very much the way the human mind works. And I learned a lot of things from that, that I think have carried through to the way I invest. And probably the biggest thing I learned from the psychology background was that humans have an incredible capacity to change. But that I shouldn’t bet on that, because we’re usually creatures of habit. So even though we can change so much, odds are we don’t. So University was fun for a while, but I kind of got a bit bored with it to be honest. And I put this down to the stupidity of youth, short sightedness, I kind of lost track of why I was there and what I was trying to achieve. So I quit not far from the end of my degree, which is in hindsight, stupid. It made my life a lot harder later on. But by the same token, I look back at that, and I look back at some of the things I learned going through things the hard way. And they’re probably been valuable lessons that I hope to spare my children from learning as they get older. So I left uni and I found my way into banking. And I’d moved from Melbourne where I was living to Perth. And while I was in Perth, I had a lot of free time, I was living in a small one bedroom apartment, I didn’t have a TV. So I started to read up, I wanted to get more interested in investing. And at that time, I started reading a lot of value investing books. So I remember reading the Intelligent Investor by Benjamin Graham and value investing by Bruce Greenwald. When I went to the laundromat to do my washing, so bring the book, I’m stuck for an hour while I’m waiting for a load. So that’s how I got through the value investing information. And I was buying more stocks at this point, I’d borrowed some money from my dad. And I was actually using a bit of margin debt as well. And this was a great period in the early 2000s, where the Chinese boom was just taking off. And the Australian stock market over that period, I think from memory went from around three and a half 1000 on the index all the way up to close to 7000. I think before the financial crisis took things out. So I was getting more involved. The portfolio was doing well. Within the bank. I’d moved from a business banking unit into more of a market oriented unit. I was working with foreign exchange. And a lot of people that I met there were living or had lived overseas. And they were saying to me, Look, you’re young you enjoy this. You’ve got to get over to work overseas while you still can before you have family and other commitments. So they put that idea in my head. And after a while I bit the bullet and I decided to move to London. And as it turned out, no brilliance on my part sheer luck. I liquidated my equity portfolio, because I thought, I’m not gonna have time for this while I’m living in the UK on a working holiday visa for 12 months. So I sold everything and bought a small apartment back in Melbourne where I lived. And when was this? So this was 2006 2006 2007. Yeah.
Corey Hoffstein 10:24
So from my naive American viewpoint potentially sounds like rolling. A an equity portfolio that had been on a bit of margin into real estate prior to the crisis sounds like both a bit of luck and perhaps a bit of catastrophe. Maybe you can touch a little bit upon how the great financial crisis manifested in Australia, and maybe how it was a little different than it was here in the US?
Daniel Grioli 10:49
Well, I always naturally been focused on the downside, even before I understood the idea of asymmetric payoff, like I do now, I naturally kind of behave that way. So when I was looking at this portfolio, I was sitting on all these gains, and I thought, if I realized this, I’m going to be up for an extraordinary tax bill. So that was actually part of the reason why I decided to go to the UK because I thought, well, I can go over there, I can try and get a job. If I do great, that’s fine. If I don’t, I can stay over there for six months a year. And in the time that I’m not earning anything. Basically, what I save on the tax from not having a year’s income is will pay for my trip. And what’s the worst that can happen? I ended up back at home after having a tax refund, paid for trip living at my parents house looking for a new job. So I was always this thinking of what’s the downside first. So I sold the stocks. And in terms of what happened in the Australian market, we did get a bit of a pullback in 2008 2009. But our government put out an extraordinary amount of fiscal stimulus guaranteed all the deposits in our bank, which is something that if you look at what our regulators had said previously, it said that the government should never do that. But they did. They banned short selling on our market. And they also dropped interest rates to a very, very, very low level. And that really kicked off a boom in the property market. So you had this lull around 2008 and 2009. And we didn’t get anywhere near the losses that people did here also, because the structure of our lending is very different. So unlike here in the US, where you can borrow a fixed rate for 30 years, about the longest you can get a fixed rate in Australia is five and most people have variable rate loans, which means we didn’t have a lot of the crazy borrowing that people had. And also we weren’t as sophisticated on the securitization side. So banks largely retained the mortgages that they underwrote. So a lot of these dynamics that really accelerated the property decline in Australia. The other thing is our bankruptcy laws are incredibly punitive. You couldn’t have jingle mail in Australia, you wouldn’t be able to own property or run a business for seven years, I think it is. So all of these things meant that we didn’t really have the crashing property that the US experience. So turns out that, again, thinking about the downside, because the debt that I had on that unit was quite modest, the apartment was positively geared, the rent was more than covering the interest. And I ended up making a little bit on the capital in that place, as well. So again, through sheer luck, it kind of worked out.
Corey Hoffstein 13:43
And correct me if I’m wrong rental property is actually one of the largest asset classes for Australian investors, right? Yes.
Daniel Grioli 13:50
So we’ve had the world’s longest without recession stretch of any country. So from that early 1990s, period, we haven’t had a recession since technically, I think it’s running too close to 30 years. And during that time, property has benefited from the economic growth, but also deregulation in banking, and a whole lot of other changes that made it a lot easier to receive credit. So we now have one of the more expensive property markets in the world.
Corey Hoffstein 14:23
So let’s pick up the thread you are moving to London, and looking for a career in banking in London. And I believe you landed at Deutsche Bank, correct me if I’m wrong, they’re in actually doing some equity derivatives and fun to fun type analysis. Right into the 2008 crisis. That’s right. So maybe you can tell me a little bit about what that experience was like. What I’d love for you to highlight if you can is we have such an American perspective of what the crisis was like you lived it from the European perspective. I would love to get your thoughts of how things unfolded in Europe.
Daniel Grioli 14:55
Sure. So I was lucky a lot of those people at the bank where I was where Working that encouraged me to go overseas, they put me in touch with their network. And another person who had worked at the bank where I’d worked in Australia, he helped me get an interview at Deutsche Bank, he was a friend of a friend. So I was in a team in what was called middle office. So it wasn’t back office, I wasn’t front office, but I had a lot to do with front office. And our role was the valuation of structured equity derivatives and hedge fund of funds. And these products were created largely for Continental European pension funds and institutions. So when you think of the US it’s such a large investment market, but most of what it does is for the US, whereas London is really the hub for the rest of the world. In front office, you had the the Spain desk, the Germany desk, the grease desk, the Italy desk, the France desk, the Benelux which was Holland, and Belgium and in the Nordics, they all had their own desks of salespeople. And they were all putting together the structures for pension insurance funds in their relative markets. And because I speak Italian, I was covering a lot of the products that went out to the Italian market, and was an interesting dynamic in Italy, a lot of most retirees in Italy had a government defined benefit pension fund. And they were encouraged by the government because the government was having difficulties meeting these liabilities to take their balance and put it into a private fund. So a lot of that had happened before the GFC. So we had a lot of insurance companies creating products to meet the demand of people leaving the government system. And Deutsche Bank was creating these products and selling it to the insurance companies. So one of the things we sold a lot of were these capital protected notes, also known as C PPIs, which, if you look it up is also one of the things that’s credited with the 89 crash, as it was a structure that was quite popular around then. And the way these notes worked is you’d purchase some call options on an equity index. And that would be the at risk portion, and then you’d back it with a zero coupon bond. And so the idea was if the zero coupon bond had a face value of $100, say, and it matures in five years time and say with a discount rate, keep the numbers simple. You’re buying it today for $70. And obviously, it accrues up to 100. And then you get your interest when you get the face value back. So if you have $100, you put $70 into the zero coupon bond, you put $30 into the equity options, the worst that can happen at the end of five years is those options expire worthless, you get your $100 back. That’s the idea. And the cppi mechanism is designed that as you get more in the money on the equity side, you can reduce how much of the capital protection piece that you have. So there’s a procyclical momentum based element to it as well. So I was valuing these sorts of structures. And one of them was, there was an equity option on a European index, which was obviously falling during the financial crisis. But because everybody was trying to squeeze as much return out of the structures as possible, the capital protection piece, rather than being a government bond, or something safe, like that was actually an Icelandic bank. And it started losing money far quicker than the equity option, which was supposed to be the riskier pot. And what I discovered once this was happening was that the spread assumptions for that European medium term note, were hard coded into an excel sheet. And I had to go to the trader every day to ask for the new marks, and the spreads were literally blowing out faster than I could update them in the model. And it was scary. And not only was it scary for us, but the way the laws in Italy worked, pensioners had to be advised they had to be told if their investments lost more than a certain amount. So you had the client, the insurance company freaking out about this huge public relations and client education issue that they had, trying to let people know that these things that they bought, which they didn’t really understand that they thought was safe, really weren’t. So it was interesting to see that happen. At Deutsche Bank. The other interesting thing to see was just the behavior of people on the various desks. So a lot of these structured notes, they might be purchased for a face value of $100. But about six to $8 was commissioned that went straight to the bank. So you buy it for $100. And on day one, you’ve lost six to 8%. And a couple of interesting things happened with that. So the first interesting thing was that the trader who actually created and price the structure didn’t always tell the salesperson how much commission he’d built in So it’s interesting seeing the fights when the salesperson figured out that the trader hadn’t been totally forthcoming on what he was making. He gets some interesting fights in the trading room floor, from the salesperson that felt he was being gypped. So that was interesting to watch how they were not only predatory sometimes, and I don’t put Deutsche Bank just in his pocket, but all investment banks, not only predatory with their clients, but also with each other trader versus salesperson. The other interesting thing about that, too, is you obviously didn’t want to tell the client that they just lost six to 8% on day one. So they would amortize the Commission’s over time. So they would justify that amortization as well the clients in this for so many months, so that gradually adjust the valuations down and hope that the market that the product was in would go up and the client would notice that they’ve paid this hefty commission upfront. So it was very interesting to spend time there to see how a lot of structured products, there’s often a lot of things built inside them that are great for the issuer, and not so great for the client. I think it was good to see how the sausage is made to get an awareness of that. The other behavioral things that were quite funny was just to watch the different responses of traders. So there was one guy I know that he was very, very cavalier when it comes to spending money. He bought a Ferrari and he renovated this big house in West London for his wife and, and he sat right next to another person who was convinced we were near the top. And he was just trying to save every little bit that he earned. So he drove this crappy little Persia around London and lived somewhere in Pimlico and a small unit, he was just trying to stash away cash. And these guys literally sat next to each other. It was interesting watching the guy stashing cash because sometimes I’d come in and he’d be talking to a client in Italy trying to sell a product. And he’d literally have an Excel sheet open on one of his windows, where he was calculating his commission on the sale while he’s still talking to the person. So it’s quite interesting to see all of these behaviors upfront because you hear stories. The other really big story that had a huge impression on me was seeing a mass sacking unfold. So being in the office and seeing a story come up on Bloomberg, that 700 People at Deutsche Bank were losing their jobs. And a lot of those people were people that I was emailing on a daily basis about various things that I was pricing. And then they just stopped replying to their emails because they didn’t know they were going to lose their job they found out on Bloomberg, security, comms hands them a cardboard box, they take their things, they’re out because they’re too much of a security risk to leave on the floor. And the mood in that place after that happened was like everybody’s grandmother just died. Nobody was talking. It was just, you got to imagine this is an enormous trading floor. So the way Deutsche Bank work, there was three levels in the London wall building that probably each level one was stocks, one was bonds and commodities. And the other one I think was currency or currency and commodities, bonds and stocks, I think were the three levels and probably 1000 2000 people on each level. And just the silence. Normally you couldn’t hear yourself, but just the silence for the three or four days afterwards was it made quite an impression on me,
Corey Hoffstein 23:19
I can only imagine. I want to return briefly to get your perspective on structured products, because I know you spent so long on that desk and you did see so many shenanigans so to speak. But I’d love your perspective on whether structured products can play an important role in an investor’s portfolio, barring funny things happening because they’re so opaque barring the commission aspect. Are there positives that structured products can play in either an individual or an institution’s portfolio?
Daniel Grioli 23:50
I definitely believe that they can. So I’m actually a big fan of structuring and I use it myself. And that often means using leverage. And I think that using leverage prudently can allow you to manage risk better, if you’re using it for that reason, rather than trying to gear and and swing harder at something. So I think structuring and structuring is actually under utilized. I think many people see it as risky or they don’t understand it. So it definitely has a role. In a case of my own portfolio. I can give you an example of how I use it. So I use a a levered ETF levered equity market ETF that in Australia, they have levered ETFs that don’t rebalance daily. They have a rebalancing range, I think it’s plus or minus 15%. Around the market exposure target the debt to equity ratio, and instead of allocating the money that I would allocate to equities, if I was 100%, invested 20 to 30% of my money in that levered ETF, which is 2.3 times levered when it’s at its target, kind of gets you close and then I put a bunch of money in preferred stocks because they’re actually less risky. In terms of volatility, the main risk with them is that they’re largely issued by banks. And so if you get a credit advance, then you have risk, but they’re paying a higher yield. And in Australia, the yield is franked, which means the tax is paid at the company level, so you’re not getting double taxation like you are in the US. And then a big wad of cash. And if you actually look at that, as an overall portfolio, if you hold that for a few years, you’re getting equity like returns, the small piece that you’ve got in levered equities is covering your upside the preferred, they’re giving you a great running yield, which is important in this portfolio, because part of the assets of my father’s Pension Fund, and the cash gives you optionality and lets you sleep at night, if the levered portion goes down, you’ve got cash to rebalance back in. So very simple structure. And it achieves portfolio outcomes with lower risk. That would be hard to get from active management. But you’ve got to be comfortable using leverage, you’ve got to be comfortable structuring and most people aren’t
Corey Hoffstein 26:01
what goes back to the capital efficiency argument, right. And I know that was something that it remains popular discussion among our peers on Twitter, I know Wisdom Tree just launched their 9060 ETF last year, I think, actually took home ETF of the Year from etf.com recently for that very concept, but I do think, post 2008, there was so much leverage aversion, so much risk aversion, I know we’ll talk a bit about that in a minute as to, again, what you saw from both individuals and institutions in their change of behavior. But it does strike me that there is an under utilization of leverage among most investors portfolios, I would definitely agree with that. So to pick up on the story, you’re at Deutsche Bank, you’re living through the 2008 crisis, you were there for, I believe, a planned about 12 months stay there about and then we’re evaluating your options.
Daniel Grioli 26:50
That’s right. So I was thinking about, on the one hand, spending some time in Ireland, because Ireland was the custodian and clearing house for the hedge fund world, they spoke English, they’re in the same time zone as the UK, but the tax rate was very low. And so Ireland actually had a skilled migrant shortage for people with a finance background. So you had preferential visa treatment if you went there, and I’d been there on holiday, and it’s a great place. So I was actually thinking about that. And at the same time, I was also starting to reconnect with somebody that I knew back home, who later became my girlfriend, and then my wife, and now the mother of my two children. So I had to choose between whether I head back to Australia or Ireland and again, lucky because I missed probably an even worse end to the crash, being in Ireland, and ended up with the love of my life. So lucky decision to come back to Australia, and it’s worked out really well. So that’s how I got back to Australia.
Corey Hoffstein 27:50
In Australia, you picked up your career working at superannuation fund, right? Yes. And can you talk a little bit about what you were doing for the fund,
Daniel Grioli 27:59
again, not a happy accident. It’s really a series of happy accidents. And I think if you ask most successful investors about the role of luck in their careers, if they’re honest, it’s always played a big part. So I had this choice to make my girlfriend at the time now my wife, she was living near Newcastle, and Newcastle is a smaller regional town. It’s on the coasts, great weather, great beaches, great surf, but not really much of a finance industry. And so I was thinking that I’d be moving to Sydney, but it’s about three hours away, and it would have made it hard to spend time together. So I thought, I’m just going to see if there’s anything in Newcastle, maybe there is who knows, I’m just going to look. And as it turns out, Newcastle is also the major coal port for Australia. And one of the major ports. And there was at the time a pension fund base there that looked after the coal mining union. And they were looking for an investment analyst to be based in Newcastle, but also spend a day or two a week in Sydney. So it turned out that I was able to get that job and start at a low level position as an Investment Analyst and work my way up through there. So that was how I got involved with superannuation or pension funds in Australia, which is a big part of what we do. So Australia has got the world’s fourth largest pension system. I think at the moment, it’s just under $3 trillion Australian.
Corey Hoffstein 29:26
So I know in reading some of the posts on your blog, you had shifted over to the pension fund during the 2008 era. And there were some really interesting things that you saw, which I alluded to earlier, on the behavioral side of both what individuals did, as well as sort of the fashion of the industry, the investment industry because I know you were also doing manager evaluation. Can you speak to that a little bit about what you saw how things changed while you were there during the crisis?
Daniel Grioli 29:55
Sure. So I left London, April 2008. By the time I got back to Australia spent some time with my family with my girlfriend moved up to Newcastle, it was August, before I started at the pension fund, so just before Lehman Brothers and the real acceleration into the peak, and so the fund at its peak was worth about $5 billion Australian was already slightly down, I think it was about four and a half billion by the time I joined. At the worst of the financial crisis, it got to about $4 billion, I think so what’s that 25% of the value of the fund was written off during that period. And the interesting thing was, we had a cash option. So the there was sort of four balanced options, a couple of single asset class options, and one of which was cash. And this cash option was invested in bills, but also in short duration credit instruments, which people thought were very safe. In Australia, they used to call us cash enhanced. And during the week of Lehman Brothers, that cash portfolio actually had negative returns. It had that on two occasions, and two weekly sets of returns. And our members just went berserk. So our phones were running hot, people ringing up saying I thought this was cash, how can cash lose money, the whole reason I put it in cash was not to lose money. So we had this big drama. And we were faced with this choice, because a lot of the credit instruments that were in his cash portfolio, and also more broadly, in our fixed income portfolio, they were selling off in sympathy with the US because everybody thought the world was ending, but they were nowhere near as risky as US assets. So the loan to lending valuations were much lower, the quality of the security was better, they were seasoned. So they really shouldn’t have been hit the way that they were. So we had this issue, we had this mismatch between what our cash fund was delivering, because we recognize that we’d made a mistake and what was on the tin wasn’t necessarily what was in the portfolio. And we needed to fix that. But at the same time, we knew that if we’re selling out of these assets, we’d be crystallizing losses for our members on what were fundamentally sound security. So what we actually did is we created an internal swap, where we swapped the credit returns that were in our cash portfolio, that credit exposure for the bill return the three month bill return, coming from our fixed income portfolio, so we created this swap, that was actually my boss’s idea. And because I had valued similar things back at Deutsche Bank, I was able to put together, the model knew the necessary bits and pieces to get this to work. So we ran this swap for just under 12 months. And it worked fantastically well because it allowed our members to stay invested in these instruments while earning the cash return. And it allowed us to ride out the storm. The other interesting thing that happened while this swap was in operation was that the cash portfolio blew out. So when we started, there was probably around $300 million in cash. And it got to close to a billion. And that was largely through very heavy redemptions from the multi asset products that the growth your multi asset products and some of the growth asset classes. So at its peak was just under a billion dollars in cash. And a couple of interesting things that happened was the biggest single monthly move to cash was March 2009. That was the biggest single cash move. And I actually had a manager at the time, who used to call me up every couple of weeks to ask what was happening with our cash. This was an equity manager. And the reason he was doing that he was calling all of his clients at the time and asking them the same thing, because he was using the cash moves as a contrarian signal. So when he saw that, members were cashing out, that’s when he was having the courage to go in and buy stocks. And it’s very interesting, because this manager in terms of his mandate, he had a tracking error limit on his portfolio. And tracking error, for those that don’t know, is a statistical measure of the disparity in returns between the portfolio and the benchmark. And he was currently exceeding that by a long way. And Sue wrote us a letter saying, look, if I have to abide by the terms of my mandate, I’m going to have to sell a whole lot of things that actually should be buying more of, are you happy to give me a waiver on my tracking error limit? We did. And it was a good call because a lot of the things that he was able to buy there came roaring back in a long way added a lot of value for our members. But the other interesting part to this cash story was when people saw the recovery, they moved out of cash, but never back to where they came from. So if they were in the balance fund, and they went to cash, they moved into moderate or if they’re in high growth, and they went to cash, they moved into balanced, but they never went back up in their risk profile to where they came from it, it was almost like it had a, I’d moved on to another fund. So I don’t know what the longer term effect was. But it seemed like it really shifted their risk preferences.
Corey Hoffstein 35:19
I want to switch gears a bit, because I know both at Deutsche and the pension fund experiences you had manager evaluation was a critical part of your role. You wrote a great post on your blog about how you view or how your view of manager evaluation has evolved over time, maybe we can begin first talking about, you really evaluated a large breadth of different managers, everything from quantitative to discretionary, from your more vanilla to your highly esoteric listed and listed listed unlisted. And you would have ultimately, every manager coming in trying to convince you coming in with high conviction trying to convince you that their view of the world was correct. How do you reconcile all those views when you’re doing this analysis,
Daniel Grioli 36:08
it’s hard, because we literally have a situation where I’d see a value manager in the morning, and they would be calm by these tech stocks, the valuations are ridiculous, this is the 2000 Bubble all over again. And then that afternoon, I’d have a growth manager saying, Well, anybody who’s buying those industrial cyclicals is an idiot, because they’re gonna get disrupted, and they’re going extinct. And I’d walk away and talk to my colleagues afterwards. And similar thing between asset classes, so you get the bond managers that always be pessimistic, and the world’s ending, got to worry about inflation, or slowing GDP growth, or whatever it was. And then the equity managers were always telling you what earnings growth is going to be eight to 10%, next year, and it’s fine. So you had to reconcile those views. And I think that’s an interesting part of my background that, in some ways, has made life difficult and gets back to this idea of being a fox. Because it’s hard sometimes to relate to any one group, or any one way of doing things for me, because I’ve sort of had my finger in a lot of pies, I had to come up with a way to sort of make sense of this. And for me, the way that I did that was really to realize that when it comes to investing, I think it was Professor van Tharp, the Trading Coach that said this, but I think it’s true. We really trade our beliefs. The end of the day, there’s lots of ways to make money. And they all work because they’re exploiting either different risks or different behavioral patterns that are fairly stable. And so I come to realize that whichever one you pick is not so much as important as whether it fits your beliefs, because that will determine whether or not you’re able to stick with it. So something like value versus growth, for example, the way I learned to think about that was that they’re both forms of extrapolation. So in value, you’re over extrapolating the bad news. And then it’s the recovery effect that gives you the value premium. And with growth, you’re under extrapolating a business that can potentially change the world or change its market. But in both cases, the common factor is people have extrapolated the wrong thing. And I found thinking about it that way helped me make sense of it, because they’re no longer in opposition with each other. They’re no longer two camps where each camp thinks the other camp is being stupid for the way they’re investing. So that was one way that helped me the other way was to realize that things like momentum and value exist on a continuum. And the way I think about that is, I use the weather as an example. So if you had to forecast what tomorrow’s weather is, today, your best guide is today’s weather. But if you had to think about the weather three or four months from now, you’d probably want to think about some element of reversion to the next season. And that’s kind of like value. So. But it’s not that these things are contradictory. They’re just on this continuum between short and long term weather. And I sort of think about a lot of things that way. And I think you’ve got to look almost for the deeper commonalities to be able to integrate this stuff together. And when you do, I think that’s incredibly liberating, because suddenly, instead of having this dogmatic approach where this is the way I will invest, you now have a tool set of lots of different things that you can use, and you can use different tools for different jobs.
Corey Hoffstein 39:35
So your weather analogy really would have resonated with me when I lived in Boston. When we have four very distinct seasons in LA here, I don’t know if that works for me anymore. So as a quant with a massive quant bias, I’m always constantly curious about where the limits of quant are. I think from sort of an arrogant marketing perspective, we tend to say that quants are able to avoid a lot of the A behavioral biases that discretionary managers can bring to the table, and therefore quant is superior. I think if you get us behind closed doors, we’re more willing to sort of start to discuss the limitations of quant as you have viewed the world again across such a wide breadth of different types of managers. Where do you think quant works? Well, and maybe more importantly, where do you think quant really doesn’t work?
Daniel Grioli 40:22
Well, there’s a few ways to answer this question. The first point I’d make is again, this idea of we trade our beliefs, I think there is definitely a personality trait dimension to who uses quant and why they use quant. So I think there’s certain people wired a certain way, and the quant approach naturally appeals to them. Whereas other people just I was having this conversation with a client, for example, who used to work building guidance systems for missiles. And we’re talking about the fact that planes today largely fly themselves. And but if the passengers knew that most passengers wouldn’t get on the plane, and he was saying, Well, I’d happily get on the plane because I used to work on the guidance systems. I know they’re better than pilots. And I said, Well, that’s you. But the majority of people want to know, even if the computer is doing most of the work, they want to know there’s a guy in the cabin. So I think a large part of it comes down to investor preference, the way we’re wired. And what attracts us. Related to that I find a common pattern with a lot of quantitative investors is that they didn’t always start out as quants. And many of them had an experience where they got badly burnt doing something another way. And they’ve almost had this get religion moment where they’ve said, That’s it. I’m not going to rely on myself. I can’t trust myself, my emotions, I’m never letting that happen again. And while I think that response is understandable, I think it sometimes misses the role that emotion can play. So there’s a lot of research that various professors have done. Damasio is one there’s also some researchers at the University of Melbourne. And there’s a lot of evidence to show that emotion is a big part of how we perceive risk. And if you take emotions out, totally, it actually affects the way that we perceive risk. So some of the studies that Damasio did in one study, he took a whole lot of brain injury patients to the casino. And he found that they won hugely, they had huge wins, because they had this impaired perception of risk. So they just kept, they just kept betting big, and they won big. In another sample, he was using, again, some brain injury patients. And he found that he was getting them to play a game with a loaded deck. And what he found was that within a few hands of the game, they could tell that the deck was loaded, but they couldn’t stop betting on it. They had this compulsion to bet even though they were aware they were conscious of the fact that the game was rigged. And they literally kept betting until they lost everything. So he learned from these and other studies that emotion is actually part of perception of risk. So when I look at some of these quants that have swung away from anything that might resemble being discretionary, I kind of hope that they would have a little bit more of a balanced view of the role of emotion and also be honest in terms of when you pick a data set to work with. That’s a choice and how you structure a model. There’s a lot of choice in that. And as one very experienced quant told me once he said, Well, fundamental managers fall in love with their stocks, and quants fell in love with their models. I think there’s some truth to that, where I think the quants have a definite edge over the fundamentals. And again, it comes back to this having seen people on both sides is that the quants are consistent in the way they weighed things. And that is hugely important because people can be very biased. What they’re interested in today is totally different to what they’re placing importance on tomorrow. And a quant process ensures that you’re making apples with apples comparisons and I think that’s a big strength of quant. The other strength of quant is that it can cover a breadth of things that a person might not be able to. That’s also a strength of corn. So I think it’s important to recognize both there’s a friend of mine who soon will be a guest on my podcast named Simon Russell who’s done a lot of work behaviorally coaching fund managers and pension funds and he’s recently written a book called cyborg where he talks about this, these two ways of using quant there’s the Terminator model, as in Arnie pure quant through and through and then there’s the cyborg model, which he calls Ironman, where you’ve got Tony Stark, assisted by Jarvis and some cool gadgets. And I think more and more active managers are probably going to turn into that sort of Iron Man like character. They’re going to use quant tools and they’re going to combine it with human insights. And I think that’s a good mix. I think in terms of me and the way I think that kind of mix suits me personally, in terms of the idea of where quants limited, I think back to what quant needs, so quant needs data. So you’ve got to have the data. The other thing is you’re generally trying to exploit a small edge over lots of repetitions. So you need breadth. And then the other related point to that is you also need, or how you ask research questions can greatly affect results. So I’ll give you examples on each. So on the data, I much more confident using a quant value approach or quant momentum approach rather than, say a growth investing approach. And the reason for that if you think of value, it’s price, which is a current data point versus some historical fundamental, whether it’s earnings or book value, which being historical, you also have the data. So you’re looking for a gap, and you’ve got the data to find that gap. Same with momentum, it’s largely price base. Same with size, it’s largely price and market cap based. Things like growth reside in the future, and you don’t have the data yet. So whether it’s a growth equity strategy, or venture capital or things like that, I think it’s harder for quant to work because it’s more future oriented breath. Same thing. So in asset allocation, I used to see this particularly that a lot of quant research tells you it’s very hard to try and time between markets and shift your allocations. And a lot of that has to do with the way quants think because again, they’re looking for small edges that are repeatable across time. And you don’t get that in asset allocation. Because you’ve got maybe depending on how you classify it, somewhere between maybe five and 12 main asset classes, and the big kind of dislocations or risks or opportunities that you’re trying to capture or avoid. They don’t happen often. But the consequences of them if you’re on the right or the wrong side are huge. So it’s kind of the opposite of everything that a quant would look for. So I can understand when you see these research pieces that come out and say, trying to time it doesn’t. Because thinking of it that way, it doesn’t work. But that doesn’t mean that you can’t do it thinking in other ways. That’s one thing I learned. So breath is important. And in terms of the final thing was how you approach, research and data. So quants are limited by the data. And so the way they ask research questions, and not necessarily the ideal way to ask the question. So I’ll give you an example. So the original foundational work on value as an investment factor use price to book. Now why use price to book? Well, why not use price to cash flow? Well, you couldn’t because a cash flow statement, I think only started in the 80s. So you couldn’t go back long term and measure a company’s price to cash flow, you had no data. In many cases, I think earnings statements didn’t go back as far as balance sheets did either. And a lot of companies don’t have earnings. So price to book, I would argue the choice to use that was largely determined by the data, and not necessarily whether it was the best way to measure what you wanted to measure. And I see that pattern a lot in quant research where you can see the researcher has made the choices they have because that’s the data they’ve got to work with. But are they really answering a question that’s relevant to what somebody would do in the real world. The classic example of that, as well as William sharps paper, the likely returns from market timing. So he tests a scenario where it’s January 1, and you decide whether you want to be 100% in stocks or 100% in bonds. And I don’t know anybody that allocates that way. I wouldn’t, I would never think of a binary bet. And I wouldn’t hold it for a year without revisiting it. So why did Sharpe run his analysis that way? Well, my guess is it had to do with the limitations of data and computing power at the time he did the study. So he did this back in the 70s. He needed enough data to have a statistically significant sample yet annual data. But by the same token, he didn’t want to be doing too many calculations because computing power isn’t what it is today. So he’s tested it in a certain way. And he’s found that you need an 80% accuracy rate, but I don’t know how relevant that conclusion is to what most people do because most people wouldn’t decide on January 1, whether they want to be 100%, long 100% cash, so that’s something I’ve noticed with a lot of quant researchers, you need to think about well is the question that the quant is answering in his paper, something that I would actually do in my portfolio.
Corey Hoffstein 49:51
I see that a lot in the use of monthly or quarterly data where we have sort of arbitrarily decided to measure portfolio returns in factor returns on a calendar basis and re formation on a calendar basis. And people who read my research will know that’s a bit of an obsession of mine. And you can get some very, very different results if you just offset when you choose to reform your portfolio. And I think to your point, the earliest research in this space, had to make some computationally driven and data driven decisions that have perpetuated in the literature, but don’t need to perpetuate anymore. And I think there’s better ways for that research to be done to your point. So back to your experience in selecting managers, I think you and I are similar minded that we tend to focus on the risks rather than the positives. And I tend to learn a lot more from bad decisions rather than good ones. Any real horror stories that you can recall from your selection days, I can
Daniel Grioli 50:53
think of some horror stories, probably the worst manager I ever encountered was a micro cap manager, that was managing too much money, given the size of its opportunity set. And so they literally were in a situation where about 20% of each client’s portfolio was different to other clients portfolios because they couldn’t get enough capital into the same set of ideas. And that led to all sorts of problems led to huge disparities in performance depending on when you signed on as a client. And what was happening in that other 20%. And the other thing I learned through that process was that the reason why this particular fund got so big and outgrew their investment niche was that they were liked, and heavily backed by a particular asset consultant who had so many clients in them in what is a fairly illiquid and difficult to trade part of the market, that they were kind of stuck with them. So they couldn’t get people out of this manager, even if they wanted to without smashing the values of everybody else’s portfolio. So I learned a lot from that about the mistakes that a fund manager can make and what to look out for in terms of capacity and size, but also the business model of ASIC consultants, and how that works. And sometimes how their incentives are different to what you’d expect them to be.
Corey Hoffstein 52:19
So picking up on that threat of red flags, I know, You’ve eventually evolved this thinking about manager selection into a framework that you call the via negativa. And a recent blog post, can you explain what that means, and maybe how it’s a little different than the way people traditionally view manager selection.
Daniel Grioli 52:38
So via negative I have to take the inspiration from that came from Nassim Taleb, because he’s used the idea in some of his writings. It’s an idea that comes from religion. And the idea is, is very hard to explain what God is, but in some ways, it’s easier to explain what God isn’t. So you’re kind of explaining God by subtraction, if you want to call it that way. And so the idea with fund managers, it’s very hard to find a good one. But it’s easier and more robust if you try to eliminate bad ones. And then once you’ve cut a sample down, try and get the cheapest of what’s left basically, is how I would describe it. And I got to the idea by trying to do manage a selection, the conventional way. So everybody looks for the the four P’s, commonly known people philosophy, process performance, or some version of those. And I was very methodical in the way I did it. And I approached it with a lot of enthusiasm. And I’d put together a checklist, which I’d researched very heavily by looking at what other consultants and groups had done. I also learned a lot from working with people more experienced than me and the kinds of questions they asked in meetings. We had a lot of quantitative analysis, our consultants were able to run holdings based analysis on all the managers look at FACTOR exposures, measure all the quantitative stuff. So I used a lot of that very heavily. And I realized over time that it’s very tough to pick managers that way, it’s very tough to pick managers full stop. And so many managers, I think the other thing I realized, seeing manager after manager after manager I’ve probably met hundreds over my career from all over the world is I was sitting on the other side of the table from them. So I had this basis of comparison, lots of them were coming in. They didn’t have that. And many times they didn’t really know what was happening more broadly. It’s kind of ironic, it’s a huge irony that, particularly with equity managers, discretionary managers whose whole business is analyzing businesses and companies. They actually know very little about their own industry and what’s happening outside of their firm, which I always found incredibly ironic, I would have just presumed that they would use the same skills that they use assessing other companies on their own business and their own industry but seems like they don’t. And so you’re all these managers would come in. And after a while you realize most of them are really very similar. If I got paid $1, for every inverted pyramid chart, I saw it, you start with a universe of 10,000 stocks, and you start filtering for liquidity, and then you come up with some kind of screen. And then you let the analysts loose on the screen, and they come up with the final portfolio, and then the pm constructs it. I’ve seen that over and over and over again, and one of the learnings that come from that as well was that this is a red flag that will interest you, is that screen. So so many managers relied on a screen to cut the universe down from say, 10,000 to 300. So arguably, the screen is doing most of the work. And that screen is really a simple factor strategy, in most cases, or smart beta strategy. So the question I would ask managers will, have you ever looked at that screen as a portfolio versus yours, and the vast majority hadn’t. And in my mind, that was an instant via negativa. Because if you don’t have the intellectual curiosity to understand how your process is working, and you’re not out there looking at that, and comparing that and looking for ways to create feedback, so that you can learn, as you look at any performance athlete, they’re studying the tapes of their game, to see what they can coach for and what they can train for creating feedback. If you’re not doing that, as a manager, there’s really nothing left to discuss. And I was consistently shocked, I did that for about a year, I asked every meeting that I was in, I asked the manager who had a process like that, what’s the performance of your portfolio versus that initial screen, and probably 90% didn’t know,
Corey Hoffstein 56:42
I’m always a little shocked, because I think that’s a very natural way to think about portfolio construction, that triangle. And even on a more quantitative side, there’s often very distinct portfolio steps where you start with your benchmark, and you start making some transformations. And I have always taken the approach of when doing portfolio analysis, I don’t use any sort of factor analysis or anything like that. I literally construct the portfolio for each of those steps. And I look at the marginal value add of each step. But you can even take it a step further and determine how correlated each step of the process is. And I’ve always been really surprised more people don’t do that. And, and more people don’t ask for that sort of evaluation to say, well, this step of the process that you’re saying is adding so much value, have you isolated its actual value add compared to all the aggregate parts of the process before it? And by the way, what did it do in q4 2018? Or something like that?
Daniel Grioli 57:36
The other thing that I used to use that for is I’d throw it back on the manager. And I’d say, well, in terms of your fee structure, are you willing to move to a performance fee arrangement where I pay you a very low base fee equivalent to Smart beta fees on that factor portion? And I pay your performance for what you do above that? If your process is 70% of the work is being done by that initial factor? Well, I don’t think I should pay more than 10 or 20 basis points for that, because that’s what I can get it for from Smart beta. So how about I pay you 10 or 20 basis points and then performance over what you do above that?
Corey Hoffstein 58:08
I’m biased because I’m a quant. So I need to state that I’ve never met a discretionary manager that says I invest willy nilly, right? They always have a disciplined repeatable process, which to a quant makes your ears perked up and say, Alright, that sounds like a factor to me. So why don’t you just start there, and then we’ll measure your performance. Beyond that right your value past it,
Daniel Grioli 58:27
I’ll tell you a funny story about factors and fundamentals. So we had invested once in a quantitative strategy, and it was ostensibly Quality Strategy was using Petrovsky F score to screen out bad companies. And then based on what was left, it was using value to prioritize the names and the value factors that it used differed across the market sectors. And so what the quantitative manager done is he looked at at the measures most commonly used by sell side analysts for each industry sector and he’d used those in his current model, the measures used for the mining sector were different than the banking sector, etc. Now, what ended up happening was, if you know how the Petrowski model works, I’m sure you do. There’s nine indicators in the school stock can have a score between zero and nine. But an eight isn’t necessarily better than a seven or worse than a nine. The literature was sort of everything that was six and above was good and everything that six and below was not so good. So the measure was not necessarily monotonic in the way it worked. And this manager was applying it to Australian equities and because of the structure of the market, the concentration of the market, you’re generally had larger higher quality companies. So in terms of the top 100 stocks, which is at 90% of the market cap. When we ran The analysis afterwards, we didn’t realize this at first, all, but two of them had a score above six. So for the Petrovski, wasn’t actually knocking anything out. So you’re just left with the value. And then what happened with this value was the manager had realized after a while that he thought he wasn’t back testing or overfitting or having problems with his data by using the sell side analysts preferred measures, he thought, this is I’m getting a genuine sort of bottoms up replicating how a fundamental investor would think, model, what he didn’t realize was that the way those sell side analysts chose those measures, because they had actually chosen those measures by regressing what had worked best. a sec, that’s so you know, the reason why the banking analysts use price to book was because that had the best quant score for banks. So implicitly in thinking that he was trying to avoid data mining, he’d actually embedded it into his valuation measure. So that was interesting. And then talking to the manager at the time, then they wanted to revise the model, you know, they’d come out with a better version of Petrovski. That was sort of a Petrovski plus, and it had 20 things in it. And so it was interesting watching that, because that’s probably a great example of quant gone wrong, and what we learned from that. So yeah, that was an interesting story.
Corey Hoffstein 1:01:24
Talk to me about combining managers. So we’ve spent a little time now talking about selecting managers, things that can go right things that can go wrong. But manager selection doesn’t happen in a vacuum. And I know you were responsible for looking across this breadth of different managers, and then allocating to them in a portfolio context. How does that change your thinking about manager selection?
Daniel Grioli 1:01:46
It changes that a lot. And it was interesting that the fund managers often didn’t appreciate my perspective on this. And when I say mine, I don’t mean me individually, but somebody in my shoes as an allocator. And just like a portfolio manager, they’re not necessarily trying to pick a portfolio of stocks that trying to create the best portfolio overall. And I was trying to do exactly the same thing with the managers. And so managers would come in and they’d point to their track record their process their people, and they’d say, you know, we’re fantastic, of course, you should hire us. And the point that I’d always make is, well, I’m not necessarily trying to find the best manager, just like you’re not necessarily trying to find the best stock, you’re trying to find the best group of stocks that create a great portfolio. And I’m trying to do the same thing. With several managers, in most cases, your equity portfolio in Australia, depending on the size of the superannuation or pension fund, would have somewhere between maybe five and 15 managers depending on the side. And you want those managers to play well together. And it was funny how the fund managers couldn’t appreciate that, again, sort of so caught up in what they’re doing and their strategy, which in some ways is understandable, because it’s their business, they should be proud of it. So those interaction effects between managers were huge. So often, you’d get a doubling up of bits, but you’d also get a redundancy where value manager is shorter stock, your growth manager is long, and so the bet gets cancelled. And that was particularly a problem in Australian equities, because it’s heavily concentrated by sector and stock. So the top 10 stocks are around 65% of the market cap. So depending on whether one was long or short. And the way I describe it is I used to describe it as a kindergarten problem. So when you’re in kindergarten, and they give you paint, you put some yellow up on the canvas, and then you put some blue on the yellow, and you get green. And you think this is fantastic. I can mix colors together and make new colors and you put red and blue together and you get purple. Whenever you put that third or fourth color, it always turned to brown, always mud. And it’s kind of the same in a multi asset portfolio that you can get two managers doing very different things, very different process, very different opportunity set philosophy. Once you start adding the third and the fourth, it’s amazing how quickly it just gets diversified away. And it’s very hard to have a portfolio once you start adding too many managers where you can be confident that you’re going to get any alpha net of face.
Corey Hoffstein 1:04:33
So I want to fast forward a bit to what you’re doing today. You are now running a business where you are providing high net worth and institutions with wealth management and advisory services, very much informed by your experiences at the pensions. Can you tell me a little bit about how your experience of the pensions has informed your thinking around advisory service versus towards high net worth individuals and how you think about building portfolios for them, that’s maybe a little different from the traditional advice.
Daniel Grioli 1:05:08
Sure. So I think the biggest difference is that the way you work with individuals or portfolios attached to individuals is very different. So I used to describe working in a pension funders working backwards. So you would, you’d come up with this investment opportunity, you do the research, you’d be convinced, but that would only be the beginning of your work, because then you’d have to convince the rest of your team and then you’d have to get the opinion of your asset consultant, because you’d know that as soon as you went to your investment committee, the first question they would ask is, what does the ASIC consultant think and if you’ve worked with ASIC consultants, you realize after a while that they’re generally overworked and under resourced, and quite reactive in the way that they approach research. And they generally tend to research things that are scalable for them that they can put 1020 30 clients into. So if you’ve got this really great niche, capacity constrained idea, they will kind of say, Look, we don’t know, we don’t look at it. So you had to go through the process of working with them, and then you pitch it to your board. And then they may have questions, because they’d want to know if it’s been done before and who’s done it and how you do it. So once you go through that process, you get approved, then you’d have to do operational due diligence and tax due diligence and legal, it could literally be 12, or more months before you’d invest. And if it was a time sensitive or pricing sensitive investment, it may not be as attractive. So there was always a political process as well as an investment process. And to some extent, the two processes are in tension with each other. Because if you think about all the really good active investors, they’re trying to figure out what consensus is, and looking for places to take consensus on, they’re looking for the spots, the opportunities where consensus is wrong, and they’re willing to get to go against the crowd on those things. If you think back to a pension fund, or any large institution, most people they’re trying to build consensus, that’s how you make decisions. So group decision making. And so the people I think that are good at one are good at the other are quite different. Not many people have both skill sets. And I kind of realized over time that I was less of the consensus builder and more of the naturally wired towards trying to figure out where the market might be wrong and where there might be an opportunity. So that was one of the reasons for the shift. And I found that in working with individuals, the conversations, I have a very different and they suit me a lot better because their objectives and how they think about risk is very different. And it’s I think it’s given me the freedom to do things that I think are interesting, and also can add value and hopefully have an edge in the market where we try to beat the market, I don’t try to beat the market everywhere, I think you’ve got to have a framework in mind where you can determine where you do have an edge. And then where you don’t you either diversify and keep costs low, or don’t play. And so in terms of the strategies that I run, now, they’ve in large part been directly influenced by some of these issues that you see in institutional contexts. For example, the manager interaction effect, I used to see portfolios, where we had several managers with hundreds of stocks. And net, when you model that up quantitatively, there was probably less than 20 stocks that drove all your risk in return. So what I took away from that as if I’m hiring all these managers and paying all these active fees, and it’s only 20 stocks, or maybe there’s another way to get the 20 stocks, that’s more cheaper and more efficient. And to the extent that I’m worried about risk, I can hold passive instruments with that, that can control risk and cost. So that’s one area that I’ve been exploring, and I’ve launched a strategy in that area. The other area was this idea of how institutions think about constructing portfolios. They take an asset and liability managing approach. And that’s quite different to what most wealth managers do, because most wealth managers begin with a risk profiling approach where they sit down with the client and they go through a checklist and they ask the client to imagine how they’ll feel possibly losing 20% of their portfolio, you don’t like losing money? Well, you must be conservative or balanced. And here’s a 6040 portfolio I’ve prepared earlier. And what I realized talking to clients is they don’t actually ever understand their needs or their objectives through that process. They don’t understand how the portfolio they’ve been given will help them achieve those objectives. They don’t really understand their full personal position. And so what I’m hoping to do with my clients is asset and liability modeling at the institutional level. So Create an economic balance sheet, were using myself as an example. I have some financial assets, I have some equity in my home, I have retirement savings, few other things. I also have my human capital, I’m going to be working for at least another 30 years, probably longer. I’ve got two kids to pay for probably never going to retire. So how do I think about that in the context of how I allocate my financial assets? And also thinking about we were talking about this earlier, the riskiness of what I do, in some respects, my human capital is a giant equity call option. So how do I think about that risk and managing that risk? If for example, I again, I mentioned the two liabilities otherwise known as my children, how am I going to work out the present value of what they’re going to cost? My parents, they may leave me an inheritance, how am I going to work out the present value of that future asset. So sort of bringing all of this into create an economic balance sheet, and actually helping people understand where they are now, the risks that they face, that’s a big part of what I want to do, because I think that’s something that’s been missing from wealth management up until now, I
Corey Hoffstein 1:11:10
want to dive into your best idea of strategy. Because I know that was informed again, by your idea of manager selection. And I know the premise of that strategy is built off of 13 f analysis. And there are a lot of other 13, F sort of replicator strategies, funds models that are out there, what makes your approach different than sort of the vanilla off the shelf 13, F replicators, that you can find,
Daniel Grioli 1:11:39
I would describe my strategy as a multi manager strategy without the managers. So when I think of trying to beat the market, I think you have to have a clear edge where you try to do that. And Michael Madison wrote a great paper on this. And to summarize his idea, there’s four kinds of edges. You have an informational edge, an analytical, a behavioral and a structural. So do you have better information or more information? Can you analyze that information better? Can you minimize your mistakes or capitalize on the mistakes of others? And can you create a structure that’s simpler that reduces frictions and costs. So in terms of the first two, there’s no way I’m going to get more information or out analyze the best hedge funds and fund managers in the world. Absolutely no way that I could match their research budget, their resources, their skills. So essentially, by using 13 F data, I’m outsourcing that part of my process to the people who can do it best. And to the extent that they’re incorporating new things like they’re hiring a bunch of quants, and doing machine learning, and all this other stuff, that’s gonna get picked up implicitly, in their picks. I’m benefiting from that research spend. But what I can do is work very hard on the second two sources of edge. So I can use rules. And even though I’m not a quant, I do think in a rules based way, and I love frameworks, because I think frameworks have some very important characteristics. Frameworks allow you to make apples with apples comparisons over time, which is important for good decision making. They also allow you to explain what you do to your clients very clearly, because words relating back to the framework, they also allow you to highlight when your thinking has changed, and why it’s not you being inconsistent, because the frameworks remain the same. But it’s the information that’s coming in, that’s changed. And so you’re, you’re updating your views as the weight of evidence shifts. And the other great thing about frameworks is they make it easier to break bad news to people, because you can relate what’s happening to the framework. And so the client is always getting this message that you’re being consistent with your framework. And despite what’s happening around you, so I do think in terms of frameworks, and I think frameworks can be very important in terms of managing behavior. And so portfolio construction and risk management is a key part of what I think my value add is doing this. And structure is important. So let’s take a direct example from my current portfolio. So there’s 15 managers that I’m sourcing ideas from today. Now, potential client asked me the question is what you do just a simple tracking portfolio of manager ideas, and this is kind of like the idea like what we were talking about before of the fund managers checking whether or not they outperform the simple screen, I took that as a challenge as well, I’ve got to look at my final portfolio and and see how it compares to a simple tracking portfolio. So I looked at the 15 managers that I’m currently sampling ideas from. And I waited them proximately in line with the weight that their ideas have in my portfolio and I created a combined portfolio which had 472 stocks in it. So essentially, you got the s&p 500. So then I thought, I’m doing this on a managed account basis for clients, the trading costs of that would be enormous not to mention the amount of hair, I’d lose trying to keep track of 472 stocks, and I don’t have that much I can afford to lose, to be honest. So what if you then narrowed in on the top 50 stocks from this composite group of managers and looked at that as a portfolio, and even then, and these managers that I’m selecting are very concentrated. So one of the filters that I have is that they have to have more than 40% of their fund in their top 10 stocks. So they’re individually incredibly concentrated. And yet, when you put them together, disappears, and even if you look at the top 50 positions, once you get to the top 10, that’s only 20% of the combined portfolio. So the conviction just vanishes, disappears. Now, if you compare that portfolio with my portfolio, there are a lot of names in common. But what is different is the portfolio weights are totally different because of my portfolio construction process. There’s also the risk management that goes with that, and the performance has actually been quite different. So to me, that’s validation of the process that if you just combine these managers, and that’s assuming you could get into them, because a lot of them are actually closed to new funds. And not only that, the cheapest manager of the 15 that I track has a fee of about one and a quarter percent. And the fees for my strategy for delivering the whole thing with the portfolio construction and the risk management are a fraction of that. So it’s a much more efficient structure by removing the frictions across the managers
Corey Hoffstein 1:16:53
strikes me that you’d have to be pretty careful as to the managers that you track with a 13 F, right, because you’re only getting quarterly snapshots. So high turnover strategies might not work, event driven strategies might not work, you’re only getting long, only positions, you’re getting information at the institutional level, right? So if they run multiple funds, you’re seeing an aggregate up and you’re not getting information about potential derivatives or offsetting shorting positions. How do you think about which managers lend themselves well, to this sort of 13? F tracking and which ones don’t? And how do you think about screening certain managers to be eligible for your universe?
Daniel Grioli 1:17:35
Everything you say is 100%. Correct? So these are all the challenges. And these are the reasons why a lot of the simplistic approaches, I think, dangerous. So I’ll give you one example. A lot of approaches I’ve seen tend to look for managers that have had strong recent performance, or they use a variety of quantitative measures like information ratio, Sharpe ratio, etc. To look for managers, what ends up happening there is that you’re data sampling, and you end up with a bunch of managers that have performed strongly. And you can get a very large embedded style bias, particularly if you’re looking at hedge funds, because the vast number of hedge funds have a growth style, if you think about it. If you’re paying somebody to in 20, you don’t want them to be buying Procter and Gamble and stuff like that. You want them to be buying, you know, the tech thing that they can tell you a story is growing its earnings at 30% a year. And so hedge funds is a preponderance of growth. And I can tell you what happened to one fund that did this back in 2015. This fund was soaring, it was well above benchmark in terms of its returns, sorry, 2016, you had the correction early 2016, with a market sold off by almost 20%, the growth names that this fund was in sold off obviously, by more now sort of down 30 40%. And then not only that, so the fund went from being well into the green versus the benchmark to well into the red. And not only that, when the market took off again, in late 2016, there was a style reversion. So it was all the cyclical names, and leading up to the election and then Trump’s when they had got a second wind. So it underperformed even when the market was rallying again, and it was because of this huge style bias, name bias, name concentration that it had. So that’s one very important thing. And it’s one thing that I do quite differently. So I’m actually running for manager universes. So I have a quality universe of growth universe, a small cap and a value universe. And the final portfolio has to have opportunities from at least two of those groups. And at the moment, it’s running with three. And I think that’s important because you don’t want to follow just one cohort of managers and one style of opportunities. The other thing that I do Do this comes back to the portfolio construction and risk management is I run a barbell portfolio and I use the idea of a barbell in a lot of things that I do because I liked the idea of the risk management, it gives you having a foot in both camps, it massively reduces your reliance on having to forecast if you know you’ve got something that is kind of going to work for you. in different environments, it gets back to this idea of Tellabs of being anti fragile, he talks a lot about barbells as well. So for me, the quality managers that I track represent the risk reduction part of that barbell. So they’re, they’re giving you higher quality stocks, they also generally have lower turnover. So it makes them easier to track, it also reduces your costs. But that being said, as a group, those stocks are still giving you somewhere between one and a half and two times what I would expect the long term return for equities to be with very safe characteristics. If you can have that at one end, anchoring you knowing that over the long term, just that part of your portfolio is going to help you meet your long term return objectives, then that frees you up to take more risk on the other side. So in terms of the value managers that I track, they’re not managers that are just looking for cheap stocks generally. Because that he probably be better off doing that, in fact, the way they’re often more activists and special situations, managers, and then there’s the growth managers as well in the small cap managers. So there’s a real mix of ideas. So there’s not one, when I’ve modeled this out, there isn’t a persistent style bias or factor bias to the portfolio. And that’s really what I’m trying to do. I’m trying to create alpha that’s crowd sourced, and on the point of data issues and time lags. Yes, so you’re getting this data 45 days after quarter end, and you’ve got a roughly 60 day trading period where you don’t know if the managers changed their mind, they could have sold entirely out of the stock. And what a lot of other 13 F strategies don’t do is they don’t have a risk management approach in place to deal with that. So we have a risk management approach that we use. And it’s an interesting exercise, because you’ve got to calibrate it very carefully. On the one hand, you don’t want to be arguing with the manager, you’ve chosen that manager for a reason, you’re backing them to pick stocks, you don’t want your risk management to be counteracting what they’re trying to do. On the other hand, you do have to cover yourself for what you don’t know. So calibrating it is an interesting exercise. But what we do is we use stops and other tools to manage the risk at the individual position level, but also at the overall portfolio level. And I think that’s a key part of it, because a large part of this game is really about keeping your losses small and letting your winners run. And so a lot of the techniques we use are actually the way that a proprietary trader would manage their own book. And that’s something that used to frustrate me a lot in watching long, only active managers for many years, is they used to just consider risk, often relative to a benchmark, as the goal. We don’t like the stock will own one or 2%, less of our risk is managed, if it goes down are not going to do as badly when it’s your capital. You don’t think that way. So the risk management that we use is the kind of tools that our proprietary trader would use for their own account.
Corey Hoffstein 1:23:33
So I’m going to completely jump topics. Here you are the hosts have a really, really great podcast, and you’ve had on some phenomenal guests. Yours truly not withstanding you, you’ve been one of our most popular guests, actually, you gave me three hours to blather on. I think it was a late Friday afternoon for me when I had a beer and it was very early Saturday morning for you. But I was wondering, have you had any guests on that have really made you question any of your beliefs about investing in general or make you think there might be some other ways to do this that I really haven’t considered before?
Daniel Grioli 1:24:15
Yes. So there have been some guests where I’ll give you one example. So this is a podcast that I recorded two days ago, that won’t be published for a couple of weeks until after I get back to Australia. But I was talking with a group. I won’t spoil it by saying who was actually it was myself and three guests, which is interesting. I’ve never done a podcast with three guests before. And one of them was explaining how they use trend in their models. And I’d ask the question, you know, how is trend different from market timing? What does it do? And he explained it in a way I’ve never heard anybody explained before. And it was kind of one of those classic penny drop moments where I’m thinking, you’ve actually thought about this a lot. And what you’re saying really makes sense. And what he was saying was that he felt trend was the ultimate protection for what you don’t know. And his thinking was, you can look at all sorts of indicators, valuation, etc. And over time, the relationship between that indicator and the market changes. And so you can never really be sure whether something is still working or whether the relationship has changed or whether you should be looking at something else. But you can never get too far wrong if you’re looking at the trainer. So he saw using trend in his models as the ultimate protection against what he didn’t know, because at some point, what he didn’t know would be reflected in the price surface, tracking the price, protecting himself against what he didn’t know. And I thought that was a really interesting idea. I’ve never heard anybody explain it that way. And it’s great when you’re sitting in a podcast and somebody talks about something like that. The other classic example, for me was when I was sitting down with Jeremy Grantham, in his office in Boston, and he’s just sharing all his experiences, setting up his business, and you know, going through 2000, and losing 60% of his clients and thinking that the markets overvalued and watching it double and double again, and him talking honestly about the impact that that can have on your psyche as a fund manager, trying to withstand that, and also working as a team to keep revisiting your investment thesis. And just hearing people talk about things like that. I found that to be incredibly helpful. Another great example was, I was lucky enough to sit down with Rob Arnott when he came to Australia to Open Research Affiliates Australian office. And what I’d done, something I tried for the podcast for the first time is I picked six of what I thought were Rob’s best papers. So there’s three classics and three recent ones. Of course, I had to pick the one about factor timing, because I wanted to try and stir up a bit of Rob versus cliff. Argy bargy. And it was interesting, because we got to talk about the story behind the paper. And when you read the paper, after hearing the story, it puts it in a whole different context. So if you understand what Rob and his team were thinking at the time, and what the research question was, what they thought they would find, versus what they actually found. And then the response of people because some of these papers are Rob was explaining clients and other industry participants didn’t really respond to them the way he expected. There were he was quite surprised at the reactions. So hearing all of that was fantastic for me, because I got to learn so much. And now when I think about that research, it’s, it’s totally different the way I think about it. And just more broadly, I’ve got to chat and sit down and meet some fantastic people. And also, I know that I’m sharing that with people that maybe don’t get that opportunity. So I’m, I’m very lucky. I’m very grateful for that. And I’m also very grateful for all the people that have reached out and told me how much they’ve enjoyed that. And I’m grateful that I’ve been able to share that with them.
Corey Hoffstein 1:28:14
So last question, season two, so we have to come up with a new last question, unscripted, you’re unprepared, you have no idea what I’m gonna put on the spot yet you’re on the spot. And the question is, if you had to sell every asset you own today, and only buy one thing, it can be a strategy, an asset class, particular fund, what would it be and why?
Daniel Grioli 1:28:38
Oh, that’s tough. That’s a good one. I might even steal that one. From my podcast, I could take the easy way out and say my house because I need somewhere to live. I’ll keep it to financial assets rather than hard assets. One thing? How long do I have to hold it for for the rest of your life? The rest of my life? I would say, on that time horizon, I would actually say I’d probably buy a Dow Jones
Corey Hoffstein 1:29:02
ETF, you’re gonna annoy a lot of people with Dow Jones over the s&p. Yes, I know
Daniel Grioli 1:29:06
people talk about it being a rotten index is price weighted, and all of that’s true. But you look at the long term chart, you kind of get to the same place. And given that I’m not allowed to sell it. Generally, the Dow Jones has higher quality, large cap quality. So there’s a lot of defensiveness built in there. fair bit a yield. So given that I’m not allowed to make a change, and I’m only allowed to own one thing. I think that’s probably a pretty good one.
Corey Hoffstein 1:29:32
All right, Daniel. This has been a lot of fun. I don’t think we got to the full three hours that you and I spoke last time. I think we may have gotten close, covered a lot of ground covered a lot of ground, a lot of your background. It has been great for me to get a totally different perspective on this podcast. I enjoy you stopping by and I look forward to our next conversation.
Daniel Grioli 1:29:49
Maybe it’ll be in Melbourne. Sounds good to me. Alright. Thanks Cory.