2:58AM Aug 19, 2019
Hello and Welcome, everyone. I'm Cory Hochstein and this is flirting with models the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
co founder and chief investment
officer of newfound research due to industry regulations, we will not discuss any of newfound resources funds on this podcast. All opinions
expressed by podcast participants are solely their own opinions do not
reflect the opinion of newfound research. This podcast is for informational purposes only should not be relied upon as a basis for investment decisions, and research may maintain positions in securities discussed in this podcast for
more information visit think newfound calm.
This Episode I chat with Wayne Himmelstein, President and Chief Investment Officer at logical 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 with Wayne as a philosopher streak and Twitter is his soapbox. Of course, 280 characters can be limiting, so I start our conversation by calling 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 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 catching. These sorts of strategies are notorious for their bleed, and we discuss what the payoff is ultimately worth the cost of insurance. Wayne describes a few ways in which we 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. 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 quote philosopher on Twitter, you say a lot of things. And it's tough to interpret them all the time, but 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 you look at your profile, your top pins tweet says quote unquote finance algorithms that developed from logic and experience that simply seek to mechanized 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 worry me, 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 fitting to talk about it more specifically to the words I use or to the idea I was trying to convey is that I've seen many quants, I'll call it a 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 data sets, and they start categorizing, and looking at 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 your value professional, or growth professional, or you got experience with certain types of sectors, Once you have an understanding of a thesis, from your experience in that sector in that factor or in trading, then you say, is there a way to systematized 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 generally use for my long term expense doing this or that, That is definitely the only way in my opinion, that one can be successful is when you've taken out something that has Well, you made mistakes you've learned and you've experienced it, and you just figure out a mechanism to systematized it. And that is really just to be able to stick to discipline, or perhaps use better mathematics to make it more precise, that's very different than starting the other way around. I think they were testing something like that, where you're starting from a place where you already know there was success, there's almost as somewhat disorders, 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, but that test is necessarily going to look at how you think about hypothesis testing this, right. So in a way that's almost curve fitting to your know, and 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 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 been shown to be successful over hundreds of years and 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 your test one month, two months, 12 months, 18 months? And so the testing of itself is not to determine does it or doesn't 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 can be helpful, right? We're just gonna completely diverged 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 we're relying on that parameter ization? I think you just said it best is if you only optimize, it gets fragile. So how I lead the way that you're 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 you're testing periods, when you look at the data, you look at the results, you have to look with not such a fine lens, that's the way I describe it, at least from my expenses. So if you test it, for example, and go with a prime example I brought up which is momentum. And sometimes there's momentum and say, one test two months, three months, four months, and go all the way out to 20 months, or a year and a half, 18 months. In that process, you're going to see that perhaps just to use 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 day 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, I will
get back on track here back to the tweets putting you back and all that. So you don't have a specific tweet here. But this is something you have written about a number of times, you've claimed that are the one true asset class is volatility. What do you mean by that? 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 dollars going to be $1. So as soon as you go out and buy something, whatever that thing is some assets, The moment you purchase it, You suddenly introduce yourself to variability. Therefore every asset is something we're volatility. When you do that, at that moment of purchase, when you're buying volatility, or you're buying variability by getting into something, you're unknowingly either long it or shorted it long or short volatility. So to that degree is if you're whenever you buy some you should know am I buying a long haul position or shortfall position? Because that fully answered question, 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 the option instrument itself is not necessarily Of course it has its main 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, I will say, a piece of real estate, nothing to do with liquid markets, when you buy it, you're buying optionality because you want this thing to appreciate. And if there's greater gentrification organization in the neighborhood, then your long haul, and if you believe that, then your long haul, because you've bought a stable, these real estate that you believe is waiting for a pop based on gentrification in other areas nearby. So you've bought a long ball position, In fact, that is paying you favor while you're waiting, it's actually better than an option. True option, you can get your data again 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, tail or vault and knowing that you have to decide which one do I want. And generally, the high return is going to be associate with a sort of opposition and a low return with a lot of opposition. To your point that sort of embedded optionality all as I said, that's what goes back to the original model of equity is 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, I think this ties in sort of the next tweet, I wanted to ask you about this for a long haul, or long 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 to exposure or don't want that to exposure. I've also heard you call this convergence or divergence. Can you explain this framework that you use to think about different investing strategies? Sure, Everything in the market is I'll call it expansion 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, 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 turn around buying growth is buying, continuance, laughing the cases, whenever someone's getting into position, just like pay off with the option structure that we're 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 the portfolio accordingly and construct their risk management accordingly. So a couple days ago, you tweeted, mistakes are underrated. As I reflect back on decades, and current 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 when maybe you can give some examples of mistakes you've made and lessons you've learned.
Mistakes are underrated, there's so many mistakes I've made. And there's so many lessons I've learned. Number one, we'll start out by saying my greatest growth and everything that I am today is a functional mistake. So um, I think mistakes are awesome. That's my number one statement to 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 tail. 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 downloads are much bigger than that moves, little ups and downs with a customer in the market. So I when I initially bought portfolios early on my first portfolio, had a long basket names, and I bought options on every name. And I thought, Okay, this is what you have to do, you need protection, the right way to manage a portfolio. And it turned out that buying options on every single name, which is just very expensive, you mitigate some of that kept on risk, but then you start making money and some of that you don't have a trade. And it's actually funny how I learned that when you might be you know, and down. And as Okay, I'm not making as much money. This is not working. But in between, I had this personal incident that occurred. That was a really funny story, I'll tell them and it got me thinking about how insurance works. It was about 2003, I'm still in New York, and I decided to move back out to LA, I've grown up in Southern California. So I moved back out here and I got out here I lived in New York had been there for about eight years, where I started my career in finance and trading. And so I get back to LA around two or three. And when I get here, of course, was the first thing you do when you move to LA as you want to buy a car. And so I've got a car and with a car, you need car insurance. And so I called up insurance company get insurance and and asked me a question, Can I 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 going to be 6000 a year. Okay, that's ridiculous. Not possible. Let me call up someone else. So I of course they were these Geico ads on TV. So I'm gonna go Geico and called Geico and same question commander, previous two years of having a St. St. answered and have it lived in our 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 I'm totally bewildered. 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 can't get into my beautiful new car. And I say to the agent, I just don't understand how to be like, I'm certainly not the first person to move from New York City from a non driving area. This is ridiculous and 6000. Another one says, I'm not in trouble. So he says to me, Oh, don't worry, I can find a company that doesn't ask that question. Almost all the time. I said, I'm sure just do what you want to do and get back to me. So he gets back to me a few days later, I found out 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 frustrating, because I'm weeks in LA and without a car. 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, I'm telling a buddy of mine about this. I'm saying after they've been happy to be on the same risk. I'm the same driver, I'm the same guy. And one giant insurance company has me at 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 they want to let us have that question. So the difference of assumption has 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 it. But most of all, our traders are actually using Black Scholes day to day to have some tweak on it, if you just need it's not the standard system have different assumptions they're making. And with these assumptions across the vault 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 in different strikes, there's going to be cheaper and more expensive. And so instead of this book I had that just bought all the same month all the same, like 2% out across this whole portfolio. But No, that's not the right thing to do, I have to take advantage of the weirdness and pricing and model variants across the option surface. And 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 too much better how to use options in protecting a portfolio at a model how to look at what other people are perceiving as cheap or expensive and get the best value for protecting a portfolio. So I want to put some of these philosophies in 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 manage long, short equity strategies for a long time. Can you may before we dive in to tying these philosophies to some practical examples, Can you talk about maybe what your historical your prototypical long short portfolios look like? 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, a cheaper and you're selling clinical 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 shortfall trait. 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 in short week of growth. Or if you are a price behavior trader, your long momentum kind of strength in names, whether it's relative strength, or using moving averages, your long some form of price strength, and your short price weakness, which is in 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 have a bit of both in a portfolio therefore have in a way to different thesis behind your total portfolio and like to sell portfolios within an optimal portfolio, it's pretty rare that I actually get the opportunity to have someone on the podcast who's done actual shorting, building a long short book. And I 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 faced,
The first one is one we've touched on already quite a bit. And this is the biggest thing, which is the shortfall component is that by its nature, when you're long and short, and most forms of arbitrage, a relative value with a 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 marketing portfolios is the consistency of payout. So you can depend on just say 70 bits 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. But The thing that comes with consistently profiting on it on a shortfall book is the other side of the trade or the pain of the trade. And so to me, that's number one. The other thing, which is a little bit more in the weeds, because I found it to be a great struggle in the area diversification. And what I mean by that is typically quants tend to have large portfolios, let's say three 400 positions, it's impossible to find a strong signal and 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 what 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 actually have two portfolios are on right now. Which one which is highly concentrated conviction trades because I need that super signal and one is 400 names 200 200. Short, because I'm taking more of a consistent play the odds betting Is that a function of the type of signal you're looking at, or is that a function of the portfolio outcome, I'm trying to generate it precisely a function of the signal, but the signal is based on the type of outcome You want me they're highly intertwined. It depends whether you want to start out engineering for an outcome, or just looking for a great signal to me, I kind of do both concurrently like I want to take the 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, it sort of reminds me of the whole notion of information ratio is equal to your information coefficient times square to breath, if you have to lower your information coefficient, but your breath goes way up, you can actually end up with higher information ratio, it's all about finding that optimal balance, I guess, and what you're trying to come up with exactly, it's only go to your other point, sort of that inherent left tail that's always lurking out there doesn't show up in day to day volume. And yet you walk in I know we're gonna talk about tellers catching a little bit. But other things as you think about managing long short portfolios that you can do inherently within management or portfolios themselves to try to address that left tail risk, I would say to start with how you're balancing it. So the number one tool or mechanism or rather Bessemer is measure people take is beta. So to me, beta is maybe going against much of the world here, but it's silly, and it's a nice measure, it tells you something but breakdown beta for a second bit as effectively Valentine's correlation, Vol is a symmetric with the left tail and correlation is has problems, you have no account for non linearity, you have the non stationary, you have got a lot of issues. So you're taking one problem variable and multiplying at times and other problematic variable. And if you multiply two problems, you get a greater problem. So the focus on using something as simple as kind of seemingly complex, what'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. It the long short exposure should balance by something that is more stable that can be understood to try to mitigate left tail is that something you'd be willing to open up about a little more because I know academically often long shorts are constructed dollar neutrals, just because it's a nice, easy way to do it. In practice, you do hear a lot about sort of beta neutral or sort of evolved neutral. Are there any sort of measures you'd be willing to open up about and talk about for these sort of ideas? Yeah, I without getting too proprietary. I like forms of their mathematical tools that account for nonlinear behavior and forms of clustering distance approaches, Well call geometric approaches I like as tools. I 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 successfully done and over others, and then shaping a portfolio according to what matters what those are some of the measures, I think, that are more viable than things like volatility, which ultimately is a very summary metric. So nothing standard about the errors of financial assets. Therefore, anything that stops standardizing is going to be at least a better tool. Going back to this idea of engineering outcomes. One of the outcomes a lot of people always want or express what they want with long shorts is that market neutrality? And 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? Well, let's start with a 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 a symmetric is not going to be neutral is gonna be neutral The moment you put it on. And as soon as the market goes, we actually see this a lot with any mortgage, what follows the further 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? The 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 me neutral at my next question is what do you want be neutral to? Are you directionally neutral? Are you factor neutral, keep ever directionally neutral portfolio that has equal long shorts, with a complete growth, tilt, or a value tilt or some other factor tilted of a volatility tilt? So the question first is you want to be neutral, under what premise of neutrality, how neutral on 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 wanna go further with this. But did that right? Well answer where you're going? Let's stick on this idea of neutral
for a moment, I'm gonna guess a neutral to what is the right question when you talk about maybe market neutrality or factory 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 form factor, neutral exposure, you're managing a sort of portfolio? What should you be concerned about? What would keep you up at night as you're trying to achieve that outcome? I think if you first start out to know, the exposure that you want to neutralize, 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 gravity, and that your long growth? 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. So you've said 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 France 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 as you first understand your objective, And Then what should keep you up at night is am I achieving my objective? A couple weeks ago, we actually got the other for coffee, we're talking about long, short portfolios. And you mentioned something really interesting to me, which was this idea. I think we're talking about August 2007, on quake and you mentioned this idea that volatility emerges because these pairs trading strategies diverged like they're 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 remember the conversation? I do remember that good. Can you walk us through that sort of logic that you were trying? Explain to me? Yeah, I think the summary word that comes to my mind is which I saw a lot in to me, I believe there was a lot of reasoning, or the driver behind the August quake quake is overcrowding and overcrowding being that just so many people doing the same trades. So the idea that vol emerges and Emily, and it's not just your portfolio is is the point that I guess no man is an island. And when we trade 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 the risk differently. And one of the best means of managing risk in these markets or portfolios or in multiple roles in general is liquidity manager is leverage management. So that 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 if it's wrong, I'll be the first to say that the trade was good. But given how others are needing to exit because they have LPs to answer to or they have risk that they're managing to, to find stop losses, or whatever might be on the total book, those trades and go the wrong way. 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 explained this quite a few times. And it's it's hard. I know what 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? I think where we win 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 managers tried to inherently limit that risk to mitigate? So first off, I'll say is, I've noticed that it is increasing over recent years, 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 across the world were always different factor portfolios, the overcrowding, this around factors measure has gotten worse and worse. I mean, when AQR unwinds everybody loses to a degree 20 years ago, there wasn't a car, maybe it was, I don't know, there's like history. But you know what I mean by that, in general, there was not as much exposure, certainly not to the factor side of things, and 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 question, I do believe there's always a means to managing time. In fact, I go back to one of the earlier things that we talked about, which is expansion of version. So if one were had reversion portfolio on which let's say its relative value, so you have value factor exposure, but concurrently, you have some expansion on which is growth factor, then you have to neutral portfolios, but with opposing factors, and therefore you've mitigated some of the crowded missed that could take place in one of the factor on wines. 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. So 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 on offsetting exposure, the typical and everyone knows is value in growth or momentum in 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 ball in the book. And that's in fact, one of the best tools for offsetting exposure in a neutral portfolio, which has a shortfall bias, typically in reversionary portfolio is to have external 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. So one of the cheeky questions you always get as a corn is a large you out on some beach, letting your computer make money for you. And, of course, everyone knows it's a constant process of evolution and research and the search for improved efficiency in your strategies. How do you think about strategy evolution,
strategies have to evolve constantly, that's for sure this misnomer of the quants can build it and go to the beaches, 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 is these two big facets of the market. But even those are evolving, even to the very idea that you buy a value stock, and it turns around and starts moving in your favor. Well, now it's a growth stock. So literally, the categories are changing on us. So if you bought a value book, and you leave it for six months, you're not on a growth book, if you were right, that is on your pics. So everything's always changing and to manage that exposure. If that's intended, then you've done a great job. But if you intend always and just being invaluable, then you got to be shifting a portfolio and then what our value metrics etc. All of this is forever changing. Of course, job is as hard as any other job in so rapidly evolving the environment specially with international 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. As you look back on your career, Have there been any strategies that you were in that stop working on you? That's a funny thing. When you say stop working, in fact that you actually wrote a tweet to post which was a post, he wrote about this idea, when will we know whether the value metric or the book is broken? I think our great grandchildren may know, given the law of large numbers, these things take forever to know whether something is broken. So I had to literally this experience where in 1999, I ran our portfolio as one of my first hedge fund actually, and I ran it for many years. And it did very well it 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 was like 6%. And by then I'd lost all my additional 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. Of course, 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 and plugged it in and did a walk forward and oh my gosh, it came back somewhere around oh seven it actually did really well in a weight and did well thereafter. And I didn't sit through it. I literally closed up shop and went on to something else because I believe that 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 you'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 quants 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 financing that does work because that regime that Allah favors you might last six months or six years. 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 different situations. Like if it's broken, you walk away. But 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 any sort of identify something's really broken versus out of favor? And then how do you think about handling those out of favorite situations? Yeah, I think that's definitely a time frame answer what I mean by that it was two things. It's the first a measure and then with that measure of time I'm 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 was still wrong longer than I needed to be, or that I wanted 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 measurement to give an example of something that you immediately notice. And that comes to my mind is there's a strategy that came about four or five years ago, which was a ETFs ARB, it was trading the ETFs that take leveraged positions, I guess they call them levered. ETFs, the double Long's or double shorts or triple long triple shorts. And so there's a group of people that realized that in to create a triple exposure, let's say s&p triple long, triple shorts, to create the exposure inside a need to have one minute to use options. So there's embedded fade or decay or bleed inside that. So a very simple marketing approach was too short to 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 data embedded inside the ETFs. So you're you're making money on time decay, while you are neutral, the market, What a fantastic arbitrage, you're totally neutral, you're only in literally the market. There's no idiosyncratic exposure, and you're just basically collecting data wonderful except and one more people start to realize this overcrowding ensued. And What happened is prime brokers saw that everybody wants to borrow these triples. And so they increase the short bar rate on the triples, in fact, to such an extent that the cost of the borrow was greater than the amount you made on the decay. And that's the point I got to is if I earn 8% of the gate, the butter rate was nine. And so there was no arm left, literally in measuring it. It was gone, Not only was broken, It was impossible. So all we needed to do is in understanding what they were doing. Look at nine is greater than eight, this thing is broken, and it's done. And a day later you move on because its history has 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, I guess it 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 tell you exactly why it? Yeah, I mean, what does that mean for your ability to even if it does come back still be in business? What is that? Exactly? Exactly? So one of 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 are running a long, short portfolio, but they might be evaluating long term portfolios, either in mutual funds or LP structure, you're sitting on either 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? 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 it all this minute, we'll 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. Look at this thing, behave daily monthly is just ridiculous. And I want to 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, hiding a lot of behavior, interment. So you want to know how this thing behaves? That's the top level ideas, 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 regressed because regression has some linearity in our not only our issues, but the map this thing you use certain, I guess, mathematical tools to map this thing. Next two 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. Or it could be things like volatility or oil and just go down the list and we've aggregated about hundred 81 pen. So we'll take any portfolio and map them next to all hundred 80 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 something they don't even know what they're supposed to other stuff like really, did you know that you had to focus on 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 managers aware of the exposures that had 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 with 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 risk they're taking. 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 explained a portfolio? Yeah, I think to me, the big thing is that the really generic sounding stuff, we take a value approach and the pages, the phone, the French, and then they have the we'd 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 premise outside of what you can buy in an ETF. I put it as simple as that. And, 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 looks 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 edges in that area. There's one manager I met years ago. And this is an example of the opposite. He had worked at fidelity for 15 years or so. And it 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 I was just area, just the depth of expertise in that area. That's not my focus. I wasn't as familiar didn't have 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 what he was doing, whether he was right or wrong as a different question. And he's returned to show how good he is at it. But the fact that he had such good reason and depth, the explanations, and then you see in the returns to enter together is okay, this guy not only knows what he's doing, he's expanding out in real time. So I like when those two things come together.
Was this a discretionary mentor they're talking about,
he was a little more quantity over quality. Yeah,
yeah. What do you think about the role of discretionary versus corn? And long short? Is this a realm where corn has a greater edge and discretion? Are there certain types of loans short portfolios, that discretionary might have an edge over one, I can't think of an example where one is more optimal than the other with respect to a specific type of portfolio. I could say, for example, if you had a large portfolio at 200, by 200, market neutral, That's impossible to do discretionary. 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. What I personally love Kwang. The reasons I love quanta are the same reasons that many people do is that things can be counted 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 exact same thesis as a coin, you could say, okay, we're going to buy that talked about buying stocks a few months ago. So we're going to value banks under this special metric, and we're going to buy all those that are cheap, and sell all those that are expensive under these kind of metrics that we understand about banks fine. So if that's your approach, the client manager is going to bucket the names, and they're gonna 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 that especially managers gonna say, well, I'm gonna get I like this one a bit more, or I'm going to get 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 discussion manager has better numbers, then you say, well, they're making those gut decisions. But that's the performance I want. So I 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 given 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, What I think is 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 that we're doctors love medical stocks and drug stocks and everything? No. So Likewise, if you're a 20 kind of person, then lean to one If not, you know, you don't. And there's good and bad in 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. But wanting to move on here, because we 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 Taylor's catching, which I want to make sure we talk about so reluctantly, I'm gonna switch topics a little bit to this idea of tell rescheduling. So I just really sort of hit the catch phrase, which is no pain, no premium, which I think 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 mega scale, you have fat left tales, you wrote this great paper called the illusion of skills, which I think I'll make sure we link to in the show notes. But I want you to read particularly those like what the math and in it you demonstrate that skew and ketosis really go hand in hand. With this idea of tellers catching in mind, how does one go about trying to protect themselves from big negative outliers?
of course Sqn, catharsis and even higher moments all go hand in hand, because it's all just one distributions. That's one characteristic and every different type of asset classes have different type of characteristics shapes. So that's for sure is, I think, first one should obviously understand what they're exposing themselves to is very much conversation we're having earlier, is this more of a right 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 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 an 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 might buy 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 association 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 follow the s&p goes down, that's pretty cool. If you have enough of them once you can come to those understandings. If you own optionality on the other side, you own right skewed to protect your left skew, I guess you could synthetically cut left tail with things like stop losses. That's what actually trend followers do. So stop loss management is a good synthetic left home indicator, 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 further. That's what happens with discretionary management is not obeying those exact lines, or exact levels like a quarter system would. So stop loss management, I'll call it as a synthetic left helmet. A Gator is good, but not hundred percent reliable because of number one gaps number two discipline. So therefore the best means which you don't have to think about is to have a right to offset, which is an option. I don't 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 tails exhibit exactly counterbalance. So instead of trying to get rid of its that you can't get rid of something financial theory would say like if an asset is if it's a right tail probably has to have a negative expected return sort of this almost like insurance payout. And I think that's the argument frequent 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 off, I think wrote a paper called the pathetic protection, it was only the fact of pathetic protection, the elusive benefits of pots 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 it's all these problems associated with puts their they're expensive. When 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 puts he has funny this idea of expensive if I think to myself most of the expensive things I can think of a really worth it. I mean, drive a Porsche or driver, I don't know, Toyota and expensive is feels it handles the road better, I guess you get what you pay for as 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, that'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. They 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 good via telephone or to buy the exposure from a professional who does that, whether in a funder to manage 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 a lawyer, or 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 in there. As far as what to do it's and managing the cost of assurances it is worth it. The point you made earlier is whether right tail always comes with a new tail, there's always a trade off. So The better the right tail, the lower the hit rate, and the worse the left to the higher the hit rate. So What do you want to 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. I Don't 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 the tough conscience? We all just go and buy it because you don't get in your car without it. And you don't wake up across the street without health insurance. So there's not even 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 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% of the money or 20,000 money options on the s&p, you spend a little bit of money, you 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 and say, okay, that's my cost of insurance. And when 2008 comes, you make a fortune and it 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 then you live through it. So it is part of it is the emotional experience and living through it. Part of it is how 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 so far, this idea of whether options are expensive or not. It 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 fundamental understanding super low probability events. So those way on the money but options are consistently underpriced. Because those rare events happen more frequently than their price to happen with the options. Do you take the same view,
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, then in real life, so kind of comical infant variants associated with fat tails. So that part of it or type says wholly agree, and I'm the biggest practitioner and a 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 volatility smile, which is that the skew picks up as you go out of the money, an option that is 20% out cost 10 cents, whereas at the money costs, I'll say $3 on the s&p as an example, people might look at that and say, well, 10 cents is dramatically less than $3. Short in nominal dollar terms, that's much less money. But the 10 cents, if that only happens once every 30 years, then you're 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's paying for itself. But those will still have great convexity when the market really cracks. So In my view, with the lower deductible, it's actually cheaper because you're making money more frequently. So it's about cheap or 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 out 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, the month of May it was a great question from at the money, that magnitude of about 6%. So that 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 Been waiting 22 years for a payoff, which by that time your kids are taking up 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're talking about options and optionality. Are there other opportunities that you like for protection? Yeah, there are some other assets that offer kind of protective behavior against equity markets, Typically flex quality assets, things like treasuries or gold. Oftentimes, the dollar against a other major market currencies, like European basket currencies have good characteristics when the markets are drawing down. And just in fact, recently, just in the month of May, the s&p was down 6%, gold and Treasury sword, those were 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 that they're 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 every markets fall, and they have their little bleed. So these are ideal instruments for less educated investors to have in their portfolios, a percentage of the 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 certain time, we were talking earlier about market neutral portfolios, 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 we lose it, I would say makes one or 2% a year. But in q4 of 18, it's up seven 8%. It's a right skew marketing 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 flyers, 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 then that has, of course, quality exposure. So when the markets fall, people run away from biotech 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 even have some convexity to them or some option like shape that are much cheaper, quote, unquote, than options. 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 expected to go. We have pretty 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, it was sleeping, and yet the soft flight to safety and 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 given sell off. I mean, how much of it is sort of situation specific? How much do you have certainly changing your protection over time? Or is it the idea of maybe buying a more diversified basket of defensive type positions? 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. It's like a derivative of timing the market, its timing, the risk of the market, and its timing a crash, it's in the realm of impossible So and 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 derivative we get into the realm of Don't even try, And that's where I would start is 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 of like we just talked about, you have some gold, yes. And treasuries, and you have some element, put options. Let's say on the s&p, you have these things, and they're always there. And a month, like may you put options didn't do as much as you wanted, but you're golden Treasury sword, and another time in 2011, in the correction, or August 15, or Jan of 16. And then you put options plus gold did, but Treasury didn't or whatever, one or two or 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 I supposed to have insurance for the emergency room? Well, yeah, that's one out of every x events are going to put you in emergency room, we're not just calling up a doctor and doing a radio appointment. So The answer is we don't know how to 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 cost again, to me, it's not expensive when you get what you want. But since it is more often a bleed, and then a payoff is perhaps people should have more treasures and gold and a little bit less optionality. But definitely all concurrently.
So Let's stick with that word bleed, because I know it's very traditional for retailers, hedge funds to have a very consistent bleed. And I know that you offer a tail risk hedging strategy that we talked about the past that you think does not bleed as much, if really at all, compared to traditional Taylor's hedging strategies. What do you do differently? How do you think about this problem differently that allows you to avoid some of the bleed, get still hopefully provide the protection that investors are looking for. There's always a trade off when you're trying to own optionality without blade or black holes, gamma and feta or the convex payoff versus cost to carry are on opposite sides of the equal sign and one goes up the other goes down and that's it to be positive or negative. So you can't have one without the other is literally the math Addy fight the math as me as a super quantum say, That's how dare you. But I will, I'm going to how do we find it is our approach is to and I'll start with saying 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 involved. And to pay for that they're going to get short, some other vault generally short, some interesting credit wall. So the idea might be your long, the s&p vault, or your short, some specific names that you think are about should come in or so you have long and short of all 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 to put spread, where you're long at one level, in short, at a higher level or lower level, depending on their side of your spread. But the problem 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 example that put spread and your spread is to be you know your short term at the money but then you're too long from Timpson out and down the market, correct 7%, you lose, so you're in options, but you lose. So that's exactly the opposite of what 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 think the trade off in 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 that people trade everything you trade, people trade, s&p futures, they trade tech stocks, Oh, in general, imagine somebody is a good trader, you talk to your buddies, a good trader who trades the tech stocks. So if someone is a good trader, well, you could generally learn and trade anything, at least a good basket of things. The more you train 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 idea as my trading background, I thought to myself, you know, what, if something 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 SMB or trading Google, it's just constraining your universe of instruments to only long puts. Okay, so buying and selling long, but 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 shortfall to just having too few inputs 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 100, long from 5% of the money, that's what you want to protect, save a little bit of a deductible. And so you have 5% of the money. And in case we actually stop 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 gonna take 50 off the table as a trader as a swing trader to take 15, you go home overnight with 50 points and and then they're cheaper the next day. So you buy not 50 back, but you buy hundreds you go home overnight, with 150, you're actually more put it then 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 hundred 50, your 720, you're only 30 left, if you go home that night, and that's the night that event happens. Trump does something silly. I think 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 unique but you still have 30 or 30% covered versus what you want it to be You're undercover, 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 needed to be Because you by trading 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 protection, sometimes underexposed, you don't know when the event is coming. So theoretically about 50% of time, you're going to be right around where you need to be 25% of time you're under exposed and 25% on your overexposed, double the amount of puts you needed in your dancing when the market crashes. So that 25% under exposed is your trade off. And I'd rather have that trade off than being shortfall. So Let's put you on the other side of the table, again, like we do with a long shorts. And there's all sorts of Taylor's catching products out there. Now, obviously, LP structure, but many even in mutual funds and ETFs structure, What should people look for when they're evaluating these types of portfolios? So
earlier I talked about these are trade off that there's always a trade off. The problem with insurance is that it costs we use the word bleed before. So that being the case, everyone who manages terrorist is trying to find some way to manage the bleed to pay for the insurance. Therefore, something in the portfolio is doing that when looking into a terrorist 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 all too short. So if all managers know Vol. And so there by knowing holiday, they live in the vault corridor. So they're going to say, Well, this is expensive all and this is cheap vault and the expensive all is going to be short, it is going to pay for the cheap while they're buying and where they're finding some idiosyncratic vol name 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%, s&p, you're actually losing money with them. But if it's 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, there is 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 said 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 finally put spreads, you accept that risk. But when your diligence in a portfolio, when your diligence and 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 example I gave you with my tiara strategy is sometimes not having enough protection on the book. So it's a more or less function, it's not a upside down opposite it shortfall function, therefore you decided that it was going 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 portfolio, if you understand that well enough, what positions are doing to find out where the exposure is actually do both look in the portfolio and ask the manager whereas exposure, make sure there's two things agree Of course, at least if you can have a 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 and if they don't know gamma. All right, Wayne, last question for you here. 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 hundred percent in cash, and you can only invest in one thing for the rest of your life. It can be an asset, it could be a given strategy, a portfolio configuration, but once you said it, you're done. You can never touch it again. What would it be and why spine and why 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 beating 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 council 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 managers select 100 of those names, and other managers like 100, Then one of them is gonna be more right and one more wrong, because one is above average, one is below average, because together that the average, So now you're selecting which one is going to be below which was gonna be so you have a new selection problem of which manager, all the hurdles against you, when you're doing anything that translates into this data that we see that very few people can beat the market. So while this first just by the market, The only reason people don't, in my view, and I guess my pay my expenses 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 to market and much lower volume. That's what you're buying is are buying consistency. So If eight 9% of years, the s&p to be able to achieve eight, nine a year, we can 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 our performance, which is much harder on the pure return side. So If one were to buy and hold on forever, then volatility becomes irrelevant, because you're holding it forever. Therefore, you've subtracted the only thing that you're fighting to mitigate. So If you don't want to 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 always trying to explain. This has been fun. Thank you for joining me