Episode #113: Stanley Altshuller, Novus Partners, “I’m Bullish On Active Management, But I Think That You Need A Correction For People To Remember Why Hedge Funds Exist In The First Place”
Guest: Stanley Altshuller. Stan is the co-founder and Chief Research Officer at Novus Partners. He helped grow Novus from a three-person start-up to a recognizable brand with hundreds of institutional clients, tens of millions in revenues, and thousands of research subscribers. Prior to Novus, he worked at Ivy Asset Management, where he was part of a team responsible for constructing, monitoring and managing all of Ivy’s portfolios.
Date Recorded: 7/09/18
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Summary: In Episode 113, we welcome entrepreneur and hedge fund expert, Stan Altshuller. Meb starts by asking Stan to give us his backstory, and how he came to co-found Novus Partners.
After Stan gives us his origin story, Meb asks about Stan’s broad approach to the markets. Stan tells us that at Novus, they start with data. This data encompasses everything from public data from regulatory filings, to private data from daily holdings reports. They bring it into an accessible, searchable database. Then engineers and programmers write various algorithms that capture and present the details of that data. This helps identify takeaways such as where the risks might be in a portfolio, and how various portfolios compare to others.
Meb asks about common takeaways from all this analysis. Stan points toward “diworsification.” As the name implies, too many investors have far too many holdings in their portfolio – from a diversification perspective, more than is needed or helpful. Stan tells us that 12 different investments is as beneficial as 100. Another takeaway Stan points toward is “conviction.” Are you truly adding value to your portfolio given your weighting decisions? Meb notes how you have to have greater position concentration to make a real difference in your portfolio. He then asks how Stan measures conviction.
Stan tells us that conviction can mean different things. For equities, the highest ROI comes from stocks with a 7.5% position or higher. But if your portfolio is highly diversified, you’re unlikely to have a single position of this size. Stan adds that, for an allocator, the threshold is about 5%.
Next, Meb asks about the state of active management. With so many headlines about flows going into passive, what are Stan’s thoughts?
Stan gives us a great synopsis, covering “dispersion” and “correlation.” The presence, or lack thereof, of these market characteristics can have much to do with the success of active managers. Overall, Stan says conditions are now setting up such that we’re seeing alpha being generated in the hedge fund space again. He tells us “I’m bullish on active management, but I think that you need a correction for people to remember why hedge funds exist in the first place.”
Meb asks about Stan’s process – what analytics help identify the good funds, what they look for, the red herrings… Stan says the first thing to do is ask whether the manager is telling you the same thing as what the data is telling you. You’re basically double-checking the manager’s stated skill set. Next, analyze whether the manager is truly going to add value to a portfolio. For instance, if you add another manager, how much diversification benefit will t actually provide? If not much, do you really want to pay their fee? Then you look at whether the manager is still generating alpha. Has there been style drift? Is he/she managing significantly more money now than in past years?
Meb hones in on one part of Stan’s comments – “performance as a metric.” This is a great part of the interview in which Stan really draws out the point that looking at performance alone isn’t necessarily all that helpful. You need to understand how a manager created his alpha. Unless you understand that, you’re a duck in the water. You cannot invest based on performance alone.
There’s so much more in this great interview: What percentage of managers are really adding value with their short book… Stan’s take on whether hedge fund managers truly deserve their fees… When is it time to give up on a manager if performance has been lagging… A major risk in today’s hedge fund space… And Stan’s most memorable trade…
This one involves Amazon and Google. Listen to Episode 113 for all the details.
Links from the Episode:
- 0:50 – Welcome and Stan’s origin story in the finance world
- 5:28 – Broad approach at Novus
- 7:36 – What are some trends Stan is seeing
- 11:33 – How Stan manages the convictions of managers
- 16:11 – Trends in active management and asset management
- 22:22 – The universe of hedge funds Stan covers
- 23:55 – Analytics and processes that helps Stan evaluate funds
- 28:48 – Hedge fund performance as a metric
- 32:24 – What percentage of hedge fund managers are generating alpha
- 35:29 – Do any managers truly excel at short selling
- 38:04 – What is the criteria for letting a manager go
- 41:24 – Is it common for fund managers to change their styles as they grow
- 43:01 – Managers shifting to quant funds
- 45:04 – Major risk in quantitative data
- 47:46 – Is it a bad idea to go long the funds that are most popular
- 49:29 – “The Madness of the Masses and Wisdom of the Crowd” – Hamann
- 49:52 – The Best Investment Writing: Selected writing from leading investors and authors – Faber
- 49:56 – Invest with the House – Faber
- 52:30 – What is on the horizon for Novus/Stan
- 55:24 – Most memorable investment or trade
- 57:21 – Follow Stan – Website blog at com and on twitter @saltshuller
Transcript of Episode 113:
Welcome Message: Welcome to the “Meb Faber Show” where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing, and uncover new and profitable ideas all to help you grow wealthier and wiser, better investing starts here.
Disclaimer: Meb Faber is Co-founder and Chief Investment Officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria Investment Management or its affiliates. For more information visit cambriainvestments.com.
Meb: Welcome podcast listeners, full on summertime show. Today we have a great show with the Co-founder and Chief Research Officer at Novus Partners. Longtime listeners and followers will be very familiar with their work. We’ve shared it many times on the blog, books, podcasts. Before that, he was at Ivy Asset Management, where he designed and implemented a set of tools and processes for analyzing multi-manager portfolios. Today he’s focusing on the role tech is playing in shaping the future of investment management. Welcome to the show Stan Altshuller.
Stan: It’s really my pleasure to be here, Meb. Thanks so much for having me.
Meb: Well, I may have just murdered your name after you just gave it to me, but you also let me know that it means old scholar, but you’re not that old. So let’s dive in. I’m excited to have you on. Been pestering you for months if not years to come on the show. So let’s get a little origin story because I only know a little bit actually about your roots and origins. Our listeners may not be familiar. I know you’re a markets guy, you run portfolios, but helped start Novus and grow it from a bootstrap startup to a pretty recognizable brand with hundreds of institutional clients and many, many research subscribers. Maybe give us a little quick career path how you ended up starting Novus and a little backstory there.
Stan: Yeah, of course, I’d love to. You know, certainly not old as my name would imply, but not too young already. I’ve been doing this for about 11 years now. Before we started Novus, you said I was at Ivy Asset Management, before that I was at a very small chic [SP] hedge fund to funds investment firm called Lyster Watson & Company. Now they’re no longer around, but there were about a billion dollars or so on. And I kind of learned the ropes. I was straight out of college, I went there. It was 2001, that will date me a little bit, but if you remember that was in the dot com bust, and it was very hard to find really good work. And I saw this space that was doing very well, that was hedged, that was protecting investor capital with a hedge fund. I became really interested in that space. And instead of going out and getting an investment banking job, I went that route, and I was very, very lucky to find you know, a great group.
I worked for a fellow named Charlie McNally who really took me under his wing and taught me the ropes from a quantitative perspective, basically, I was a quant there. Doing a lot of modelling, applying my math skills that I kind of learned in school, and forecasting returns for hedge funds, modelling the best kind of portfolios. And the first thing I realised is that, boy, a lot of fund to fund portfolios were really over diversified. You had a ton of managers in them. Lyster Watson back then was no exception.
And some of the work that I did back then was quantitatively not really proving but persuading the guys to get a little more concentrated. And I knew that there was a large part of the story missing. It’s not all quant. So I wanted to kind of expand and, you know, really understand the inner workings of the managers from the inside and that’s when I went to Ivy Asset. And I was there for three years, just over three years, and you know, we spoke with managers and we really got to find out what the investment process looked like from the inside.
They gave us a really good glimpse as to how they form a thesis, why they would invest in a stock or a bond, why they would size it the way they did, you know, how they thought of portfolio management and all the qualitative aspects were really great. That’s where I met Basil Qunibi, who’s my Co-founder and CEO of Novus. And you know, basically, long story short, we knew that with all this influx of data into the industry, you know, they’ve increased transparency they were getting from managers, the regulatory pressures for even more transparency.
We thought that the type of analysis and the type of work that investors would do in the future, would be very, very different from analysing the 12 data points a year which were monthly returns. Which was basically the predominate analysis that investors were basing their large investment decisions on. So we thought the world would change, and we spoke to a couple likeminded people and, you know, long story short we started Novus spring of 2007 right before the 2008 disaster hit.
Thankfully we were too small, just too small to register. We had just a handful of clients back then, and the crisis didn’t affect us as much as, kind of, feared and, you know, our clients stuck with us. We were very, very lucky to have that handful of clients still with us today. And, you know, no looking back from there. We just kind of took it from there and. you know, from three people up to 65 people today, as you said hundreds of clients, and, you know, on both sides…we played both side of the equation. We started with investors: so fund to fund like the Ivys of the world, family offices and institutions and the endowment and pensions.
But we also have a bunch of managers, a bunch money managers like hedge funds and loan-only managers that use our services to better understand their own investment process, and to better analyse their own data exhaust what have you, to use a term that gets thrown a lot today. So that’s kind of where we are right now.
Meb: Well, let’s talk about the broad approach of Novus, and kind of what you all do. There’s a snippet from the website that says that you bring together the world’s top technologists, data scientists, and analytical minds to help investors generate a higher returns. So how do you guys go about that? What’s the general framework or process for your approach?
Stan: I think it starts with data. So when we say data scientists we really do mean that. We mean folks that come from these very big data shops that know how to manage lots of data that’s not normalised data, that’s hard to just throw on an Excel spreadsheet and do analysis. You have to do a lot of data cleansing, data management, data cleaning. We call it ETL or the term is ETL, extract transpose and load. A lot of data work that requires, you know, folks that just understand how to manage data.
So, you know, we take all sorts of data in, from public data from the regulatory filings which is where our bread and butter was in the early years, 2007, 2009 I would say. All the way through private data, daily holdings and daily positions files that we get from the administrators of managers books are invested in. And we bring all that data into an accessible, searchable, database that our clients can access and analyse themselves. And we also have an analytics engine on top of that and that’s where the engineers come in. So we have a group of programmers and engineers, folks that actually write the algorithms that calculate things like attribution and risk, and all the other kind of detail that you want to know about your data.
How has the world changed? How has your portfolio changed in light of the world changing? How do you compare against other investors similar to yourselves or similar strategies to yourselves? And where are the risks in your portfolio? You can’t really do the latter without the former. So you can’t get to it without getting the data right. And that’s really still a really big opportunity in the industry. Folks still don’t…a lot of them…and when I say folks I mean large investors and even sometimes asset managers.
A lot of asset managers do not have all their i’s dotted and t’s crossed, all the data in a searchable good format. A lot of people invest in alternative datasets these days, but we focused on the intrinsic data. So the data exhaust, the data that comes out of your own investment process, and that’s what would help folks monetise.
Meb: So what does that actually kick out as far as takeaways? I mean you mentioned a lot of times these end investors, the real money guys often either have too many funds, you know, we joke a lot about it on the retail side, where investors often will come to us with dozens of mutual funds. And the phrase we always use is mutual fund salad. So I guess you could have it for hedge fund salad too. Feel free to steal that phrase. It’s from Josh Brown.
And so you have this portfolio and so you’ve seen probably hundreds if not thousands of end user portfolios of hedge funds, or active managers in general. Once you’ve kind of started to spend enough time with the data, what are some of the end takeaways from the end investors? Are there some obvious mistakes you see? I imagine performance chasing is always a big one that people talk about. What are some of the major considerations that you guys come across?
Stan: For the investors, diworsification is very real. There’s a lot of research out right now saying that you basically get as much diversification benefit from a dozen different managers, or a dozen different investments, really, even if its stocks as you do from a hundred. So the net benefit of diversification goes down dramatically after you’ve got more than a dozen investments in your portfolio. You just need to have negative correlations in some of those investments. And you don’t really need that many.
So the most obvious mistake number one, boom, right there, you know, just too many things in a portfolio at once. And then you get into this idea of conviction. How much conviction do you have in your investment? If you’re investing in managers, you could do a very simple analysis that we always do for our investors, an equal weight portfolio. So meaning you have the same dollar amount month to month in every single manager, equal weight portfolio versus your actual return.
Are you adding value with your sizing decisions against an equally weighted portfolio like a dumb portfolio that has no decisions? No portfolio management skill whatsoever, just selection. Usually, by the way, you lose out to an equally weighted portfolio unfortunately. Some of the more concentrated strategies do a lot better in that sort of analysis where the PM really truly understands the relative value of these managers, the relative value that they bring to the portfolio, whether it be a correlation-diversification benefit versus the true ROI that you get on a manager, so those types of things.
Smaller position size is hurt and this is true of investors and money managers as well. One of the most common things we find with hedge fund managers is their smallest positions tend to be a drag on their performance. They’re just in there. They’re farm names. They’re on the radar so that the analysts might not forget about, really. You know, it’s like a technicality that if you take out of the book might add an additional 20 basis points a month to your P&L. Same is true for the investor’s tiny positions whether it be either they’re just locked up and they can’t get out or they just happen to hold these managers for the lack of any better ideas, you know, to invest in them some other day.
These things tend to be a drag on performance. And unless you quantify, unless you calculate and know exactly how much of a drag it is in your performance, well then that’s not an active management decision. You’re not really making an optimal decision for your portfolio unless you know exactly how much it’s costing you. Very few people actually know exactly how much it’s costing them to carry on all those smaller positions.
So that one, obviously, you know, another big one is just pure manager selection. When I was investing back at Lyster Watson and at Ivy, these big names, these hedge funds with a huge aura, you know, and a huge club-like, cult-like following, attract a lot of attention regardless of the merit. You know, you’re not gonna do a big analysis on Pershing Square, you’re just gonna invest in Bill Ackman, really, because he’s awesome, right? But if you do the analysis, you know, maybe Bill Ackman is bad example. But if you do an analysis and your manager is saying you invest based on merit, based on repeatable skill, based on demonstrably value, things that you can actually demonstrate, you can put two numbers instead of just being a great salesman. If you invest like that, you’re gonna have a very significant edge over your peers.
Meb: You had a number of interesting comments in this discussion. One, we often laugh because there’ll be investors that we’ll talk to and they say, “Oh, Meb, I’m stressing out, I’m thinking about adding commodities to my portfolio but I just can’t decide.” And you say, “What’s the position size?” And they say, “Well, I’m thinking about adding, you know, 1% of my portfolio.” And you say, “Look, you know, you need to add for many of these asset classes or managers in many cases like 5% for it to even make a rounding error difference.”
But I think the challenge for a lot of people, of course, it goes back to that old school career risk, where if they have one conviction manager or an allocation to an asset class that does very poorly, it’s a lot easier to get fired. And you see that as a drag. I wanted to follow up a little bit on your comment about conviction because it’s interesting. So if you’re looking through these managers, how do you measure the conviction? And I know you guys have done a little work with Barclays here. But is it that you’re looking at conviction meaning it’s the biggest holding in their portfolio, or rather is it the biggest relative to a market cap weight? How do you guys measure conviction? Because that can mean a lot of different things?
Stan: Let me just add a little bit of colour to that point you just made about de-risking or CYA covering. You know what, it’s that, exactly that that leads to poor performance, and it’s exactly that that leads to return chasing and allocating based on size versus based on merit. That’s where all the money, the new money, is going to the large managers simply because no money gets fired for hiring IBM. And that is really unfortunate, you know, you can’t do that if you’re fund to fund, or if you’re a fiduciary managing money. But unfortunate truth is that the CYA is a very, very big asset. So you’re right on, on that.
And I would think that our clients are very different. We actually help them calculate the true value, the true diversification. When you have the data behind it, you’re empowered to take educated risk. You see the CYA fear goes down because you point not to the fact that this person wears a nice suit, that’s why I invested in them. You point to the fact that they had repeatable skill and they showed alpha here and here, that’s why I analysed it, no one else analysed it that way. That’s why I invested in the small manager. Just that little point, it’s very, very relevant that you made.
Now to go back to the conviction question, conviction could mean different things. We have done a lot of work with Barclays on basically backtesting what conviction means with hedge fund managers. So we found that the highest ROI, the highest return on invested capital, dollar for dollar, on average, comes from stocks with a 7.5% position or higher. Meaning the maximum managers invest in a certain stock with a larger than 7.5% position of their portfolio, percent of their total equity or their total holdings.
Managers that are super diversified they’re never gonna register a 7.5% position at anything. So we’re only talking about a small subset of the universe, and you get numbers like 50 managers invested in Facebook and 25 of them are invested with conviction. And you take other stocks like that and you rank them and it’s exactly what we do with the Barclays you’re referring to. You rank them based on number of managers invested with conviction, 7.5 and above, you do the backtest, were running it out of sample now for a while, and lo and behold, the 20 stocks with highest conviction outperform the market by something like 600 basis points a year, something crazy.
And it’s been consistent and then, you know, and we thought it was so compelling that we went out and we launched an index with Barclays. It’s actually called the Novus Barclays Conviction Index. And I’ve got 50 names a little more diversified, but it’s got the same exact idea powering it. So in that sense, conviction is 7.5%. Now for an allocator, we did similar backtests for allocators, it’s about 5%. So the conviction threshold is a little lower, they’re a little more diversified on average. You can’t really find too many allocators with 7.5% positions to managers, just not that many, it’s really not that many. Some of our clients do have these chunky positions and they’ve done very, very well.
But for the most part, I would call anything above 5% of your equity given to one manager, that’s a conviction trade. You got a couple of them with a portfolio, that’s gonna be your core value driver. So you’ve got three or four conviction managers, 20%, 25%, that’s gonna be what’s gonna be driving your returns in a go-forward basis. You better have a lot of conviction in them, they better have some favourable correlation, properties, so good diversification to the rest of your portfolio. They’re not invested in the same exact stock, so the other manager…kind of the basics, they’re doing something unique and they’ve still got a good story. There’s still a high conviction for ROI behind those managers. So I don’t know if that answers your question.
Meb: Yeah, it goes back to the whole, kind of, theory of in a world where you can invest in a market cap weighted portfolio for essentially free, but let’s call it five basis points. If you’re going to be different, you need to be concentrated and active and in many ways, you said weird, where the portfolio, it makes no sense to pay for these closet indexing funds even if it’s 50, 100 basis points because it doesn’t move the needle. You really have to have something that there’s a lot more conviction, and it makes sense to me.
And I wanted to ask you a little bit, kind of, take a step sideways or back perhaps and, you know, we’ve had this pretty interesting last 20 years where a lot of the hedge funds and fund of funds first part of the 2000s had a great relative returns, and many have struggled since the global financial crisis, and many struggled through the global financial crisis. And so there’s two comments I would like to ask you about here. One is talk a little bit about the state of active management where all the media headlines are, “All the flows are going into low cost ETFs or index funds or something like that.” And second, maybe make a comment or clarification about, you know, when you say the word hedge fund, it means a lot of different things to different people. You know, there’s long-short equity, there’s quantitative trend following, and CTAs, there’s global markets neutral, all that good stuff.
I assume we’re majority talking about long-short equity space here, but you can qualify as you see fit. So two questions, one what are the trends in active management and asset management in general? And two, kind of, what specifically are the main fund styles you guys focus on?
Stan: I’ll tell you if I read another article of pensions so and so, you know, divesting for managers because of high fees, it makes me very sad to see the that. Because if you compare it against the S&P 500 over the last 10 years, obviously, that’s not a very fair comparison. And you know, if you bought life insurance and you didn’t die over the last 10 years then that money would have been wasted too. So if you go back to premium, and if you go back to 2006, 2007, you see a couple of things in the market that are very, very helpful for active managers in general. You saw a lot of dispersion between the good and the bad stocks.
Dispersion means you take the whole market of the S&P 1500 or whatever, the Russell 3000 works too, and you take the upper half of the performers and the bottom half of performers, and you take the diff, and you roll that, right on a 24-month basis or whatever. The difference between the outperformer and the underperformer was massive in 2005, 2006 going into the crisis. Next, you take the concept of correlation. Correlation means how much these equities…and even bonds. This was true across asset classes, but more so you saw it on long-short equity space.
How much do assets perform in line? Meaning if one stock goes up, what is the likelihood the other stock in your portfolio also went up? So correlations were actually very low. So the returns in these securities were not interdependent. They were acting by themselves in isolation, a lot of idiosyncratic risk. So what that does is if that sort of environment rewards active managers, it rewards the good research, right? If you’re an analyst from Harvard, you’re sitting there at, you know, a stock picking shop or a long-short equity, or a market neutral fund.
You’re constructing your bear trade, right, and you’re looking for good stocks that outperform and bad stocks that underperform and you’re doing a very, very good job, the sort of environment with high dispersion and low correlation is gonna reward you if you’re doing a good job, right? You’re gonna buy a stock that’s gonna go up 10 percentage points more than the market went up, you’re gonna short a stock that went down 10% more than the market went down. And you’re gonna pocket 20 percentage points, beautiful, okay? The crisis hit, the rest is history, money started flooding the markets, rates went to negative, quantitative easing from all over the world.
Now what that does when governments work in unison to flood the economies with easy money, what that does is it destroys any sort of dispersion and it drives up correlations. There’s only two points in time when correlations are gonna high, during times of stress, extreme stress where everything’s going down correlations jump to one, or during some of the extreme easing and low-interest rates where everything is going up. Correlations are low too, correlations are like, one. Everything’s going up and that’s what you’ve had.
You’ve had an environment of extremely low dispersion and extremely high correlations where the markets just did not differentiate between the good and the bad, they weren’t trading on fundamentals, they weren’t trading on idiosyncratic, you know, any fundamental drivers within the management or within the growth prospect of a company, barring a few tech names and a handful of the banks, right? Everything else in the market was not working according to those rules. It was just lifting up or both.
And as a hedge fund manager, you’re gonna get crushed. If you’re good, you’re gonna get crushed. If you’re bad, you’re gonna get equally crushed. It doesn’t matter. The skill you have is valued at zero by the market. So what’s happening now? Now it’s coming back. Now correlations are crashing and people are scratching their heads and saying this march is long in the tooth and the governments are promising to end quantitative easing finally and they’re probably gonna do it. They are increasing interest rates, interests are going up.
And while hedge strategies are still taking out of the nose and getting beat up by the media and all that, all of a sudden they’re generating alpha again on the long side. All the stuff that used to work is now beginning to work again. They’re just beginning to get traction. And you could see it literally from a rolling alpha chart of HFR against S&P. You’ll see that the alpha was negative, maybe 6 to 12 months ago. Now it’s zero. Now it’s in the positive territory again. That’s a 24-month and a 5-year basis.
So things are coming back, I’m bullish on active management still, but I think that you need a correction for people to realise that, and for people to remember why you need the insurance in the first place, why hedge funds exist as an asset class in the first place. And that, unfortunately, doesn’t come without a little bit of pain. So when that pain is coming on, that’s anybody’s guess, but, you know, when it does come, obviously, the hedge strategies they’re gonna be there.
Meb: What’s the universe for you guys, the hedge funds? Is it purely long-short equity, or do you cover arbitrage, stat, quant, trend following, CTAs? Is there a particular subsector of the hedge fund world you guys are focusing on?
Stan: So think of where we will add value, think of managers that have many at best, right, so they have many trades, they’ve put on a good number of trades where you can actually analyse and make sense out of trends, right? Every time you make it outside [SP] position and a health care stock with poor liquidity, you lose money, or you’re expected return is negative 1.2 ROI, okay? Think about managers that have that sort of repeatable process, and then anything is fair game. We’re multi-asset class, you know, the sweet spot traditional client for us, what would be the equity strategies and equity link strategies like options, but we’re very much multi-asset class.
My colleague, Adam, is doing a lot of work. We’re partnering up with back set [SP] so that we can really bring this world of skill set analytics to the fixed income world, the multi-asset class world, and we’re getting more and more clients outside the traditional long-short equity mould. So think about basically anyone that really cares about being retrospective, analysing their process as far as managers go, and getting better, really, right, just improving. That’s fair game for Novus, and that’s good clients for Novus.
Meb: Good, I’m looking for the day when you guys expand into publicly traded funds, and we can put in a bunch of ETFs and mutual funds onto the platform too.
Stan: Oh, Meb, for you I’ll do that pro bono [crosstalk 00:23:52].
Meb: Good, all right. So let’s talk a little bit about… People who are listening to this say, “Okay, I believe Stan. He makes a good case for active management. I’m ready to go start allocating my billion-dollar endowment to active managers. Walk me through maybe the process of what analytics will help you determine what are the good funds, and sort of what is the process there? What are the main things you look for? What are the things that other people look for that don’t matter? The whole general, kind of, like how do you then go select the good guys and separate them from the junk?”
Stan: This is the core of what our investor clients use Novus for. And the number one thing, the first thing that they look at before they decide to invest in a manager, you know, before they have that investment committee meeting. When they walk into a manager’s door, the number one question they need to ask is, is this manager telling me the same as what the data is telling me? And if there is a big discrepancy, they don’t invest in the manager or they really need to understand why. So meaning, if the manager tells you that they are good at, oh pick a skill set, right? Managers have tons of degrees of freedom to generate output.
So let’s say they’re a Paul Tudor Jones and they’re great at calling market tops the markets bogs, right? So then the client asks, “Okay, how is your portfolio done against a static net exposure return portfolio static net? Let’s say you didn’t move your net around at all, you take that skill set out, has that skill set that you’re telling me you’re good at been additive or is it been detractive? Did it detract from the performance?” But basically using data to understand what the manager is telling you and if it’s actually true.
Another case manager tells you they’re phenomenal at picking stocks in the health care sector. Great, given your net exposure, so all held constant. Forget about your net exposure management skill and gross management, all else held constant, do you generate security selection skill with the same exposure to the health care sector over time? Do you compound alpha in that sector over time? If those things align, it’s, like, all right, you check the first box. So he’s not trying to pull a fast one. He understands, this manager, he or she… This manager understands their skill set, they understand the value proposition to the investor, they’re thoughtful enough about it, and they’re telling me the truth or they’re telling me something that makes sense that I can corroborate with data.
That’s number one. Number two, like, all right, are you gonna add value to my portfolio? So, you know, the number two analysis, first thing they look at the data and they say, “Okay, I already have an existing stable of manager.” I’ve got 12 managers, 20 managers what have you. If we add this manager to our stable, how much diversification benefit do they provide? So we’re on something called an overlap matrix. I’m sure people have heard of a correlation matrix. This is a forward-looking analysis. So correlation looks back at the returns. We don’t care much of the returns, returns can change. Returns are not good for analysing hedge fund managers because of the reasons across there’s too many degrees of freedom. You don’t know what the returns were caused by.
So you look at overlap. Look at everybody’s portfolio in a snapshot of time today. What does the portfolio look like today? If we were to add this health care manager, how much active management skill are they adding? Just 5% of their book unique to my managers, what they already hold, or is it 90% of their book, or is it 50% of their book? And if it’s only 5% uniqueness that they’re adding to my portfolio and they say that they’re good health care stock pickers, do I really need to pay them this fees? That’s their ultimate critical question. Do I need to do that or can I just double down on the managers that already have the same exact health care stock picks in their portfolio? Maybe I just invest more in them and I don’t diversify and I don’t pay an extra layer of fees.
So that is question number two. Once you’ve built your portfolio, you’ll obviously want to understand, does the manager that is currently holding your portfolio still add value? So if they were particularly good back three years ago when you invested in them, if they were particularly good in small cap financials, they were fantastic at finding these, you know, situations small banks, you know, and they added a lot of alpha, did they now change? Did they style drift? Did they grow to the point where they can’t invest in the same sector where they generate a lot of alpha?
You need to understand a couple of things. You need to understand, do they generate alpha in small, mid, large mega, and do they generate alpha across other sectors? If it’s only health care, and if it’s only small, and if they’re running $7 billion right now when they used to run 600 million, then that skill set is really under attack, and as investor you need to understand where that alpha is gonna come from still paying the same fees. You know, the manager is just bigger now. So that sort of style drift is, you know, a major, major focus for our investors.
Meb: You sort of dropped a bomb quote there and just continued on as if it wasn’t like the most important quote probably from this podcast. But it was something along the lines of…and I’m gonna totally mischaracterise it probably. But you mentioned performance being not the thing to necessarily look at, but what does 99.9% of the investment world, and I’m including institutions in the same bucket, focus almost entirely on and it’s simply trailing performance? And this goes back to an old comment that we often make here which is the focus on the process rather than performance.
But every single investor I talk to almost without fail is really all they talk about and the questions all pertain to that. Maybe touch on that briefly and along the same thread, what percentage of funds that you think cross your desk, there’s what, 10,000 hedge funds now or something, do you think are actually adding value? And then second, is that actually can add value in the short book? So performance is a metric and then what percentage actually do add value?
Stan: Okay, performance is a metric. This is like our mantra here. This is why we started the business. At Ivy, we only analysed performance. We thought that was madness. Here’s why. It could be okay to analyse performance for a mutual fund. Why? A mutual fund or an ETF, ETF is even less so, but a mutual fund has very few degrees of freedom to generate returns. They have security selection. They can be an asset allocation. So they can be allocated to certain sectors where they’re taking active bets and they monitor that. And they can be, you know, overweight, underweight, sort of, securities where they’re taking bets. If they’re underweight apple, they’re short apple. Really, that is it.
Now there’s also exposure [inaudible 00:30:33] so they could hold cash position. They can be underexposed to the market when they think that allocations are raised or whatever. So those are maybe three fair enough vectors of return generation. So I’m not gonna be arguing saying you can’t look at, you know, “Morning Stars,” at, you know, all nonsense. You should be looking at performance of mutual funds. It’s not misleading. When you’re looking at hedge funds, it’s extremely misleading, extremely misleading. It’s actually negative value.
And the reason for that is because hedge fund managers have a ton of degrees of freedom to generate performance. You absolutely need to understand how they generate a performance. How they did it. Did they do it with trading? Did they do it net exposure management? You take net exposure out of the question, completely different. Did they do it with position sizing? Did they do it on the short side? Did they do it with macro calls? Did they do it with investing in just one stock? You literally have no idea. You don’t know that just by looking at performance. And all this junk analysis that you run on top of performance stream correlations up the wazoo, you know, you take 12 data points and you derive 100 data points out of that 12. The kurtosis, the skewness, the this and that and that.
It’s all noise. It’s all junk. None of it works. It’s misleading. I feel passionate about it because it actually hurts investors. With a mutual fund, a top quartile performing mutual fund will [inaudible 00:31:53] quartile mutual funds with about two to three years. That research has been done. With asset managers, they mean and revert like crazy. A top performer yesterday is not a top performer today. And then they could be a top performer tomorrow. And unless you understand the inherent way of how they generated that return, unless you really take the time and break it down and you understand the how, you understand, they give you the freedom they pull [SP], you do the analysis against the status quo portfolios, unless you do that, you’re a duck in the water. You can’t invest based on performance.
Meb: And so what kind of percentage, when you look at these guys, do you think are actually good at their jobs, they’re actually generating alpha? This is probably a really hard question to answer. And then on the flipside of the same coin I’m always curious about the second part is are these guys actually good at shorting or do they just use it as a marketing piece to charge 2 and 20?
Stan: The stuff that I see out there saying only 5% of managers deserve their fees is complete nonsense. It’s absolute nonsense. I don’t think it’s that low. When I’m looking at portfolios and I’m looking at clients, my understanding of worth, like worth your fees, is very subjective. It is based on the mandate of the client, and the fit in their portfolio. Because let’s be honest, if they weren’t worth their fees, the industry wouldn’t command $3.2 trillion still today. It just wouldn’t. And people would just leave.
It’s a completely, you know, open contract. You know exactly what you’re getting. You can get out of the fund in 90 days or even less notice. So the industry is still here, that means more than 5% of hedge funds are worth their fees. So the quantitative measure that I look at, the first one is uniqueness, how unique are you? We did a great analysis with my guys two years ago and we looked at how unique are the managers that, you know, we cover. And we found that there’s much more uniqueness than people give them credit for.
For instance, one myth that I’m gonna bust right now is that hedge funds are overweight FANG stocks. Myth, false, completely wrong. Hedge funds are underweight FANG massively when you compare it against mutual funds, when you compare it against indices. Massively overweight FANG like by half, okay.
Another myth, every hedge fund is doing the same exact thing. Completely wrong. 60% of hedge funds, 6-0, 60% of hedge funds have unique portfolios when compared against the common portfolio of 100 most common names. That might have been hard to follow but I have a portfolio of 100 most common names that hedge funds hold. It’s like a massive Clemenger [SP] portfolio here of 100 most common names, Google, Microsoft, Facebook you got it 6-0. 60% of managers have less than 40% allocation to those names. The managers are unique.
And if you really wanna talk about performance, I’ll talk about performance, but I’m not gonna talk about it in an absolute sort of way. I’m gonna talk about it in relative sort of way. So if you look at managers and you say if you exposure adjust their returns to the benchmark that they’re benchmarking themselves against, then over 50, and I would say probably over 55% of hedge funds actually generate value on a risk-adjusted basis to their benchmark.
Meaning if I’m a healthcare manager and I’ve got a long and short, I’ve got a long book and I got a short book, and I’m 45% net. If I take the iShares healthcare index, and I have to multiply it by 45%, 55% of managers are gonna beat that benchmark. See what I mean? That specific benchmark specific to them. So it’s a lot larger than people say. You know, of course, if you look at how many beat the S&P 500, not a lot, under 10%.
Meb: Is that, sort of, the same framework where you say on, sort of, the…after you look at the exposures? In general, are these guys…do you think they’re pretty good at shorting, or is it takes a special Chainos sort of mindset to be a dedicated short seller? Are there some that are good and most are terrible? Any general thoughts there?
Stan: Yeah, no I have a few clients that are dedicated short sellers. I don’t know why they do it. It’s the most difficult thing in the world, and you have to have… I don’t know if I’m allowed to say balls of steel in your podcast, but you really have to be…
Meb: Yeah, all those short sellers that are dedicated guys are always a little something wrong in the head, a little wonky. I say that very lovingly, because I’m good friends with some, but they all got a screw loose, that’s for sure.
Stan: Oh my God. You know, it’s just such a tough business, and especially over the last 10 years. So here’s what happened. Up through late 2017 since the recovery, shorting has been a disaster, and really, I’m not gonna be way off by saying that probably 80% of managers haven’t been able to generate meaningful alpha on the short side, it’s been a disaster. And this is due to the fact that correlations are low. Remember if you short a stock, correlations are high. If you short a stock that has very bad fundamental, declining fundamental as a shitty business, but correlations are super high, it’s still gonna go up on you. And when the short goes up, your position grows. When a long goes down, your position kind of risk controls itself, and you’re less exposed to whatever it is you’re long.
If you short and the short runs up on you, you’re increasing your short exposure. Managers hate that. They cover, they cover, they cover, the cover. And they lose money on trading on the short side because of that principle, they lose money on the short because correlations are super high and, you know, everything is floating up regardless of the fundamentals. It doesn’t matter what kind of research you do, good or bad. That is what’s changing right now. Correlations are crashing, and when I say correlations are crashing, I mean intra stock correlation. One stock is not correlated to another in the market.
Also, set intra sector correlations are crashing. That’s probably the bigger story. So sectors have been completely diverging. They’ve been going in a complete haywire fashion. So you can generate a lot of money just doing active calls and asset allocation, doing active calls on sectors. And managers are doing that, managers are still picking the declining businesses and they’re beginning to make alpha only in the last 12 to 16 months are they beginning to make alpha again on the short side.
Meb: That’s interesting. Shorting to me is like the most fun, interesting world to follow, but it’s so hard for a lot of the reasons you mentioned. So probably the hardest question I’m gonna ask you today, at least hardest for me and probably hardest for a lot of investors out there is, all right, so say you’ve got your alpha juice group of 10 or 20, 30 managers. You love them. They’re awesome managers. When do you let them go? So what is the correct criteria for saying, “You know what, this guy is just no good anymore. It’s time to liquidate this allocation,” because I think a lot of people really, really struggle with that.
There is the pretty famous story that came out in the past week, not a particularly great story, but we’re talking about David Einhorn for example, and listing kind of a number of things that may or may not contribute to the challenges. And a lot of these aren’t quantitative. They’re stuff like going through a divorce or having a large amount of assets. So maybe becoming a little bit complacent, or hey you’re interested in buying a baseball team at this point, who knows? What are some of your advice, analytics, thoughts, on at what point do you call or straight up fire an investment manager?
Stan: You know at Ivy average lifespan for a manager was three to five years. I have some clients today, 70 years and longer, they stick around with his manager through thick and thin. They really understand the manager. And even when they’re going through a hard time, they have a very strong conviction with the manager, thinking that the returns are gonna come back. But here’s when they still fire the manager. The problem with fees right now is not they’re too high or too low, but the problem with fees is that the sweet spot where the interests are aligned between investor and manager is very small and misleading. It’s just a sliver where the interests are actually aligned. And when a manager grows beyond that certain threshold, call it 2 billion, and I didn’t do the research on this, but you know, call it whatever, 2 billion.
Where the management fee alone becomes a big driver of their end of year take-home pay cheque, and that becomes a life risk management exercise, not a fund risk management exercise, do you know what I mean? They’re managing for the business risk. They’re not managing their portfolio in the same way that they used to when they were a $700 million fund. And you see the risk tolerances go down. You see the diversification go up. You see the style drift from a money maker, from a stock picker, from someone who’s hungry doing the research to an asset gatherer, to, you know, just getting to a size and staying there. And their risk controlling their business, they’re not risk controlling their portfolio.
It’s a hard thing and, you know, there has to be a lot of qualitative and quantitative aspects for our clients to actually pull the trigger, but that is a very common one, unfortunately. The obvious one is when a manager grows so much and their ego grows with them, now I’m really gonna get in trouble, and then they stop talking to the investor. That is an easy one. When they stop providing the transparency that they used to provide, when they run with their ego, and you can see it from the trade, they hold a trade that’s definitely a losing trade. They would never have done this 10 years ago, but now they do this sort of stuff where their ego is driving the book.
And they don’t, kind of, talk to you, and they don’t level with you, and, you know, you get the sense that they’re not really very forthcoming with the investors. That’s an easy one. That one is just, “All right, I can definitely find better use of my money,” and they pull the trigger and they get going.
Meb: There’s a side that, and this is a totally bleacher comment from someone who’s not that involved in your world, but, you know, we look at a lot of the old school managers, and one of the challenges when they start to drift into areas outside their core competence, and as they grow in size, it’s a lot of cases where they’re, sort of, forced to, or maybe they need to find bigger pools to wade in. And one of them that… It’s like catnip for long-short guys for some unknown reason in my mind, but is getting attracted to the macro space.
And the longs-short guys, it’s like love. There’s nothing they love more than talking about Fed policy and gold, and whether it’s market timing or trends. Is that something you’ve seen as far as shifts in style and focus? Is that relatively rare or common, sort of, behaviour out of fund managers as they grow? And do you consider that to be a warning sign?
Stan: I’ll tell you. I think it’s fairly rare, actually, and it’s definitely a warning sign. It’s very easy to check for that. You know, you’ve got a stock picker that loses money on broad market calls or big macro themes. They either cut it out, they do the analysis and they stop it themselves or an investor asks them to stop. And, you know, it happens rarely. The big theme that I’m seeing right now is definitely this quantification or, you know, this turning to alternative data sets from traditionally fundamental managers, managers that used to pick stock, consumers discretionary stocks, the Julian Robertson shop. So you’ve got Maverick launching a couple of quant funds right now, and really allocating to them. I wrote about that.
Meb: Did I see, and maybe it was you writing about it. Did they launch some sort of like 13-F replication fund or was I in Europe or something or am I just imagining that? I saw they did some quant funds.
Stan: They have a quant fund. It’s not a 13-F replication strategy to my knowledge. Its literally a quantitative kind of machine learning AI-driven selection process where the computer analyses tons of data. It’s this latest trend in analysing alternative data sets. Satellite imagery, click data, social data, credit card data, tons of spending data, that’s anonymised from the data owners anonymising, they sell it back to the hedge funds. So that is how they did it, and they’ve hired people I’m sure and they’ve allocated to it. I don’t know what the returns have been, but it’s a massive change and big departure for guys like Lee Ainslie and [inaudible 00:43:46]. Code 2 is the latest one. That was in the news. They had a stake in an alternative data company, and that’s today’s macro call, I would say. That’s the macro of what managers were doing about 10 years ago. Now they’re doing this, everyone seems to be investing in it. All the large target cubs.
Meb: It seems funny to me. Like, you know, we used to say, I think 10 years ago even, that you know, a lot of the quants in my world, I mean, the best and brightest, that’s AQR, LSV, Goldman, etc., you know, people talked about multifactor investing. I said, “Look, these guys all have the same Ph.D.’s, they all have the same CHRIS database, I don’t know how many ways you can mine this because if you pull up a stock all five of them own it.”
And so I often hear about a lot of hedge funds using these new databases now and the classic, of course, is the satellite imagery. But I said, “How many of these new databases can there be?” And so you really, it seems like have to either come up with a database no one else has, which if you’re the database seller seems like a crazy business model because you wanna be selling to all the hedge funds, not just one. Or two, be able to massage that in a different way. And going back to the old, “everyone has the same Ph.D.’s,” that’s tough. But it’s an interesting area and seems to be growing leaps and bounds over the past couple years.
Stan: It’s an amazing area and that brings me to the major risk that’s brewing right now in the hedge fund space. And you’re absolutely right, they have the same Ph.D.s and the same data input. There’s about 250, 2-5-0 of these alternative data providers right now that are very popular. So the types of data that they sell you are already mentioned. The basic one is credit card spending data, satellite imagery to try and time earnings, to try and basically see if they beat or underperform with basically the size of the parking lots, and how many cars there are in these retail outlets.
And then there’s tons of others: social data, click data, sentiment data, and now there’s 250 at least in all, all selling to the buy side. And then there’s the data owners that produce this data exhaust which are the social media companies and the credit card companies. And then there’s hundreds of intermediaries that package this data and sell it. And they try to sell these hedge funds as some analytics, some value-added analytics, but they’re all the same, that’s the thing that boggles my mind.
All these things are kind of the same. You know, you’re looking at the same data and you’re trying to trade ahead of earnings. They’re gonna give you the same signals. And so what people often forget is in August 2007, quants, all…either they probably predicted the 2008 disaster early, and they all issued red sell signals, all at the same time. And we had a couple of fundamental guys go under and get clobbered without even understanding what happening. It was the quants that just happened to be coincidentally invested in their name, all selling out at the same time. It was a disastrous two-week time period. I don’t know if you remember that August 2007.
So if you look at it today, the leverage and the sheer dollars that are invested in quant strategies, we have a way of figuring that out, we track a large quant peer group which is massive right now. It’s at record. The number of dollars invested in the same quant stocks is double what we had going into 2007, double. And the liquidity is not half, but close to half. They’re not gonna be able to sell out.
The other thing that you’ve got is you’ve got all these fundamental managers trying this too. They’re hiring the same Ph.D.’s, they’re looking at the same signals supposedly of these, you know, traditional quant shops and they’re doing it too. And they’re putting real dollars to work. You know, I don’t have a crystal ball in front of me, but if that were to happen, if these things converge on another sell signal, you’re gonna have a lot more participants that get hurt, and you’re gonna have a lot deeper red, you’re gonna have a lot deeper red right now [inaudible 00:47:27] 2007.
Meb: You just made a wonderful case for our terrorist fund, thank you. I think it makes me wanna invest in the companies that are providing the data to all these hedge funds, because we all know hedge funds will pay anything for a good source of data. And so they’re probably making money hand over fist. You know, it kind of reminds me of Goldman publishes a basket, and I haven’t looked at how it’s doing, but it seemed to me like a really silly long on the idea. I think it sounds like a great short idea where they would track the hedge fund universe and go long, essentially the most crowded is the most popular names.
They called it the VIP basket, and that seemed like a very odd investment approach. Do you disagree with me? Do you agree with me? Is that sort of like the probably worst investment idea on the planet?
Stan: Oh, funny. You know, I know that work. I used to religiously read it. I don’t know what happened to it, but they had a copy and used to publish this thing. I know exactly what you’re talking about. So they had this one minor tweak. It would be, you know, a dumbest idea. It would actually be a short candidate if not for this one tweak that they had that made it kind of okay. So if you read the fine print, they don’t actually just pick the most common stocks which is a short, you know. It’s actually a short, like, we track the most common stocks they underperform. You know, and I could spend another hour talking about why and all that stuff, but, you know, no conviction blah, blah, blah. But what they did is they said, “Okay, it’s the most common in their top 10,” in their top 10. So that just added a little bit of conviction and it…
Meb: Because it adds, yeah, the conviction filter. Okay, interesting.
Stan: So, you know, it was a bad idea combined with a good idea and so it was kind of okay. You know I mean?
Meb: We used to say we we’re gonna launch an ETF and invest in the most crowded hedge fund positions because if and when they wanna get out of those and, like, you would probably imagine a Valiant, I wonder what’s most common now. I’m sure Apple’s gotta be up there.
Stan: It’s Facebook. Five years ago it was Apple. Its Facebook today. One out of every four managers hold it. It’s kind of crazy. That one’s really, really big right now.
Meb: We’ve got a couple more questions and then we’re gonna have to wind down. Otherwise, I think we may have to have you back on in like three or six months, because I have three more pages of questions. You guys had a guest post on your blog recently. And by the way, listeners, we’ll add show notes to all this because there is a gazillion great pieces on the Novus site to focus on. We talked about the Tiger Cubs. We actually had some research from Stan and the crew in our, “The Best Investment Writing,” book, and our book, “Invest with the House.” So you’ll see threads that they have published over the years, and we’ll link to some of those on there. We’re not gonna touch on them today.
But you had a great guest post and I’ll see if you, kind of, agree with this or disagree with this, or have no opinion. It was from Kellogg Hamann and it said, “A group of hedge funds hasn’t been this bearish since the lead up to the financial crisis. Prudent investors may want to pay attention to the track record of this small crowd.” Is that something that you see on a, sort of, industry-wide level that there’s some interesting analytics that you can tease out from their broad positioning? Or is it more complicated than that?
Stan: Yeah, that guest post is from K.C. Hamann. He’s a really great guy. He runs, you know, one of the most smartest 13-F strategies I’ve ever seen. This guy really takes it to the next level. I checked that work and, you know, he looks at the overall weighted beta of a portfolio is an indication of the forward price movement of the market A, and then of the sectors. His sector works particularly [inaudible 00:50:54]. You know, I’m seeing the same exact results. So it seems to be that managers are kind of bearish only taking beta into account. There’s obviously a lot you’re missing when you’re looking at public filings. You’re not looking at net exposure. Net exposure is the ultimate indication of bullishness or bearishness, and net exposures are still very high, no one hunkering down for, like, a disaster scenario or anything like that.
In fact, the opposite is true. People are increasing their net as a general rule, what I’m seeing right now. But the work that K.C.’s done is really, really good especially around understanding conviction of a particular group. See, his point is there’s madness in the crowd but there’s also wisdom in a certain crowd. And he picks that crowd very, very well based on quantitative methods, and he looks at their predictive power, and he sees, “Okay, these guys are really good at managing portfolio, and they think about the data of their portfolio.” And looking at that as a signal, you know, he’s seeing definitely some bearishness surely in their portfolios. But yeah, that’s a great piece from K.C. and I encourage your readers to take a look at that. It’s on the Novus websites.
Meb: We’ll link to it. It’s always been my favourite back in the…when I used to spend a lot more time looking at hedge funds. I always loved the ones that were truly weird and different, where you look at their top 10 holdings and half the time you didn’t know even what those stocks are. If you see the ones that, like you mentioned, owned all the hedge fund hotel names, those to me were always less interesting. So like the Bauposts and the Carlo Kennels [SP] of the world that own these things and I have to like literally go Google them because I never heard of them, they’re always the most fun.
All right, a couple more, we wind down. You got a curious mind, you’ve spend a ton of time with this data looking forward for not just the industry but Novus, and what you guys are up to. What’s on the horizon for you? What do you spend most your time thinking about these days that maybe some projects you’re working on, or perhaps even unfinished projects or ideas that you’re thinking about? Anything on the brain?
Stan: Oh yeah, for sure. You know, I’m very, very lucky in the sense that a seat where I get to talk to some of the smartest investors in the industry, and that’s just really, really lucky. It’s certainly no merit to myself, but it’s the way it’s worked out. I’m super thankful for it. So I speak with our manager clients, our investor clients, more so even, basically on a daily basis. They call me I pick up the phone and call them and pick their brains to what they’re seeing and how they’re thinking about the world.
So, you know, with that, you know, I have a couple of projects. First of all, you know, we’re a Fintech company, so I wanna continue to build great products. I think, you know, people think of us as more than just a vendor, as someone who’s thoughtful about the investment management process, you know. And I want to build technology that helps our investors take advantage of, you know, more and more data, take advantage of all the latest trends that are happening in AI, in machine learning.
I want to be able to turn data around faster for our investors. Right now it takes a couple of days to turn around a report that’s manually entered. I want it to take a couple of minutes. So machine learning and AI is something we’re working hard on to automate a lot of these processes. Very manual onerous laboursome processes of data management, data entry that started out this call with.
And on top of that I wanna build great technology. I want our investors to understand their risks better than ever so they can invest with confidence. I want our investors to understand their managers, you know, just as well as the managers understand themselves so they can keep those investment horizons longer, so that they don’t have to trade out the managers all the time. They can keep conviction higher so they can be more concentrated and take that risk with confidence. And for that, we’re building a host of portfolio analytics tools for our investors. Basically, analysis that says, “Hey, if you were invested in your top five managers, how would you have done? If you invested in this manager how would you have done?”
And basically, you know, these tools let investors experiment, and do cheap experiments and tweak their portfolios and run various scenarios, equal weight, not equal weight, conviction weight. You know, those are the kind of things that my investors have asked, or my clients have asked for, and that’s what I’m focused on right now. Certainly, the data aspect, making things more efficient for them, and the analysis on top of that, I’m really passionate about that. I think there’s a lot of opportunity. Not a lot of folks are building great technology for these large institutional investors. It’s all bifurcated. They have to use dozens of different systems. And I wanna help them, you know, make their lives easier and make their process better.
Meb: Last question we ask everyone. Looking back on your personal career, has there been a most memorable investment or a trade yourself? So for you, this could be a fund, it could be an individual security, it could be wonderful, it could be painful, it could be all the above, anything come to mind, most memorable?
Stan: For sure. I mean, you know, we have a no trading policy at Novus that when we put that in about seven or eight years ago when we had a very sensitive client. And I so happened by chance in my PA, I had a very large outsized position in Amazon and Google. And I thought, “Okay, am I gonna keep this, or I’m gonna trade out?” I literally sold everything else. I sold my oil bet, I sold… I was very long oil, I sold gold, I sold everything out of my PA. And the things that I kept are the things that I couldn’t trade, I couldn’t add to, or couldn’t take out. I just kept Amazon and Google. And just buy dumb luck, because I couldn’t sell them, it’s been great that worked out very, very well. So my active management skill was very poor but because I was forced just to hold them, I got very lucky.
Meb: Well, that’s a classic example, you know, is that so many people, they often…the media does these articles, they say, “Well, if you just bought Amazon at their IPO, you would be up how many hundreds of thousands of percent.” And so the problem is no one would have held that because it had these multiple 50, 80, 90, I think it had a 95% decline at one point. No one other than a psychopath wouldn’t have sold it. And so that’s probably the best investment strategy.
There was a guy in another podcast who talked about when he buys investments he just says he’s never going to sell them. And so that’s probably a much better behavioural approach than a lot of people get caught up with.
Stan: I can tell you quantitatively that’s very, very true Meb, even true of hedge fund managers.
Meb: Maybe we just start all bunch investment vehicles that are free but have big exit fees. I think that would be a good idea for people. Yeah, I know. So Stan, where do people find more information? They wanna follow you. They wanna follow Novus’ writings, everything else, where do they go?
Stan: Oh yeah, novus.com. N-O-V-U-S.com, everything you ever want and more, you can subscribe to the blog, we have a great blog, you know, something comes out every week. We’ve got a good research library of stuff, some good guest post, everything you ever wanna know, N-O-V-U-S.com. It’s right there. You can follow me personally. I’ve got a Twitter. It’s all linked on there.
Meb: And if you follow Stan on Twitter, you can get to see him hiding behind a pair of ski goggles. Where were you skiing? Do you remember?
Stan: Oh yeah, that was in Utah. I was lucky enough to ski with a client of ours, who are pretty good skiers. Let’s just leave it at that.
Meb: World class spot. I love it there.
Stan: Yeah, really great, really, really great.
Meb: What mountain? Do you remember?
Stan: You know, I frequent Snowbasin. It’s a bit out of the valley. It’s about an hour’s drive up but it’s worth it. It’s a place where they held the Olympics. It’s really, really great. A lot of locals ski there. So, you know, sometimes we’ll do the Park City and whatever with everybody else, but when I get the chance, I go to Snowbasin, great, great mountain.
Meb: Awesome. We’ll have to connect and do an off-site there. Stan, it’s been a blast. Thanks so much for taking the time today.
Stan: Absolutely. My pleasure, Meb. Thank you so much Meb and Jeff, I appreciate it.
Meb: Listeners, thanks for sitting in today. You can find the archives. We’ll put all sorts of show note links to mebfaber.com/podcast. You can always leave a review for us on iTunes and subscribe the show on any of the various players. My current favourite is Breaker and Overcast. Thanks for listening friends, and good investing.