In this episode I chat with Wayne Himelsein, president and chief investment officer at Logica Capital.  To our conversation Wayne brings over two decades of experience managing long/short portfolios, ranging from statistical arbitrage to factor long/shorts.

For as deep in the weeds as he liked to go as a quant, Wayne has a philosopher’s streak and Twitter is his soapbox.  Of course, 280 characters can be limiting, so I start out conversation by putting Wayne in the hot seat and ask him to explain the deeper meanings behind some of his recent tweets.

Using these philosophies as a foundation, we then dive into long/short portfolios.  We talk about the practical difficulties of managing these strategies and Wayne explains why he believes that beta-neutral is a fool’s pursuit.  

We then switch topics to tail risk hedging.  These sorts of strategies are notorious for their bleed, and we discuss whether the payoff is ultimately worth the cost of insurance.  Wayne describes a few ways in which the bleed can be managed and the ensuing tradeoffs with each method.  

In discussing both long/short and tail risk hedging strategies, I ask Wayne what due diligence questions he would ask if he were evaluating another manager.  I find this question always provides great insight into what managers of these strategies actually think is important.  Wayne does not disappoint.

I hope you enjoy my conversation with Wayne Himelsein.  


Corey Hoffstein  00:00

Okay, are you ready? Yes. All right. 321 And let’s rock and roll. Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

Narrator  00:23

Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations. He will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of 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

Corey Hoffstein  00:54

In this episode, I chat with Wayne Himmelstein President and Chief Investment Officer at logica capital. To our conversation Wayne brings over two decades of experience managing long short portfolios, ranging from statistical arbitrage to factor long shorts for as deep in the weeds as he likes to go as a quant. Wayne has a philosopher streak and Twitter is his soapbox. Of course, 280 characters can be limiting. So I started our conversation by putting Wayne in the hot seat and asked him to explain the deeper meanings behind some of his recent tweets. Using these philosophies as a foundation, we then dive into long short portfolios. We talked about the practical difficulties of managing these strategies, and Wayne explains why he believes that beta neutral is a fool’s pursuit. We then switch topics to tail risk hedging. These sorts of strategies are notorious for their bleed, and we discuss whether the payoff is ultimately worth the cost of insurance. Wayne describes a few ways in which bleed can be managed and the ensuing trade offs with each method. In discussing both long short and tail risk hedging strategies. I asked Wayne what due diligence questions he would ask if he were evaluating another manager? I find this question always provides great insight into what managers of these types of strategies actually think is important. Wayne does not disappoint. I hope you enjoy my conversation with Wayne Himmelstein. Wayne, welcome to the podcast. Thank you, Wayne, we are going to just go straight to the record of truth, which is Twitter. You’re a bit of a quant philosopher on Twitter, you say a lot of things. And it’s tough to interpret them all the time with just 280 characters. So I figured I’d start right off and go right to your tweets. Let’s play What does Wayne mean by this? So when we look at your profile, your top pinned tweet says quote in quant finance algorithms that develop from logic and experience that simply seek to mechanize what is already well understood, have a chance at success. Those that begin in data analysis, categorization quantification, or statistical or numerical gymnastics do not. What do you mean?

Wayne Himelsein  03:15

The big takeaway point from this is what many practitioners and theorists have talked about forever for years, which is beware of data mining or curve fitting? To talk about it more specifically to the words I used or to the idea I was trying to convey is that I’ve seen many quants, I’ll call it our beginning quants, especially who have a belief that the ability to use math and to sort through data and start if they pick up a couple of datasets, and they start categorizing. And looking in the data, they’ll find some interesting patterns or signals. From my long term experience, I found that most of the stuff you find it starting out that way, doesn’t pan out in the long term, the best approach is actually precisely the opposite, which is, you understand trading or you understand the criteria you’re looking at, you understand the way some aspect of the market works, whether it’s you’re a value professional, or a growth professional, or you have experienced with certain types of sectors, once you have an understanding of and a thesis, from your experience in that sector in that factor, or in trading, then you say, is there a way to systematize what I’m doing what I’m thinking? Is there some mathematical procedure or algorithm that can stick to the process that I have that I generally use for my long term experience doing this or that? That is definitely the only way in my opinion that quant can be successful is when you’ve taken something that has where you’ve made mistakes you’ve learned and you’ve experienced it, and you just figure out a mechanism to systematize it. And that is really just to be able to stick to a discipline or perhaps use better mathematics to make it more precise. That’s very different than starting the other way around.

Corey Hoffstein  04:58

How do you think about testing something like that, where you’re starting from a place where you already know there was success, there’s almost somewhat to a certain extent embedded survivorship bias, you know, these rules have been successful for you in the past, you’re starting from this basis of maybe discretionary trading, and you’re trying to sharpen the rules through some sort of quantitative systematic process, but to a certain extent, because you know, the rules were successful, the back test is necessarily going to look good. How do you think about hypothesis testing this,

Wayne Himelsein  05:27

right? So in a way, that’s almost curve fitting to your known understanding, like, oh, wow, it works? And yes, it does, because you know, it does. So that’s something that certainly to struggle with, on the side of the coin that I previously explained. The answer to that, in my view, and from my experience, is that the testing is to find really the best method of operating something that you know, works. Let’s go with an example, if you believe that trend following works, and I believe it does, it’s a good process, time series, momentum in the market has been validated time and time, again, by many studies, and have been shown to be successful over hundreds of years in markets, it makes sense, it has a lot of relationship to human behavior. So if that’s your thesis, you know, it works. The testing is, okay, well, under what timeframe? So you test one month, two months, 12 months, 18 months? And so the test in and of itself, is not to determine, does it? Or doesn’t it work? Because you have that understanding? The testing is defined? Is there an optimal timeframe? Is there an optimal stop loss amount? These are the things that help you optimize? What thinking is already valid. That’s where it can be helpful.

Corey Hoffstein  06:32

I we’re just gonna completely diverge from my intended path here. But you brought up the idea of optimal parameters. This is something that I do a lot of research on, how do you think about balancing the idea of finding those optimal parameters and doing this research, versus over optimizing for the parameters and almost creating an overly fragile system? Because you’re relying on that parameterization?

Wayne Himelsein  06:55

And I think you just said it best is if you overly optimize, it gets fragile. So how I lead the witness. Yeah, leading, Your Honor. So exactly, as you said, is you can’t over optimize. And the idea there is, when you’re trying to go through the optimization process and your testing periods, when you look at the data, and you look at the results, you have to look with not such a fine lens, that’s the way I’ll describe it, at least from my experiences. So if you test it, for example, and go with a prior example, I brought up which is momentum. And so times there’s momentum and say, one test two months, three months, four months, and you go all the way up to 20 months, or a year and a half, 18 months, in that process, you’re gonna see that perhaps just to use it as an example, it gets higher and higher and higher, nine months is better than 10 months is better than nine and 11 is better than 10. And 12 is better than then 11. And then it can, let’s say it peaks out around 1314 months, and then it starts to not be as good at 1516. Let’s just say that that’s the way it looked. So one could say, okay, 12.7 months is the peak. But that is too precise. That’s over optimizing what we then would look at that data and say, You know what, it’s somewhere between 11 and 15. And let that be your assumption. So the idea is that you use the data to hone in on the best area, but don’t use the optimal as the profound parameter of truth. That’s not going to be reliable over time.

Corey Hoffstein  08:18

Alright, let’s get back on track here back to the tweets putting you back in the hot seat. So I don’t have a specific tweet here. But this is something you have written about a number of times, you’ve claimed that sort of the one true asset class is volatility. What do you mean by that?

Wayne Himelsein  08:35

Well, let’s start out with having no assets, right? If you just have cash, then you have no volatility, you’re sitting in cash, and next week, your dollar is going to be $1. So as soon as you go out and buy something, whatever that thing is some asset, the moment you purchase it, you suddenly introduce yourself to variability. Therefore every asset is some degree of volatility. When you do that, at that moment of purchase, when you’re buying volatility, are you buying variability by getting into something? You’re unknowingly either long or short? It ie long or short volatility? So to that degree is if you’re whenever you buy something you should know, am I buying a long vowel position or a short vowel position? Does that fully answer your question?

Corey Hoffstein  09:15

Yeah, I think so. I guess to push it sounds a little bit more theoretical than necessarily, like literally the quantification of using options to buy volatility is the only asset class necessarily like a Delta hedge option is not the basis for all asset classes, or is that what you are saying

Wayne Himelsein  09:32

and option instrument itself is not necessarily Of course every asset there’s many types of assets, but every asset is in a way optionality. So if I go and buy something, which that’s not perceived as an option, let’s say a piece of real estate, nothing to do with liquid markets, when you buy it, you are buying optionality because you want this thing to appreciate and if there’s greater gentrification or urbanization in the neighborhood, then you’re long vol if you believe that, then you’re long vol because you’ve bought a stable piece of Real estate that you believe is waiting for a pop based on gentrification in other areas nearby. So you’ve bought a lot of opposition, in fact, but it’s paying you theta while you’re waiting. So it’s actually better than an option. True option, you can get your theta and gamma at the same time. So setting that aside is, in my view, any asset you buy somehow translates into an option structure. And with that option structure will either put you in a position of being long or short, a tail or vol. And knowing that you have to decide which one do I want. And generally, the higher return is going to be associated with a shorter of opposition and a lower return with a longer ball position

Corey Hoffstein  10:37

to your point that’s sort of embedded optionality in all asset classes, that sort of goes back to the original Merton model of equities are going to be basically a long option and when your fixed income is going to be selling optionality. So there’s certainly that tie with a lot of the traditional asset classes from a theoretical perspective. But I think this ties in to sort of the next tweet, I wanted to ask you about the sort of long haul or long the tail short details type concept, where you have described that investing as either inherently mean reversion or expansion, which I could see being you either want that tail exposure or don’t want that tail exposure. I have also heard you call this convergence or divergence. Can you explain this framework that you use to think about different investing strategies?

Wayne Himelsein  11:19

Sure, everything in the market is I’ll call it expansion or reversion. And or one could say, trend or contrarian, really, it’s the simple idea is that when somebody buys a stock, or a market or an asset, you either believe it’s dropping, and it’s either going to turn around, or the trend is going to continue. So you’re either buying continuance or you’re buying a turnaround, there’s no other reason one would buy, you’re not buying it for to sit still. Therefore, I mean, if you look at big for example, factor categories, buying value is buying, turnaround, buying, growth is buying continuance. That being the case is Whenever someone’s getting into position, just like payoff or the Option payoff structure that we were talking about before, you have to identify whether you’re long or short volatility. If you’re buying a reversion, you’re buying short volatility, if you’re buying expansion, you’re buying long volatility. So it’s the same thing as kind of categorizing your decision into either a vol bucket or an expansion reversion bucket. They’re all just different words for the same idea. To me, one has to know that and construct their portfolio accordingly and construct their risk management accordingly.

Corey Hoffstein  12:22

So a couple of days ago, you tweeted, mistakes are underrated. As I reflect back on decades in quant finance, and trading, the more I realized that the mistakes I made, were my greatest teacher. They bite you when they happen. But if you harness them over time, they can foster exceptional growth, study your mistakes. So Wayne, maybe you can give some examples of mistakes you’ve made and lessons you’ve learned.

Wayne Himelsein  12:48

Mistakes are underrated, there’s so many mistakes I’ve made. And there’s so many lessons I’ve learned. Number one will start out by saying my greatest growth and everything that I am today is a function of my mistakes. So I’m having mistakes are awesome. That’s my number one statement that said, let me answer you more specifically, I’ll go back to when I was more of a novice trader in the beginning years, I understood very quickly. So my early days as a trader, I pretty quickly understood the idea of stocks having gapped down risk, and I’ll call it left tails. I didn’t know if I thought about it in the distribution terms as specifically as I do today. I certainly didn’t. But just the idea that you dowel moves were much bigger than up moves, little ups and big downs are the customer in the market. So I when I initially bought portfolios early on my first portfolio, I had a long basket names, and I bought options on every name. And I thought, okay, that’s just what you have to do you need protection is the right way to manage a portfolio. And it turned out that buying options on every single name with God is just very expensive. You mitigate some of that gap down risk, but then you stop making money. And so then you don’t have a trade. And it’s actually funny how I learned that when my p&l went down, and I was okay, I’m not making as much money. This is not working. But in between I had this personal incident that occurred. That was really a funny story that I’ll tell and then it got me thinking about how insurance works. It was about 2003 I was still in New York, had decided to move back out to LA I have grown up in Southern California. So I was moved back out here and I got out here I lived in New York had been there for about eight years, or I started my career in finance and trading. And so I get back to LA around oh three and when I get here of course what’s the first thing you do when you move to LA is you want to buy a car. And so I got a car and with a car you need car insurance. And so I called up insurance company again insurance and and they asked me a question. Can you have your can we have rather your previous two years of driving history? And so having lived in New York and I didn’t have a car? Of course nobody does in the city for eight years. I said I don’t have that I lived in the city for in Manhattan. And so they came back to me saying oh, that’s gonna be 6000 a year. Okay, that’s ridiculous. Not possible. Let me call up someone else. So I, of course there were these GEICO ads on TV so I’m gonna let me call GEICO and called Geico. Don’t same question. Can we have your previous two years of driving history? And same answer didn’t have it lived in New York City didn’t drive. And they came back to me saying Even stranger, I was sorry, you’re not insurable. And so I call up an insurance agent and totally bewildered. It’s like, it’s been like two weeks, I’m in LA, I’ve got a car, I can’t drive and riding my bicycle around here being all frustrated that I can’t get into my beautiful new car. And I say to the agent, I just don’t understand how could it be like, I’m certainly not the first person to move from New York City or from a non driving area. This is ridiculous and 6000. And the other one says, I’m not insurable. So he says to me, Oh, don’t worry, I can find a company that doesn’t ask that question almost all the time is that I’m sure just do whatever you want to do and get back to me. So he gets back to me a few days later, I found that it was Mercury Insurance. And they don’t have that question. And he got a quote at 1400 a year great. I hit the bid, I’m done. And of course, happy to start driving again. It was a frustrating experience, because I’m weeks in LA and without a car not and this was before Uber, by the way. So let’s imagine that didn’t exist because it didn’t at the time. And so I’m sitting there, and I’m telling a buddy of mine about this. And I’m saying after the event, how could it be I’m the same risk. I’m the same driver, I’m the same guy. And one giant insurance company has made 6000 A year one can’t even price me and one’s at 1400 a year. It’s like hundreds of percents of dispersion on the exact same risk. And Alan, you know, what insurance is just weird. Like this all depends on the different assumptions and variables that go into it. And one question is one set of assumptions and then one other one doesn’t have that question. So it’s a different set of assumptions. Hence, a huge variance in the output and a huge model risk. And so as I started thinking about this, I’ll call it dispersion of modeling and insurance. It led me to an understanding the markets you know what that’s the same in options pricing is everybody’s using different models. And of course, Black Scholes is this backbone of, but most Vallabh traders aren’t actually using Black Scholes day to day they have some tweak on it, if you just it’s not a standard system, they have different assumptions they’re making. And with these assumptions, across the vol surface, the strikes up and down and across the calendar upwards and outwards, there are different prices for every option. And that’s when I realized, you know what, because of all this modeling, and people wanting, having demand for different options at different calendars, and different strikes, there’s gonna be cheaper and more expensive. And so instead of this book that I had, that just bought all the same month all the same, like 2%, out across this whole portfolio. Well, no, that’s not the right thing to do, I have to take advantage of the weirdness in pricing and model variants across the options surface. I mean, this was about six months of thinking and looking at the realization was, oh my gosh, insurance isn’t as clearly defined, or optionality isn’t as clearly defined, as one would think. And I learned to much better how to use options in protecting a portfolio, how to model how to look at what other people are perceiving as cheap or expensive, and get the best value for protecting a portfolio. So

Corey Hoffstein  17:52

I want to put some of these philosophies into a little bit more maybe a practical context, I know, your background is running long short portfolios, as well as running some tail risk hedging type portfolios, you’ve managed Long, short equity strategies for a long time. Can you maybe before we dive in to tying these philosophies to some practical examples, can you talk about maybe what your historical your prototypical long short portfolio is look like?

Wayne Himelsein  18:18

Yeah. So going back to this idea of expansion reversion, let’s say to use the two examples is reversion would be relative value. So a relative value portfolio is you’re buying, quote, unquote, cheaper, and you’re selling, quote, unquote, expensive, and you’re saying that A is cheaper than B or Coke is cheaper than Pepsi. And from a relative value basis, they should come closer together, because they’re in the same sector. It’s a reversion or contrarian trade, you’re expecting them to revert. And to that end, it’s a short vol trade, that’s a very common and stable way to put together a long, short portfolio. The other side of the coin is there’s the times where, because you’re short, Vol, you’re exposing yourself to risk. So the other prototypical portfolio is to do exactly the opposite, to take a expansionary approach, which would be long, stronger growth and short, weaker growth. Or if you’re a price behavior trader, your long momentum kind of strength in names, whether it’s relative strength, or using moving averages, you’re long some form of price strength, and your short price weakness, which is an expansionary, of course positioning. So for me, a prototypical portfolio should have both elements, because you never know when the market is going to present one or the other. And so the right thing to do is to have a bit of both in a portfolio and therefore have in a way to different thesis behind your total portfolio and it’s like two sub portfolios within an optimal portfolio.

Corey Hoffstein  19:42

It’s pretty rare that I actually get the opportunity to chat with someone on the podcast who’s done actual shorting, building a long short book. I don’t want to pass by the opportunity of chatting with you about some of the practical difficulties of managing a long short portfolio when you think about taking these ideas from theory. And the portfolio you want to manage and bring it down to the level of actually implementing it. What are some of the practical difficulties you face?

Wayne Himelsein  20:07

The first one is one we’ve touched on already quite a bit. And this is the biggest thing, which is the short vol component is that by its nature when you’re long and short, and most forms of arbitrage or relative value, whether price based or valuation based, has a left tail. So the biggest challenge I’ve experienced and I’ve seen across the industry is managing that left tail. And supposedly the beauty of long short and market neutral portfolios is the some consistency of payout. So you can depend on just say, 70 pips a month as a number, but then you come in one month, and you’re down 7%. That is the tail risk. And that is the Piper has to be paid. The pain that comes with consistently profiting on a short vol book is the other side of the trade, or the pain of the trade. And so to me, that’s the number one. The other thing which is a little bit more in the weeds, because I found to be a great struggle in the area of diversification. And what I mean by that is, typically quants tend to have larger portfolios, let’s say three 400 positions, it’s impossible to find a strong signal in that many positions. If you said, your best signal strength or your your favorite companies are perhaps a list of five or 10. So the problem is that the best signals are fewer. And as you get more and more, which you should do to become more of a quantity, you want to have more probabilities repeated more often, therefore you have more positions, but therefore you’re weakening your signal. So there’s this really interesting trade off between signal strength and diversification both being beneficial. And I’ve struggled with that idea for years and ended up in two different sides of it. I actually have two portfolios I run right now, which one which is highly concentrated conviction trades, because I need that super signal. And one is 400 names to under long to under short, because I’m taking more of the consistent play the odds betting

Corey Hoffstein  21:52

is that a function of the type of signal you’re looking at, or is that a function of the portfolio outcome you’re trying to generate,

Wayne Himelsein  21:58

it’s precisely a function of the signal. But the signal is based on the type of outcome you want. I mean, they’re highly intertwined. It depends whether you’re one who starts out engineering for an outcome, or just looking for a great signal to me, I kind of do both concurrently like I want a particular outcome. But then I know I’m familiar with certain signals. So the best way to apply a certain signal kind of associates with an outcome and you say, Well, how much do I want this outcome in my portfolio,

Corey Hoffstein  22:25

it sort of reminds me of the whole notion of information ratio is equal to sort of your information coefficient times the square root of breath, if you have to lower your information coefficient, but your breath goes way up, you can actually end up with a higher information ratio. But it’s all about finding that optimal balance, I guess, in what you’re trying to come up with. Exactly. So I want to go to your other point, sort of that inherent left tail that’s always lurking out there that doesn’t show up in day to day volume. And yet your walk in I know, we’re going to talk about tail risk hedging in a little bit. But are there things as you think about managing long short portfolios that you can do inherently within managing long short portfolios themselves to try to address that left tail risk,

Wayne Himelsein  23:05

I would say, to start with how you’re balancing it. So the number one tool or mechanism, or rather, best words measure people take is beta. So to me beta is maybe going against much of the world here, but is silly. I mean, it’s a nice measure, it tells you something, but break down beta for a second beta is effectively vol times correlation. Vol is asymmetric with the left tail. And correlation is has problems, you have no account for non linearity, you have the non stationarity, you have got a lot of issues. So you’re taking one problem variable and multiplying it times another problematic variable. And if you multiply two problems, you get a greater problem. So the focus on using something as simple as kind of it’s seemingly complex, but it’s not as beta is the starting point is don’t use that, judge the names by some other, find some other measure of behavior and find a balance within the portfolio, ie the long short exposure should bounce by something that is more stable, that can be understood to try to mitigate left tail,

Corey Hoffstein  24:14

because that’s something you’d be willing to open up about a little more, because I know, academically, often long shorts are constructed dollar neutral, just because that’s a nice, easy way to do it. In practice, you do hear a lot about sort of beta neutral or just sort of vol neutral. Are there any other sort of measures you’d be willing to open up about and talk about for these sort of ideas?

Wayne Himelsein  24:33

Yeah, I without getting too proprietary. I like forms of their mathematical tools that account for nonlinear behavior, forms of clustering, distance approaches, well call it geometric approaches, like as tools, also like the use of what’s called stochastic dominance, which is utilizing the actual distribution itself, understanding not to achieve Some expectancy but to understand the characteristic of some asset by the shape of the distribution, and then seeing which ones are stochastically dominant over others, and then shaping a portfolio according to what matches what those are some of the measures, I think, that are more viable than things like volatility, which volatility is a very summery metric. So there’s nothing standard about the errors of financial assets. Therefore, anything that stops standardizing is going to be at least a better tool.

Corey Hoffstein  25:31

Going back to this idea of engineering outcomes. One of the outcomes a lot of people I least want to express that they want with long shorts is that market neutrality? And I think hence the focus on beta, when you start to go towards these alternative measures, do you lose the ability to still engineer that outcome? Or is that outcome, even something worth engineering towards?

Wayne Himelsein  25:53

Let’s start with the word market neutral. If you want to make something market neutral, the outcome you want is neutrality. So nothing else matters, right? Therefore, by whatever means you can achieve neutrality, beta, being asymmetric is not going to be neutral, it’s going to be neutral the moment you put it on, and as soon as the market goes, actually, you see this a lot with equity market neutral portfolios, the further the market drops, the more they go down, the more beta goes non neutral. So it gets more extreme as things start going wrong. Therefore, engineering to beta was the error. Because the objective going back to what I first said, is neutrality. You start with that premise is how do I engineer something that will stay neutral? That very idea of neutral is a funny concept? Because I don’t know even if there’s such a thing as neutral if someone says they want to be neutral, right? My next question is, well, what do you want to be neutral to? Are you directionally neutral? Are you factor neutral, even have a directionally neutral portfolio that has equal long shorts, with a complete growth tilt, or a value tilt or some other factor tilt that volatility tilt? So the question first is, you want to be neutral, under what premise of neutrality, how neutral and the more neutralized you are, the less alpha is available? There’s a lot of competing ideas here. But I don’t know where you want to go further with this. But did that right? Well answer where you’re going?

Corey Hoffstein  27:09

Let’s stick on this idea of neutral for a moment. I mean, I guess neutral to what is the right question. And we can talk about maybe market neutrality or factor neutrality. But neutral does seem to imply some sort of negation of exposure, ideally. And it’s almost like you’re in a well defined box, you know, at least hopefully, what to expect, when you are talking about a long short portfolio that is theoretically market neutral, or beta neutral, or some sort of factor neutral exposure, when you’re managing that sort of portfolio? What should you be concerned about? What would keep you up at night as you’re trying to achieve that outcome?

Wayne Himelsein  27:46

I think if you first sought out to know, the exposure that you want to neutralize, so that’s where we start. So if your premise was I want to neutralize directionality, but I like taking a growth bet, then what should keep you up at night is that you’ve maintained the directional neutrality, and that your long growth? Because so I guess the easy answer is your premise should keep you up at night, am I achieving my premise? And if you are, it’s also before you go with that premise. If you’ve wanted the growth tilt, it’s that you understand what the exposures are associated with that growth tilt. So you’ve said to yourself, I know where I want to neutralize, and I know where I want to get my alpha. And if that’s where you get your alpha, you have to know that number one, you have alpha there. So if you look at your growth, tilt, and measure that against a fama French growth factor, do you beat it? If not, you’ve got no edge. So get rid of the tilt. And if you do, then you have to manage that edge. So I think going back to it is first understand your objective. And then what should keep you up at night is am I achieving my objective? A couple

Corey Hoffstein  28:49

of weeks ago, we actually got together for coffee. We’re talking about long short portfolios. And you mentioned something really interesting to me, which was this idea. I think we were talking about the quant quake, August 2007 quant quake and you mentioned this idea that volatility emerges because these pairs trading strategies diverge like there are concurrent coincidental events that you can’t just inherently manage volatility, necessarily to manage your positions because the positions go haywire, creates the volatility almost inherently. Do you remember that conversation?

Wayne Himelsein  29:23

I do? I remember the good cop. Can

Corey Hoffstein  29:25

you re walk us through that sort of logic that you were trying to explain to me?

Wayne Himelsein  29:31

Yeah, I think the summary word that comes to my mind is which I saw a lot in to me I believe that was a lot of the reasoning or the driver behind the August quant quake is overcrowding and overcrowding being that just so many people doing the same trades. So the idea that vowel emerges and really and it’s not just your portfolio is the point that I guess no man is an island. And when we trade in the markets, we trade with millions of other participants and we find that good pair trade Let’s call it Coke versus Pepsi, or whatever it may be, rest assured, many others have found it. And there’s just gobs of computing power, and PhDs and all the rest doing the same thing. And so we’re all going after the same edge. And therefore, when things start to go wrong, the differences between the different groups is that they manage their risk differently. And one of the best means of managing risk in these market neutral portfolios, or in large portfolios, in general, is liquidity management is leverage management. So the overcrowded risk is that everybody’s in this trade, and it’s a good trade. That’s why everybody’s in it. So you’ve done the right thing. But as some of these bigger shops start to unwind, it becomes a everything going the wrong way. And it’s wrong, I’ll be the first to say that the trade was good. But given how others aren’t needing to exit, because they have LPs to answer to, or they have risks that they’re managing to, to find stop losses, or whatever might be on their total book, those trades are gonna go the wrong way, as so long as you’re in it, you’re exposed to that, that becomes a very difficult thing to manage. And it’s difficult to manage, because at the get go, you made the right bet. I mean, I’ve experienced this quite a few times. And it’s it’s hard. I know, it was their second part of your question. That was, I know, you asked me about conversation, we talked, we went on and on about it. So why don’t you continue with? Yeah, you want it to

Corey Hoffstein  31:18

be I think where we went in the conversation? was the idea of is this just an inherent risk to the type of strategy or is this something that can be managed as you act in the market? As a long short manager? You know, you’re not alone. As you mentioned, you are in competition with other long short managers who are potentially going to crowd your trade, which can make position management difficult. Are there things you can do as a long short manager to try to inherently limit that risk?

Wayne Himelsein  31:49

Alright, to mitigate. So first off, I’ll say is, I’ve noticed that it is increasing over recent years, so I have a feeling that the increase in factor exposure ever since Fama, French, and the proliferation of ETFs associated with factors and massive shops to AQR to the world, with all these different factor portfolios, the overcrowded nervous around factor exposure has gotten worse and worse. I mean, when AQR unwinds everybody loses to a degree. 20 years ago, there was no AQR maybe there was I don’t know the exact history. But you know what I mean by that, in general, there was not as much exposure, certainly not to the factory side of things. Certainly not before fama French did their work. So with that proliferation of exposure in those brackets, it is getting harder and harder to manage. That said, to answer the second part of question, I do believe there’s always a means to managing it tight. In fact, I go back to one of the earlier things that we talked about, which is expansion reversion. So if one were had reversion portfolio on which let’s say it’s relative value, so you have value factor exposure, but concurrently, you have some mean expansion on which is growth factor, then you have two neutral portfolios, but with opposing factors, and therefore you’ve mitigated some of the crowdedness that could take place in one of the factor unwinds. However, you could come to the August quake, where both growth and value concurrently unwound. So then you need a third portfolio. Maybe you need portfolios ad infinitum, but not necessarily. The point is that there’s always potentially another thing to add that has, and this comes down to portfolio construction that has an offsetting outcome or an offsetting exposure, the typical and everyone knows his value and growth or momentum and value, right. But there’s more than that, perhaps to add some optionality. And I love optionality for this right? So when markets gets choppy, you want some long vol in the book, and that’s in fact, one of the best tools for offsetting exposure in a neutral portfolio, which has a short vol bias, typically in a reversionary portfolio is to have external long vol exposure. So you have some s&p optionality on the books and when one goes wrong, the other pops, that’s the name of the game.

Corey Hoffstein  34:01

So one of the cheeky questions you always get as a quant is why aren’t you out on some beach letting your computer make money for you? And of course, every quant knows it’s a constant process of evolution and research and search for improved efficiency and your strategies. How do you think about strategy evolution,

Wayne Himelsein  34:20

strategies have to evolve constantly, that’s for sure this misnomer of that a quant can build it and go to the beach is comical to say the least, the market is always changing. In fact, it’s funny even the idea of factors and categories, if you think of something like value and growth as these two big facets of the market, but even those are evolving into the very idea that you buy a value stock and turns around and starts moving in your favor. Well now it’s a growth stock. So literally the categories are changing on you. So if you bought a value book and you leave it for six months, you now own a growth book if you are right, that is on your picks. So everything’s always changing and and to me manage that exposure. If that’s intended, then you’ve done a great job. But if you intend always in just being invaluable, then you got to be shifting your portfolio and then what are value metrics, etc, etc. All of this is forever changing a quants job is as hard as any other job. And so rapidly evolving environment, especially with introduction of HFT, and AI, ml all these just so many more shops and so many more computers, computing different kinds of things, we have to always be on our toes.

Corey Hoffstein  35:28

As you look back on your career, have there been any strategies that you were in that stopped working on you?

Wayne Himelsein  35:34

That’s a funny thing. When you say stop working. In fact, you actually wrote a tweet post, which was a post he wrote about this idea of when will we know whether the value metric or price to book is broken? I think our great grandchildren may know, given law of large numbers, these things take forever to know whether something is quote unquote, broken. So I had literally this experience where in 1999, I ran stat, our portfolio as one of my first hedge fund, actually and ran it for many years. And it did very well returns in the high teens, low 20s. And, and then in 2002, it did 4%. And I’m like what’s wrong. And I, of course, I tried to tweak and I did this, and I did that. And 2003 was equally slow. I think it was like 6%. And by then I had lost all my institutional capital, and I couldn’t explain it. And I’m beating myself up and I closed up shop and I said, this thing’s broken quote, will no matter what I could test, I thought it was done. And then lo and behold, like 1011 years later, I thought to myself, I wonder how that thing would have done. So I took the model, and I bought some data and plugged it in and did a walk forward. And oh my gosh, it came back somewhere around oh seven. And it actually did fairly well in oh eight and did well thereafter. And I didn’t sit through it. I literally closed up shop and went on to something else because I believed it was broken. But it wasn’t it was just went in and out of a regime which its regimes were really wide. There were three four year in or out of favors. Similar could be said these days with trend following they’re not in a very wide out of favor wide timeframe regime. So we never know is the true answer. And it can be frustrating. But as a quant going back to the evolution is when because you don’t know if it’s broken, the only right thing to do is stop out and move on to find anything that does work. Because that regime that out of favor, GM might last six months or six years.

Corey Hoffstein  37:22

So you sort of lead right into the next question I want to ask, which was trying to ascertain whether something is broken, truly broken or just out of favor and what you do in the different situations. I guess if it’s broken, you walk away by when it’s out of favor, right? I think if you truly believe it’s out of favor, you would just stick with it. But then it’s a career risk issue, just blindly sticking with something. So I guess Question one is, how do you sort of identify if something’s truly broken versus out of favor? And then how do you think about handling those out of favor situations?

Wayne Himelsein  37:50

Yeah, I think that’s definitely a time frame answer what I mean by that? Well, it’s two things, it’s first a measure and then with that measure a timeframe. So the more you don’t know, the more your measure won’t determine whether something’s out of favor, the more time you might give it to try to fix it, to use a general word. So I spent a year and a half fixing, tweaking, studying data, categorizing all this stuff to try to figure out where the exposure what was happening, because I believed in what I was doing. Turned out I was right many years later. But I, that time, I was still wrong longer than I needed to be the order that I want it to be. The thing is, the first job is to measure and some things are clearly wrong and not going to work as soon as you measure them, and then you move on. So the stop loss is a function of your time, which is a function of your measurement. To give an example of something that where you would immediately know something that comes to my mind is there’s the strategy that came about, I don’t know, four or five years ago, which was a ETF arm, it was trading the ETFs that take leveraged positions, I guess they call them leveraged ETFs that double Long’s or double shorts or triple lungs triple shorts. And so there’s a group of people that realize that in to create a triple exposure, let’s say s&p Triple long, triple shorts, to create that exposure inside an ETF one needed to use options. So there’s embedded theta or decay or bleed inside that. So, a very simple market neutral approach was to short the triple long and short the triple short in that sense, one is both long and short the market equally you are neutral to the market, but you get to acquire or your alpha source quote unquote, is this the carry is the bleed from the theta embedded inside the ETF. So you’re you’re making money on time decay, while you are neutral, the market. What a fantastic arbitrage. You’re wholly neutral, you’re only in literally the market. There’s no idiosyncratic exposure, and you’re just basically collecting theta. Wonderful, except more and more people start to realize this overcrowding ensued. And what happened is prime broker saw that everybody wants to borrow these triples, and so they increased the short borrow rate on the triples. In fact, to such an extent that the cost of the borrow was greater And then the amount you made on the decay. And that’s the point it got to us. If I earn 8%, on the decay, the borrow rate was nine. And so there was no ARB left literally in measuring it. It was gone. Not only was it broken, it was an impossible. So all we needed to do is in understanding what they were doing. Look at nine is greater than eight, this thing’s broken, and it’s done. And a day later you move on. Because it’s history. It’s been over traded, it’s been found, and I’m out. When there’s something therefore that’s measurable, you can cut your losses much quicker and move on to something new. When it’s not measurable. It’s when it’s difficult. And that comes down to a personal decision. How much time am I willing to spend tweaking and contorting to try to figure out whether I can fix it. And we all have our limits? And at some point, I guess, if you decide to you cut your losses and move on and do something else,

Corey Hoffstein  40:48

I guess that comes down to a business question as well. Exactly. It’s not just tweaking and contorting and trying to fix it. But how much time can you spend defending it? How sticky is your capital? Is it tight? Exactly. flighty? Yeah, and what does that mean for your ability to even if it does come back, still be in business?

Wayne Himelsein  41:05

What is that? Exactly? Exactly.

Corey Hoffstein  41:07

So one of the questions I love to ask people who are really have a long practical history of managing a particular type of portfolio is how they would do due diligence on that portfolio. So my guess is, a lot of the listeners to this podcast aren’t going to go out and start running a long, short portfolio, but they might be evaluating long short portfolios, either in mutual funds or LP structure. If you’re sitting on the other side of the table, asking the questions about someone’s Long, short portfolio, what are the types of questions you’re asking? And what are the red flags that you’re looking for?

Wayne Himelsein  41:40

I’ve been in that position. So there’s so many answers to it, because I will ask a lot of questions. I can’t think of them all this minute. But let’s start at the very top. First off is every portfolio or many funds only provide monthly numbers. So at the very, very top of the list is get more granular with your timeframes. And look at this thing behave daily. And monthly is just ridiculous. And I when I say ridiculous, people say oh, a five year track record is long, but five years monthly is 60 data points. That’s absolutely tiny. And it’s masking slash hiding a lot of behavior intra month. So you want to know how this thing behaves? That’s the top level ideas. What What can I expect? So number one, you get daily returns, and you map this thing. And when I say map, I don’t mean necessarily regress. Because a regression has some linearity in order not only area issues, but you map this thing you use certain, I guess, mathematical tools to map this thing next to different exposures and see what is this thing exposed to, I don’t ever listen to what they tell me. I just run it versus we have in here about 180 different exposures that we have time series for factors or exposures. It could be things like volatility or oil and just go down the list. we’ve aggregated about 180 of them. And so we’ll take any portfolio and map them next to all 180 and say, what is inside this thing? And what’s interesting is we most often find or very often find that the things that managers will tell you that they’re doing, sometimes they don’t even know that they’re exposed to other stuff. Like really, did you know that you had a 30% Exposure to momentum? Oh, no, I didn’t. I’m actually a value investor. And so I think number one is get granular and with that granularity, identify where the exposures are, once you’ve identified the exposures, you start asking questions to understand how intentional that was, and how much the manager is aware of the exposures they have and what it is that they’re actually doing. And the ones that, of course, are particularly responding and very clear about everything that’s inside it would it has the same findings as yourself, then you know, that they’re on top of it, and those that are open up their eyes wide and say, Oh, really? I’m What do you mean, I’m in momentum? Well, that’s somebody should be concerned with because they don’t know the risks they’re taking.

Corey Hoffstein  43:56

Are there any immediate red flags that stick out to you when you talk to someone who’s running a long short book that you say, this person isn’t aware of this type of risk? Or they haven’t thought something through even without looking at the numbers just from maybe reading someone’s pitch book or the way they explain a portfolio?

Wayne Himelsein  44:13

Yeah, I think to me, the big thing is that the really generic sounding stuff, we take a value approach and the pages, the fama French, and then they have the we like good quality companies. I really don’t we all so sarcastically, I shouldn’t put it that way. But somebody has to have some prime minutes outside of what you can buy in an ETF. I put it as simple as that. And they have to have a not just a sound thesis, but something that is that steps outside of the easily Bible box. I think when I see PowerPoints and so much of them look so similar. They’re just kind of telling me all this stuff that is so standard, and doesn’t give me any insight into what their edge is in that area. There’s one manager I met years ago and this is an example of the opposite. He had worked at at Fidelity for 15 years or so, there was a specialist in the banking sector. And he just knew things about bank balance sheets and how to look at them that most investors don’t, because that was his area, just the depth of expertise in that area. That’s not my focus. I wasn’t as familiar. I didn’t have particularly good questions to ask because I didn’t know that side of the business. But he went on talking for an hour about so much detail about the specifics about metrics for banks that were different to any other company. I knew he had a deep understanding of what he was doing. Whether he was right or wrong is a different question. And he’s returned show how good he is at it. But the fact that he had such good reasoned and depth, the explanations, and then you see it in the returns, two and two together is okay, this guy not only knows what he’s doing, he’s, it’s panning out in real time. So I like when those two things come together. Was this a discretionary manager that you’re talking about? He was a little bit more quantity, a little more quantity? Yeah, yeah.

Corey Hoffstein  45:54

What do you think about the role of discretionary versus quant in long short, is this a realm where quant has a greater edge than discretionary? Are there certain types of long short portfolios that discretionary might have an edge over quant?

Wayne Himelsein  46:07

I can’t think of an example where one is more optimal than the other with respect to a specific type of portfolio market. Say for example, if you have a large portfolio a 200 by 200, market neutral, that’s impossible to do discretionarily. So some things are just on the side of obvious, but I don’t think either one is more suited to a particular type of investing. It’s more that they’re just different beasts where I personally love Quan. The reasons I love quant are the same reasons that many people do is that things can be characterized and understood and can be a process can be repeated consistently. So I think the only thing that with discretionary approaches is you can have the exact same thesis as a quant you could say, Okay, we’re gonna buy a talked about bank stocks a few minutes ago. So we’re gonna value banks under this special metric, and we’re gonna buy all those that are cheap, and sell all those that are expensive under these kind of deeper metrics that we understand about banks, fine. So if that’s your approach, the quant manager is going to bucket the names. And they’re going to put a certain amount to each one based on some matching or some algorithm that can identify the risk, whether through vol or some clustering mechanism. And so they’re going to have these balance pairs, the discretion, the manager is going to say, Well, I’m gonna get I like this one a bit more. So I’m gonna take a bit more of that one. That’s neat. But I don’t know how you can show consistency over time, when you’re making these decisions that don’t repeat, I guess, the judgment side of it at the same time? What if that discretionary manager has better numbers, then you say, well, they’re making those gut decisions. But that’s the performance I want. So I don’t know, that’s a very hard thing. And I don’t know that there’s a right or wrong, I think I know what my answer is, now that I’ve sit here giving you a whole different answer. I know exactly what the answer is, is with investing, you have to go with what is you? I think everybody, every person is different. Of course, every person is different. It’s not what I think it’s what I know. So the most right thing to do is to look within yourself and ask Who am i What do I like and go with what you feel good about. And you see this all the time it where doctors love medical stocks and drug stocks, it’s an area they know. So likewise, if you’re a quantity kind of person, then lean to quantity. If not, you know you don’t, and there’s good and bad and all of it. So the best you can do for yourself by going with what you know, is because you’ll be able to ask better questions and be more comfortable with what’s happening day to day,

Corey Hoffstein  48:24

reluctant to move on here. Because I feel like we’ve just started scratching the surface of your knowledge along shorts. But there’s this whole other category that I know you have a large degree of expertise in, which is tail risk hedging, which I want to make sure we talked about. So reluctantly, I’m going to switch topics here a little bit to this idea of tail risk hedging. So I have this really sort of pithy catchphrase, which is no pain, no premium, which I think is a lot of what we were talking about earlier. And it basically means that without the potential for losses, we really shouldn’t expect to earn a risk premium. So practically, this tends to translate to most traditional assets have negative skew. They have fat left tails, you wrote this great paper called The illusion of skill, which I think I’ll make sure we link to in the show notes, but everyone should read, particularly those who like a little bit of math. And in it, you demonstrate that skew and kurtosis really go hand in hand. With this idea of tail risk hedging in mind, how does one go about trying to protect themselves from big negative outliers?

Wayne Himelsein  49:29

Yeah, so of course, skew, and kurtosis, and even higher moments all go hand in hand, because it’s all just one distribution. So it’s one characteristic and every different type of asset classes have different type of characteristic shapes. So that’s for sure, as I think first one should obviously understand what they’re exposing themselves to. It’s very much the conversation we were having earlier. Is this more of a ride secure or left skew exposure, and most things, as you rightly said, are negative skew. All Stocks are typically negative skew. And so The question becomes how do you mitigate that exposure is very, very difficult. The number one way I’ve found the only right way, I’ll say is to buy the only thing that is extremely right skew, which are options. So if you’re going to be in a portfolio of stocks, if it’s a diversified enough portfolio, you have optionality on for example, the s&p, or if you’re in a small cap basket on the Russell, or if you’re in five names you can make by optionality on those individual names, the point being is, the only way to get rid of the left tail is to balance it with the right tail. And to have that obviously, you have to have the right offset temporarily, you need the time associated to match that when this thing goes down, the other thing goes up. So you need to understand that the time relationship between the two. And that’s very simple. If you own a basket of stocks, if they fall, the s&p goes down, that’s pretty clear. If you have enough of them once you can come to those understandings. If you own optionality on the other side, you own right skew to protect your left skew, I guess you could synthetically cut left tail with things like stop losses. So that’s what actually trend followers do. So stop loss management is a good synthetic, left tail mitigator. But then you have gaps, you can get through a stop loss quite easily. And if you’re not a quant discretionary, you could say well, let me let it go a little bit further. That’s one of the problems with discretionary management is not obeying those exact lines, or exact levels like a quant or a system would. So stop loss management, I’ll call as a synthetic, left tail. mitigator is good but not 100% reliable because of number one gaps and number two discipline. So therefore the best means which you don’t even have to think about is to have a right tail offset, which is an option.

Corey Hoffstein  51:38

I don’t want to put words in your mouth. But it sounds like what you’re saying is instead of trying to think about getting rid of the left tail, you’re talking about having an asset that will exhibit right tail behavior at the same time as that left tail is exhibited

Wayne Himelsein  51:49

exactly counterbalance. So instead of trying to get rid of it’s that you can’t get rid of.

Corey Hoffstein  51:54

So I think financial theory would say like if an asset exhibits a right tail probably has to have a negative expected return sort of this. It’s almost like an insurance payout. And I think that’s the argument frequently against something like options where you’re trying to engineer this right tail payout is that the insurance is just expensive, potentially even overly expensive. Roni Israel of I think, wrote a paper called the pathetic protection, it was something to the effect of pathetic protection, the elusive benefits of puts or something like that, where he showed that most investors would actually just be better off from a cost perspective, just reducing their Beta, rather than buying puts, because there’s all these problems associated with puts they’re, they’re expensive. And then you have these rolling timing issues that go along with them. From your perspective, is protection, ultimately worth the cost when it comes to things like

Wayne Himelsein  52:47

puts? Yeah, it’s funny, this idea of expensive if I think to myself, most of the expensive things I can think of are really worth it. I mean, drive a Porsche or drive a, I don’t know, Toyota and expensive is feels, it handles the road better, I guess you get what you pay for is part of my claim I’m making. So when it comes to portfolios, is that also true? I mean, I think for the ordinary investor, where it’s very difficult to learn to properly trade options, and how to manage optionality and rolling and all the Greeks and options modeling, it’s an uphill battle. I think, probably, if I were talking to kind of an average investor, I would say, just lower the leverage or the beta in your portfolio. Yes, I agree with that answer. I agree with that pair hadn’t heard of that paper, I haven’t read it. But I agree with that reasoning, at the same time is one could learn a little bit and be able to engineer outcomes or could buy a tail fund or buy that exposure from a professional who does that, whether in a fund or in a managed arena. But if one were to do it themselves, I think the road to learning it if they have interest is not that difficult. I mean, if you’re a doctor or lawyer, you’re just a busy family person, you don’t have time, that’s fine as you go the other route. But if you have the interest in the passion, it’s not impossible to get from zero to 80%, you might not get to 99% proficiency, which really only comes if you do it every day, all day for years and years and years. But you can get a pretty deep understanding of what to do. And there as far as that what to do. It’s and managing the cost of insurances. It is worth it. The point you made earlier is whether right tail always comes with a negative payoff. There’s always a trade off. So the better the right tail, the lower the hit rate, and the worse the left tail, the higher the hit rate. So what do you want? Do you want hit rate? Or do you want the risk? And I think if you are willing to accept a lower hit rate, for some right tail, you can get there but without much of a loss. And let me use a different example to put it into day to day terms. Let’s say the insurance that we all buy without even thinking like we don’t say to ourselves, is it worth it to have health insurance? Is it worth it to have car insurance we all just go out and buy it because you don’t get in your car without it and you Don’t wake up and cross the street without health insurance. So there there’s not even a question. But when we’re buying it, we make decisions like what’s our deductible? And what’s our total coverage. And so there’s these are called knobs to turn, which are the same knobs that one could learn and turn on portfolio optionality. So for example, you could buy just 10% out of the money or 20% of the money options on the s&p, you spend a little bit of money, just like health insurance premiums, you have a portfolio, let’s say it’s a $2 million portfolio, and you’re spending $2,000 a month on this, you know, and say, Okay, that’s my cost of insurance. And when 2008 comes, you make a fortune and covers half your loss. And was that worth it? Well, if you did better and slept better, and all your friends were crying, and you were up having a nice Scotch one night, when everybody’s suffering, then you did better than you live through it. So it’s part of it is the emotional experience and living through it. Part of it is how do you want to feel day to day, going back to the point is, you could learn a little bit, you can turn some knobs, and you can have some insurance in place that protects you, or, of course, go with a professional.

Corey Hoffstein  56:04

So part of this idea of whether options are expensive or not, is really comes down to whether you’re getting what you pay for, which is what you mentioned, Nassim Taleb argues and has argued many times that really far out of the money puts are actually what people should be buying. Because as humans, we just have a really hard time fundamentally understanding super low probability events. So those way out of the money put options are consistently under priced because those rare tail events happen more frequently than they’re priced to happen with the options. Do you take the same view,

Wayne Himelsein  56:38

not really, I take the same view that people don’t fully understand highly rare events, that is true and don’t account for how much more frequently they occur in capital markets than in real life. And so kind of the quote unquote, infinite variants associated with fat tails. So that part of it what type says wholly agree, and I’m the biggest practitioner and believer in all of that, the difference is that I do not agree necessarily that buying further out is better and cheaper. It is well known, what’s called the volatility smile, which is that the SKU picks up as you go out of the money, an option that is 20% out costs 10 cents, whereas at the money cost, let’s say $3, on the s&p as an example, people might look at that and say, well, 10 cents is dramatically less than $3. Sure, in nominal dollar terms, that’s much less money. But the 10 cents, if that only happens once every 30 years, then you are losing so many 10 senses versus the $3. at the money option, the expectancy is so much higher, I mean, the market could be down five out of 12 months a year. And so you’re getting paid so frequently that it’s paying for itself. But those will still have great convexity when the market really cracks. So in my view, the lower deductible is actually cheaper because you’re making money more frequently. So it’s about cheaper, expensive, is expectancy based. And yes, if you buy 30% of the money to Tellabs point, that will happen more often than you think it might and you don’t understand that. So you might want to buy a few of those. But if you really want to protect all the time you buy at the money, and what’s nice is about doing that is that it’s more expensive. But there’s so many events that occur from being at the money, I mean, month of May was a great correction from at the money, the magnitude of about 6%. So the may pay off, might have fully covered your bleed in January through April. So every four or five months, you get your money back. That’s a much easier thing to handle than waiting 22 years for a payoff, which by that time your kids have taken over your portfolio and you don’t even remember the losses you’ve had so much bleeding along the way. So anyway, that’s the point. So I think you know, obviously there’s truth tell he was a very smart man and I agree with love most of everything he says, but in this case I diverge with him on where one should buy optionality. We talked

Corey Hoffstein  59:01

about options and optionality. Are there other opportunities that you like for protection?

Wayne Himelsein  59:09

Yeah, there are some other assets that offer kind of protective behavior against equity markets, typically flight to quality assets, things like treasuries or gold. Oftentimes, the dollar against a other major market currencies, like a European basket of currencies have good characteristics when equity markets are drawing down. And just in fact, recently, just in the month of May, the s&p was down 6% gold and Treasury soared. Those are good assets to own. There’s been gold bugs out there for many years. And of course, nobody could ever doubt the reliability of treasuries over time to be flight to quality that is effectively what they are is, is there the benchmark so these things are good to own and they offer I’ll call it put like characteristics, there is some convexity in their behavior when equity markets fall and they have very little bleed. So these are are ideal instruments for less educated investors to have in their portfolio as a percentage of their portfolio that will behave a little bit like puts and give them the comfort during those heavier drawdown times. The other kind of less known things are certainty, we were talking earlier about market neutral portfolios. I in fact, engineered a market neutral portfolio just to have that type of exposure, where I’ll call it defensive market neutral. And so most of the time, it doesn’t actually have much return, doesn’t really lose it. And I, let’s say makes one or 2% a year. But in q4 of 18, it’s up seven 8%. It’s a right skew market neutral portfolio, that was a highly engineered portfolio with a specific outcome of being acting like a put, but not really costing money. So how do you do that you are typically long more quality names, I think of a portfolio that’s long utilities or Procter and Gamble, and you’re short, some high fliers, it’s hard to keep that making money most of the time or keep it even flat. But you can if you know what you’re doing, and and then that has, of course, quality exposure. So in equity markets fall, people run away from biotechs. And into utilities. And so that thing pops. So yes, there are many other ways to get exposure to assets that counter behave equity markets, and that even have some convexity to them or some option like shape, that are much cheaper, quote, unquote, than options.

Corey Hoffstein  1:01:21

So you brought up May, which I think is a really good example of sometimes the things you expect don’t always go the way you expect them to go. We had a pretty I mean, I wouldn’t say overly dramatic sell off, but a pretty good sell off in May, and implied volatility, barely budged. For the s&p, it was sleeping, there was sleeping, and yet you saw flight to safety in some of these other assets like treasuries? How much of protection is sort of a moving target with some of this stuff? If you can’t inherently just rely on one type of protection for a given sell off? I mean, how much of it is sort of situation specific? How much do you have to sort of be changing your protection over time? Or is it the idea of maybe buying a more diversified basket of defensive type positions?

Wayne Himelsein  1:02:07

The answer is, there’s absolutely no way to time. What type of protection one needs at one moment, that’s, that says, probably harder than timing the market, which is impossible. So like a derivative of time in the market, it’s timing the risk of the market. I mean, it’s timing a crash, it’s in the realm of impossible. So it’s not just a crash or a crack. But what way this particular crack will play out. That’s what you’re asking. So it’s like a derivative of a derivative, we get into the realm of don’t even try. And that’s where I would start it’s uncertainty of uncertainty. That being the case, the most robust and only right answer to that is to have a little bit of each all the time, you don’t know when it’s coming. So in a portfolio, like we just talked about, you have some gold, you have some treasuries, and you have some out of the money, put options. Let’s say on the s&p, you have these things, and they’re always there. And in a month, like May your put options didn’t do as much as you wanted. But your gold and treasuries soared another time in 2011, and the correction or August of 15, or Jan of 16, then the put options plus gold did but Treasury didn’t or whatever, one or two of three of three are gonna make it or not. But you always have these things there. It’s like saying, I guess back to the larger insurance analogy, you have your medical and you have your dental and you have your vision. And so I don’t know where I’m going to get hurt. But either way it’s covered. And when asked, Well, should I have insurance for the emergency room? Well, yeah, that’s one out of every X events are going to put you in the emergency room, we’re not just calling up a doctor and during a regular appointment. So the answer is we don’t know how they unfold, we have to have all of them and be positioned, and it’s just up to a person to decide how much exposure to put in those buckets, with optionality being potentially the heaviest, quote unquote, cost. Again, to me, it’s not expensive when you get what you want. But since it is more often a bleed than a payoff, is perhaps people should have more treasures and gold and a little bit less optionality, but definitely all concurrently.

Corey Hoffstein  1:04:03

So let’s stick with that word bleed, because I know it’s very traditional for tail risk hedge funds to have a very consistent bleed. And I know that you offer a tail risk hedging strategy that we’ve talked about in the past that you think does not bleed as much, if really at all compared to traditional tail risk hedging strategies. What do you do differently? How do you think about this problem differently that allows you to avoid some of the bleed, yet still, hopefully provide the protection that investors are looking for.

Wayne Himelsein  1:04:32

There’s always a trade off when you’re trying to own optionality without bleed per Black Scholes. Gamma and theta are the convex payoff versus cost of carry are on opposite sides of the equal sign and one goes up the other goes down and that’s it. Positive and negative. So you can’t have one without the other is literally the math and you fight the math as me as a super quant I’d say that’s how dare you right? But I will. I’m going to how do we fight it is a Our approach is to, and I’ll start with saying that there’s always a trade off. So in generally, the way vol managers have managed that trade off is to say that they want to be long, some vol. and to pay for that they’re gonna get short some other vol. Generally short, some idiosyncratic vol. So the idea might be your long, the s&p Vol, or your short some specific names that you think are about or should come in or so you have long and short vol exposure, and the short ones are paying for the long one, which you think is the one you really need. Or you have, for example, a put spread, where you’re long at one level and short at a higher level or a lower level, depending on the size of your spread. But the problems with those is, if the confounding of when you’re wrong, you’re short, the thing you want to be long. So it’s like anti thesis, I want to do what I don’t want to do, but I’m doing it. So give the example of a put spread and your spread is to be your short from at the money, but then you’re going along from 10% out and down the market, correct 7%, you lose. So you’re in options, but you lose. So that’s exactly the opposite of where you’re intending to do. So I don’t like those approaches. But that’s a very standard approach for paying for your bleed, the way we approached, it was a little bit different, which is where we’re going to take the trade off. So I take the trade off and sometimes having less than you need. What we do is we trade it if one thought to themselves that they could trade something right. So there’s a lot of traders out there. People trade everything you trade, people trade s&p futures, they trade tech stocks. So in general, imagine somebody is a good trader, you talk to your buddies, a good trader, he trades the tech stock. So if someone is a good trader, well, you could generally learn and trade anything, at least a good basket of things. And the more you trade them, the better you get at trading them. That’s how experience grows. And that’s the nature of our learning process. So with that ideas, my trading background, I thought to myself, You know what, if there’s Sunday I’m going to trade. Well, let me just trade long options and see how tradable they are. And it’s no different than trading s&p or trading google it just constraining your universe of instruments to only long puts. Okay, so buying and selling long puts, sometimes you have more, sometimes you have less, you’re never short, you’re always long, but you’re getting in and out of more or less per day, depending on your trading procedures or your in this case, it’s an algorithm. So it’s a it’s a systematic process of trading. By doing that, you’re exchanging your trade off from having this unwanted short vol. Two, just having too few puts at any one time. So if you can imagine this scenario, let’s say your ideal position is 100. Long puts that should cover your portfolio. And let’s say it’s 100, long from 5% out of the money, that’s what you want to protect. So you have a little bit of a deductible. And so you have 5% on the money. In our case, we actually start from at the money because we want to be fully protected from the first moment downward. But let’s say 100 Long is the right number against the beta of your portfolio. So but today, we think that they’re a little expensive, they’re overpriced. So we’re going to sell we’re going to take 50 off the table as a trader as a swing trader. So you take 50, and you go home overnight with 50 puts and, and then they’re cheaper the next day. So you buy, not 50 back, but you buy 100. So you go home overnight, with 150, you’re actually more pointed than you need to be, but they were so cheap, you wanted to buy more you come in the next day, and they popped up really nicely, even more than you expected. So of your 150, you sell 120, you’re only 30 left, if you go home that night, and that’s the night that event happens. Trump does something silly. I mean, that’s more often it happens almost every night. But let’s just say it’s more. So tonight, you come in with 30 puts the next day, and you have less than 100 You need but you still have 30 to 30% covered versus what you want it to be, you’re under covered, but you’re still somewhat covered. The point is that making that exchange for something, and sometimes in fact, you’re in more than you need it to be. Because you by trading, you ended up buying more. So what we learned and built a system around is the trading of them allows you to effectively scalp as a trader would as a swing trader would. But you’re scalping something that you always want to own, which is protection. Sometimes you could be overexposed to protection, sometimes under exposure, you don’t know when the events coming. So theoretically about 50% of the time, you’re gonna be right around where you need to be 25% of the time you’re underexposed and 25% time you’re overexposed, you had double the amount of puts you need it and you’re dancing when the market crashes. So that 25% underexposed is your trade off. And I’d rather have that trade off than being short vol.

Corey Hoffstein  1:09:27

So let’s put you on the other side of the table again, like we did with the long shorts. There’s all sorts of tail risk hedging products out there now, obviously in LP structure, but many even in mutual fund and ETF structure. What should people look for when they’re evaluating these types of portfolios?

Wayne Himelsein  1:09:45

So earlier I talked about I’ll use the word trade off that there’s always a trade off. The problem with insurance is that it costs we’ll use the word bleed before. So that being the case, everyone who manages tail risk is trying to find some way to manage To the bleed to pay for the insurance, therefore, something in their portfolio is doing that when looking into a tail risk strategy. If they say, oh, it just protects the tail. And the next question is, well, where’s the exposure? It has to be somewhere. If you’re protecting the tail and you don’t look like the bleed of an option, then you are making a trade off. There is no other way to achieve that, as I talked about before, is most of those trade offs are finding other areas of vol to short so vol managers know vol. And so they’re by knowing vol they they live in the vol corridor. So they’re gonna say well, this is expensive vol. And this is cheap, Vol. And they expensive all is going to be short, it is going to pay for the cheap vol they’re buying, and where they’re finding some idiosyncratic vol names, specific vol moves that they can short to pay for their index optionality or on the index, they’re short at the money, which is bringing in more dollars to pay for some out of the money. So if you lose only five or 6% of the s&p, you’re actually losing money with them. But if it is a big crack you’re making or there’s calendar spreads, where you’re you’re shorting front month to have longer term protection. The point being is that there is always a trade off, you cannot own options and not pay for them. Just like you said earlier. It’s the no pain, no gain, or I think that was your term, I’ll call it no theta, no gamma. So that being the case is one has to find out where that pain is, and that pain has to be acceptable to you. So if you say to yourself, you know what, I’m fine with deductibles, I’m fine losing five to 10%. Anytime I just don’t want to lose a dime more than 10%, then you’re fine with put spreads, you accept that risk. But when you’re diligence in a portfolio, when your diligence is gonna tell risk manager when you’re trying to find one for yourself, dig in to find out where that exposure is. And then ask yourself Is that something I’m comfortable to, in the example I gave you with my tail risk strategy is sometimes not having enough protection on the books. So it’s a more or less function, it’s not a upside down opposite, ie shortfall function, therefore use decide is that a risk I’m willing to take, sometimes it’ll be there for me, but other times, it might not be there when I want it as much. And these are all the again, to use the word trade offs that we make. So the number one thing to do is either ask the manager or looking at their portfolio, if you understand well enough, what positions are doing to find out where the exposure is actually do both look in the portfolio and ask the manager, where’s the exposure? Make sure those two things agree, of course, at least if you can have the knowledge to figure that out? And then once you understand that risk, contemplate whether that’s the acceptable risk, because there is to use both of our points, no theta no gamma.

Corey Hoffstein  1:12:33

All right, Wayne, last question for you here. And this is the last question of the season that I’m asking everyone who comes on the podcast. And the question is this. Let’s say I said to you that you had to sell every investable asset, you have your 100% in cash. And you can only invest in one thing for the rest of your life. It can be an asset, it could be as a given strategy, a portfolio configuration, but once you set it, you’re done. You can never touch it again. What would it be and why

Wayne Himelsein  1:13:02

spy, and by the market, it would be spy for sure why? The number one benchmark for everyone is the market meaning every promotional material, every advertisement, every manager you talk to is, oh, we beat the market, we’re trying to beat the market, we’re trying to beat the market. That’s what everyone is trying to do. And yet when you look at the statistics, something like 92% of managers and mutual funds over the last 20 years have not beaten the s&p. So and there’s a mathematical reason why actually, right is that because every manager has is doing trading. So you have slippage cost of commissions, you have taxes, and you have the fact that you’re selecting out of this basket. So by definition, if the s&p 500 names, if one manager selects 100 of those names, and another manager selects 100, then one of them is going to be more right and one more wrong, because one is above average, one is below average, because together, they’re the average. So now you’re selecting which one is going to be below which one is going to be up. So you have a new selection problem of which manager all the hurdles against you when you’re doing anything, it translates into this data that we see that very few people can beat the market. So why all this fuss just buy the market? The only reason people don’t, in my view. And I guess my opinion, my experience is that you want to control volatility. So we build these great portfolios, which are not great, because they’re necessarily beating the market every year. They’re great because they’re doing similar in the market and much lower vol. That’s what you’re buying is your buying consistency. So if eight 9% A year is the s&p to be able to achieve eight, nine a year we can’t do this. But let’s say every single year for the last 25 years with that level of stability means that whenever you need your money, it’s there at that level. That’s the ultimate achievement to me is to achieve consistency, not out performance, which is much harder on the pure return side. So if one were to buy and hold something forever, then volatility becomes irrelevant because you’re holding You’re forever, therefore you’ve subtracted the only thing that you’re fighting to mitigate. So if you no longer have that fight to mitigate, because it doesn’t matter to you anymore because you’re in forever, then just buy the thing that you’re always trying to beat. This has been fun. Thank you for joining me. Thank you very much for having me.