Negative Skew, Ergodicity and Thoughtful Diversification
In this episode we chat with Wayne Himelsein, president and chief investment officer at Logica Capital. Wayne has spent over two decades managing long/short portfolios and so brings a wealth of experience to the conversation.
We talk with Wayne about a concept called negative skew and how it leads many investors to underestimate the risk in their portfolio (as well as what investors can do to protect against it).
We dived into the concept of ergodicity, an idea that I initially discovered through Nassim Taleb but that has become a very valuable mental model for me for looking at both markets and life in general.
We dove into the pitfalls of diversification and how many investors engage in anive rather than what Wayne calls thoughtful diversification as well as why you only need to be right 54% of the time.
You can find more of Wayne on Twitter (@WayneHimelsein).
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Have comments about the show, or ideas for things you’d like Taylor and Jason to discuss in future episodes? We’d love to hear from you at info@mutinyfund.com
Transcript of Episode 6:
Taylor Pearson:
Hello and welcome. I’m Taylor Pearson and this is the Mutiny podcast brought to you by Mutiny fund, a multi-strategy long volatility fund designed to give retail investors a way to ensure their portfolios against volatility, tail risk and black swan events. This podcast is an open ended exploration of topics relating to growing and preserving your wealth including investing, markets, decision making under opacity, risk, volatility and complexity. This podcast is provided for informational purposes only and should not be relied upon as legal, business, investment or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of our same alternatives, Mutiny Fund, their affiliates or companies featured.
Taylor Pearson:
Due to industry regulations, participants on this podcast are instructed to not make specific trade recommendations nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, Forex trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they’re not suitable for all investors and you should not rely on any of the information as a substitute for the exercise of your own skill and judgment in making such a decision on the appropriateness of such investments. Visit www.rcmam.com/disclaimer for more information. In this episode we talked with Wayne Himelsein. Wayne is the president and Chief Investment Officer at Logica Capital. He has spent over two decades managing long short portfolios and so brings a wealth of experience to the conversation. This is a really fun one.
Taylor Pearson:
I talked with Wayne about a concept called negative skew and how it leads many investors to underestimate the risk in their portfolio and what measures can do to protect against it. We dive into the concept of ergodicity. An idea that I initially discovered through [inaudible 00:02:00] lab, but this become a very valuable model for me, looking both at markets and life in general. And then we also dove into some of the pitfalls, the diversification and how investors engage in what Wayne calls naive rather than thoughtful diversification and some of the ways to correct against that. I hope you enjoy this episode. If you’d like to learn more about Wayne, you can find more on his Twitter @WayneHimelsein. Wayne you wrote an article 2018 about how most mutual funds and hedge funds are negatively skewed, which leads to investors underestimate their risk. What is negative skew and how does that lead to investors underestimating their risk?
Wayne Himelsein:
Negative skew is effectively, it’s been called lots of different things, but it’s a fat tail on the left side, which is the downside. So if the shape of a distribution where a normal would be symmetric the asymmetry or a misshape in distribution has a more fatness on the left which means you’re going to have bigger negatives more often. And actually, not necessarily more often, but either deeper negatives or more frequent negatives. But either way it could be fat. Fat can come from frequency or magnitude. But it’s certainly not symmetric is the point. So that’s first and that was the first part of that. What was the second part of your question?
Taylor Pearson:
How does that lead to investors underestimating their risk? Are there examples of particular strategies or particular historical anecdotes where you’ve seen that play out?
Wayne Himelsein:
Sure. So underestimating the risk is if the assumption is that there’s not negative skew, which there very much is in all hedge fund or certainly in capital markets. If the assumption is that there’s not, then one will not expect as either a deep or as an extended draw down or as frequent draw downs as there is going to be. So effectively by buying some asset that could have a bigger draw down, but you don’t account for that, you either going to put more money into it or you’re going to leverage higher. You’re going to take a risk where looking at, call it the return side of the distribution, so looking at the positives, you’re going to take more risk when some of that positive is actually risk transfer.
Wayne Himelsein:
It’s built up negative skew waiting to happen. Harvey and CDK are our two big academics from Duke and Georgetown that talked tremendously about this. My favorite idea that I took from them is that the profit or the gains are not gains, they’re just risk transfer. The idea being that over time you think you’re winning, but it’s just kind of tucking away a risk, which will show itself or reveal itself at some later point in the future, in the form of a bigger draw down. That kind of corrects it back to where it’s supposed to be. So not accounting for that can be dangerous of course.
Taylor Pearson:
Tell me if this is a fair summary. Let’s say a fund has some model of the world that one out of 1,000 days, the markets can go down more than 5%. And they have unaccounted negative skew could either be, that happens one in 100 days as opposed to one on 1,000 days. It happens more frequently. Or, you the draw down on a one on 1,000 day basis is more severe. It’s 15% instead of 5%. So they’re taking on risk based on that. Based on that assumption which is sort of what you said, the transfer of risk. They’re just transferring that risk sort of into the future.
Wayne Himelsein:
Right. That’s exactly right. The summary is if one thing that there’s only going to be a 5% draw down and they put, just let’s use extremes to make the case or make the point, they put half their net worth into something because there’s only a 5% draw down and then there ends up being a 20%. Well, they would never have put the half of their net worth into it. And so that is simply the problem. And as far as the transfer is, transferring to a future date is the longer something goes. If something has a negative skew characteristic, the longer you go in time building up return, part of that return, you should be almost reserving to give back later. Part of the return is not really return. It’s a transfer of risk that will come out at a later time. It’s like the Piper has to be paid. And so you’re taking it today and you’re calling it your gain, but don’t. Set 20% of your gain aside because you’re going to give it back if there’s negative skew.
Taylor Pearson:
Okay. You’re picking up pennies in front of bulldozers or nickel in front of a bulldozer.
Wayne Himelsein:
The way I like to frame it oftentimes is selling insurance. Selling insurance is the risks that the bad event happens and you’re making money on the premiums while you wait. Which is not really making money, it’s just transferring your risk to when the event occurs.
Taylor Pearson:
Can you think of any examples in the past few years of where you’ve seen this play out?
Wayne Himelsein:
I mean, the past few years there’s been less events, but I mean, even Q4 of 18 was one of these. The market with the way it fell in December, if you looked at a Gaussian or a normal distribution, the fall there might’ve been a couple of days falling and then it would have stopped because normal distributions decay a lot faster at the tails, but there’s negative skews. So the market fell and fell and fell and the draw down just went deeper and deeper more than it would under a normal assumption. So if somebody had not accounted for that, they start getting margin calls and perhaps get shaken out of the market. When had you sized your position appropriately, you could have withstood it or held your way through it and been part of the recovery rally in January.
Taylor Pearson:
And this you also wrote about, showed the potential for mean reversion strategies to backfire, which I think goes along these lines. What is a mean reversion strategies or what are some examples and then how can they backfire?
Wayne Himelsein:
So yeah, the idea of mean reversion is that is the assumption that there’s a mean or an average that has to be maintained. There’s some force, almost gravity like, that’s pulling you back to the mean or to the average. With that idea in mind, if something starts to move away from the average a mean reverter would say, well, it has to come back. So what goes up must come down, so to speak. So the risk of mean reversion is that, again, not necessarily under an assumed normal distribution if it extends away from the mean.
Wayne Himelsein:
At some point it starts to get highly improbable that it’ll continue as a mean reverter, especially as a normally assumed mean reverter or as a mean reverter who is assuming normality or symmetry in the distribution. The further it moves away from the mean, the higher the probability it has to revert. Whereas in the negative skew world, it could move quite a bit further than you would ever imagine from that mean. And it could move so far and you can’t even see the average. And the average almost starts to mean nothing in terms of where capital markets can go.
Taylor Pearson:
And is it, I guess the example I was thinking of is, is it sort of like a, I don’t know if paradigm shift is an overused word, but presumably the market for buggy whips was mean reverting for a long time. You’d have more and less buggy whips and eventually something happened where that market no longer mean reverted or is it is there a similar, I’m trying to think if there’s a similar analogy.
Wayne Himelsein:
Yeah, that’s easy. All arbitrage. I mean, any form of arbitrage is a form of mean reversion. An arb is saying two things are quote and quote mis-priced, so they should be closer together and they’re drifting apart. So let’s talk about a relative value arb and a relative value arb is saying that the relative value between two like assets. And if you think of what are like act assets, one popular form of arbitrage is pairs trading such as Coke and Pepsi. So they’re both selling similar beverage, they’re both growing similarly, they both have moving through the market share. But so the relative value, one should not trade at a 100 PE and the other at a 10, that would be too disparate for two similar kinds of companies.
Wayne Himelsein:
So a relative value trader might look at those two PEs and say, “Well, one’s overpriced, one’s under priced. So relatively, they should revert back to being a very similar PE.” To talk about, in kind of a simple fundamental metric. If that’s the case, well that’s possible that they should mean revert. But maybe Coke is about to come out with new Coke. And so it’s going to mean expand a little bit longer than you expect because they’re innovating. So that’s the idea where in markets things that should be mean reverting. And like I said, most arb strategies will do most of the time except when an outlier changes the idiosyncratically one of the sides of the position.
Jason:
Also a lot of people don’t realize a lot of times that value investing is a mean reversion strategy at the end of the day.
Wayne Himelsein:
Absolutely it’s mean reverting because if something’s too cheap, it should go back to its quote mean value, which is fairly priced. So too expensive is of course should mean revert to cheaper and value or to cheap should mean revert to fair valued. And in fact, most investing is mean reverting or is not mean reverting. Most investing is people’s belief fundamentally in a mean reverting process.
Taylor Pearson:
And how does that backfire? I know you’re using the example of a value investing. Like what are the ways in which that could backfire on investors? If you’re betting on mean reversion, it doesn’t happen. How would that play out?
Wayne Himelsein:
Well, that too cheap is not cheap at all or it’s not too cheap. It can go cheaper. This is the common. So we can think of every, talk about survivorship bias. Think of every company that was too cheap that is now no longer in existence from Enron to 3Com to sorry, not 3Com I man WorldCom. There’s a common phrase for this called the value trap. Which is, if something’s cheap and all my gosh, it’s trading at half book value and then a year later it’s at a quarter of book value. So the question becomes, is cheap actually cheap or can it extend further? And if there’s really something wrong at the company level or at the industry level, then there’s no such thing as cheap enough.
Taylor Pearson:
We’ve talked about and you’ve written about quite a bit, the idea of ergodicity of processes can be either or ergodic or a non-ergodic. What are some examples of ergodicity when you think about it?
Wayne Himelsein:
Yeah. Ergodicity is a really cool concept about the equating of the time average and the space average. Space average is really just saying the probability space. So in probability terms, something has odds and in time, those odds might not play out. So the easy example of this, I always love the gambling or casinos as the perfect analog for betting, of course. So if you think of a coin flip, no matter where you are in the world geographically, and no matter what time of the day or night you start flipping a coin you’re going to approach 50, 50 odds. That’s the nature of the ergodic process of coin flipping. It’s independent all over the place.
Wayne Himelsein:
But so the probabilities will line up with when you started and where you started in every aspect. But in some things, if you as a single person walk into a casino and make some bet that is purportedly, it has an edge 51-49, whatever it might be, your experience might not be ergodic. So the odds of the game are as such, but you as one standalone path can perhaps lose all your money that day and not have enough to come back. So that’s the time that you’re entering at 3:00 PM and your personal experience or the single path does not equate to what’s called the ensemble average, which is what all possible scenarios would average to for a broader process.
Taylor Pearson:
I use the example I always think of, I think [inaudible 00:14:20] like don’t cross the river that is on average four feet deep.
Wayne Himelsein:
Love that one. Yeah.
Taylor Pearson:
You only need one section of the river to be 15 feet deep before you fall in and drown.
Wayne Himelsein:
Assuming you can’t swim, yes.
Taylor Pearson:
Yeah. We’re assuming you can’t swim or the current’s too strong. I’m not a particularly strong swimmer, so this is my concern. Talk about how that relates to investing in portfolio construction.
Wayne Himelsein:
Yeah. Crossing the river with an average depth of six inches is the perfect analog because you’re investing in assets with the average return of X. So you buy some company and you believe companies have an average return of 8% a year or 7% a year. Let’s look at the S&P over 100 years, it’s somewhere in that range, dividend adjusted. So we assume that average and we say, “Great, I’m going to buy this thing and it’s going to make me 8% next year.” And that company happens to be the one where there’s a fraud or where there’s a market change or where any number of things go wrong in the broader market or idiosyncratically to that specific company. And there, your experience will not be that 8% a year. It could be a loss of 50%. Because you’ve bought something on the assumption of ergodicity where there is none.
Taylor Pearson:
From an investor’s perspective, like the dangers of presuming markets are non-ergodic, that’s one. Are there any other just examples you see sort of out there happening right now where people are sort of assuming a market is non-ergodic when it is really ergodic?
Wayne Himelsein:
Assuming that it’s ergodic when it’s non-ergodic in fact. I mean, investors all the time. I’m on Twitter a lot, as you guys know. And all the time on Twitter I see these tweets about people quoting, the market was down three days in a row and the last 100 times it did that, or 20 times it was up 4% the next week. All these probabilistic determinations based on the last seven times this happened. And you know what? That’s assuming ergodicity. I mean, first of all, these are all tiny sample sizes.
Wayne Himelsein:
So set that aside, none of them are relevant. But even if these were large scale averages, the next time can be different. So it comes back to the idea that people trusting or putting credibility into probabilistic assumptions where they’re not just relying on the probabilities, they’re relying on the ergodic nature of those probabilities that they’ll follow through that way. Is a risk that’s happening day in and day out where people are making investments and then losing more than they ever thought they would have.
Taylor Pearson:
If you’re thinking sort of like of a general investor, someone that’s saving for retirement now, their financial advisor, what are sort of the things they should be thinking about in terms of like things in terms of how ergodicity or markets being non-ergodic impacts them? Are there sort of common piece of advice that need to be rethought or what should they have in mind?
Wayne Himelsein:
Yeah. So that’s really simple. It’s just to say to oneself, “I can be the worst path.” It’s like get up in the morning, look in the mirror and say, “I can be the worst path.” It’s as simple as that. It’s that everybody’s optimistic. They look in the mirror and say, either I’m going to get the average or I’m going to beat the average because we all believe that we’re above average. so the honest thing to do is to say, “No, I can easily get the worst path.” And how do I mitigate that exposure? The easy answer to that is that first of all, minimize the amount of money you’re going to put in any one thing.
Wayne Himelsein:
It’s diversification. And when I say diversification, not just naive diversifying, but it’s smartly diversifying in things like equity markets, treasury markets and gold market, all these different kind of asset classes. And then there’s of course, owning insurance on the market, optionality. Protecting oneself from what’s in academics called the risk of ruin. So the idea that anything could happen tomorrow that could blow up your account. If you wake up everyday thinking that or feeling that feeling, then you’ll do things to protect that from happening.
Taylor Pearson:
You said naive diversification. What are some common ways people sort of fall into the naive diversification trap or what are forms of naive diversification to be conscious of and maybe contrast it with sort of what you’re talking about and how that’s different?
Wayne Himelsein:
Yeah, naive diversification is, I’d say most simply the idea that just adding more greater numbers is diversifying. I give the easy example. If you want to be in a bunch of stocks and you say, “Well, 20 or 50 stocks is safer than 10 stocks.” Okay. But 50 is just getting close to the market, but I would rather be in five stocks long and five stocks short. So if you diversify your directional exposure, you’ve now done a tremendous amount more of diversifying or of mitigating your risk than just adding additional numbers. So adding numbers isn’t the kind of naive way to think about it. Whereas adding thoughtful offsetting exposure is not naïve, it’s thoughtful diversification.
Wayne Himelsein:
And another way to think about this is in terms of correlation. Where a lot of people would say, “Well, I want to find things that are low correlated to what I have.” This low correlation is this Holy grail where in my opinion, low correlation is high risk. Or not high risk, it’s semi naive in the sense that low correlated still means that you move a lot together. If you really want to be diversified, then you need negative correlation. I’m not saying that’s easy to do, but that’s real diversification. Versus low correlation, which non-ergodically, a good percentage of the time things can move together in extreme ways.
Taylor Pearson:
Right. So low correlation is saying these things could all move in the same direction at the same time, but just in varying amounts. Everything in your portfolio is going down. They’re just going down at different rates.
Wayne Himelsein:
Well, correlation actually doesn’t even take into account magnitude. Rates of change could change, put it that way. Nonlinearity is a risk to using correlation. The bigger point really is that correlation is not stationary. So what is correlated or not correlated today could change tomorrow. Correlation is not stable. So to the extent something is low, low could easily become higher. And especially with a confounding variable like illiquidity. So when markets get illiquid, things that don’t correlate suddenly do as an example.
Wayne Himelsein:
But if things are negatively correlated or more extremely offset as opposites and use a very simple analog of risk on risk off with equities versus treasuries, then there’s more tendency to truly be differentiated. Because they’re there different things that have naturally opposing behavior. Because the rationale for investing in them is different. So there’s a thesis behind their opposition. Versus a low correlation, which just seems low on paper in numerical terms, but really, it’s the same thing underneath that just doesn’t happen to move together for a few years. Does that make sense?
Taylor Pearson:
Got it. Yeah. You mentioned sort of illiquidity as a confounding variable. Could you give an example, like when does illiquidity, sort of you have two things that seem uncorrelated and you have some confounding variable. It doesn’t have to be a liquidity that causes that to happen. Is there any historical examples or things you’ve seen happen?
Wayne Himelsein:
Well, whenever markets start falling I mean I guess, certain things can get more illiquid. So let’s say, I don’t know, small caps can get more liquid and in doing so can have more extreme moves. But we all know already that small caps are correlated to larger caps. So that’s not a good example of what you’re asking. Let me try to think of a better example. A better example would be in different markets. If I looked at let’s say emerging markets versus developed markets. And so somebody thought that emerging was less correlated to a major developed. So the S&P versus an emerging market country whichever one would be less correlated. But given global market issues, systemic risk and given illiquidity, those things will correlate a lot more than one would expect. Because there’s still capital markets.
Jason:
Another classic example would be like private equity or real estate things that have illiquidity premiums. When there’s wash your cash and there’s liquid markets, they tend to maybe be slightly statistically uncorrelated but then when liquidity dries up, correlations go to one as we saw in 2008.
Wayne Himelsein:
Yeah, I mean, that’s a perfect example. Absolutely. Anything illiquid when money is needed, when they have to change their marks, when mark to model breaks, then correlations will come together. In fact, the only reason they’re not correlated is because they’re synthetically marked. So it’s not even a real assessment.
Taylor Pearson:
You’ve also written about sort of the difference between idiosyncratic risk and correlation risk, the ties into that. You said idiosyncratic risk may actually be safer than correlation risk. Idiosyncratic risk scream. And as for correlation risk, is a silent killer. Can you explain what you mean by that?
Wayne Himelsein:
Well, if you think about idiosyncratic risk, obviously by taking a concentrated bet, to a degree, you know what you’re betting on. And if someone says, “Well, I’m afraid of that concentration.” Maybe I don’t trust their signal as well or their evaluation as well. So they want to diversify. So they start adding positions and you add more and more positions and then you become some proxy of the market. 100 stocks in the S&P starts to become the S&P, even though it’s 500. The change starts to slow down between you and the grand or the broader index. So at 100 names, you no longer have your idiosyncratic risk.
Wayne Himelsein:
So you’re safer, quote and quote, but you’re not because now you’ve got systemic risk. Because where that one company that you knew so well or you had some amazing signal on, could have been the one that stood strong during a correction. Well, if there’s a correction and you’re proxying the index, then you’re going to fall with it. So your systemic exposure grows while your idiosyncratic exposure has been reduced. In the end, I think the greater confidence one has, or with this in mind, the greater confidence one has in their signal or in their evaluation methodology, the more they should want to be concentrated to make the bet on their idiosyncratic idea, not on the systemic risks that they can’t control.
Taylor Pearson:
The phrase that pops right for me, I think Carnegie had some quote about put all your eggs in one basket and watch that basket.
Wayne Himelsein:
I think I see that a lot as people just feel this, the emotional safety of, my eggs are all over the place, but I can’t even see them. They’re so widely spread and then things go wrong.
Jason:
Wayne, how do you think about the combination of those idiosyncratic risks? So like you said, creates systemic risk with your basket with high conviction trades.
Wayne Himelsein:
What do I think about it in what way? Sorry.
Jason:
The combinations of your individual trades, your idiosyncratic risk with each one of those trades and then combining it into your overall basket. So you don’t, like you said, the overall basket doesn’t add up to a systemic risk, but you have maybe, hopefully, fundamentally uncorrelated idiosyncratic risk.
Wayne Himelsein:
The way I think about combining them is really on opposite sides. So I want to have the concentrated risk or the idiosyncratic edge because I believe in my signals, I want to have that edge as my alpha source. And then systemically, if I can outperform that system then I should stay in my concentrated bets. And then on the system is where my head should be. Because at some point if the system starts to come down as much as the idiosyncratic bets might be right, they have the weight of the market pulling them down. So I can trust in their alpha over time, but because I don’t want to be pulled down by the system, I want to use the system to hedge. So for me the combo is to be short the systemic exposure and long the concentrated higher edge bets. Does that answer your question or is that what you were looking for?
Jason:
Yeah, exactly. You like having your idiosyncratic signals because you believe they can outperform the market. And when the markets go down, you know your correlations are likely to go to one to anyway. So you try to just hedge that overall market risk versus your idiosyncratic risk.
Wayne Himelsein:
Exactly. Exactly.
Taylor Pearson:
Yeah. Here’s the example I think of that we’ve talked about some Jason and I is if you’re an entrepreneur or a small business owner that’s sort of your idiosyncratic risk.
Wayne Himelsein:
Exactly. That’s a perfect analog.
Taylor Pearson:
Right. It’s concentrated. You wake up every day and you think about it and how to mitigate it. In a way that’s safer than I’m going to distribute all these eggs, run all of these baskets that I don’t actually understand that I can’t really keep an eye on.
Wayne Himelsein:
Well, so let’s use the prior thesis with that example. If you’re a concentrated owner of one company, think of, not that it’s going to matter to him, but Jeff Bezos is at Amazon. So he’s got all his net worth or Gates at Microsoft, the markets moves won’t really bother these people. But if you think of those examples, it’s all concentrated and they believe they’re putting all their eggs in one basket. They’re working day in and day out and they’re making this company as great as it can be. The risk to their stock is the market. So all they should be doing is doing what they’re doing everyday building an exceptional company and then hedging the market out thereby staying protected and actually betting on themselves and protecting against the system from hurting themselves.
Taylor Pearson:
Right, I think of like, I don’t want to say Amazon stock, like peak-to-trough in 2001 was like 90 or 95% draw down and had nothing to do with how Jeff Bezos was operating the company or how the company was being operated. That was just the market around technology stocks was pulling that down.
Wayne Himelsein:
Exactly. Yup. Fell with a crowd.
Taylor Pearson:
I think that’s a good transition into Logica and what you’re working on now. Why don’t you just talk a little bit about Logica and how you sort of see it playing into real diversification or that ability to sort of hedge market risk?
Wayne Himelsein:
Yeah, so for the longest time, and when I say the longest time, maybe the last five, six years, the focus has been on exactly what we’ve just been talking about, which is finding the best way to hedge systemic risk. And really the problem for every investor is the market falling. I think of it as people always talk about or sit around tables and discuss what can go wrong. What if Trump does something silly, what if China, what if just go down the list and 9/11. However many possible questions or examples of things that can happen, the beauty of the market is that there’s a collapsing of dimensions that all those questions have the same answer, which is that the S&P drops.
Wayne Himelsein:
And I love that. I love that. I don’t have to worry about the whys. I can just focus on the result. So the result for me was how to trade the falling S&P. And really that’s a function, not a function of, but the best way to trade it in our opinion because markets are very hard to time. The better mathematical tool that we like is volatility. Volatility is easier to model in our opinion. It’s gotten more consistent behavior. It tends to do things like cluster. It’s got natural characteristics that I think are harder to find in the market itself. So we focused on understanding volatility, its behaviors, its characteristics. And by doing so, find a better way to make money on markets falling through expressed through volatility trading.
Taylor Pearson:
Walk us through just how the high level structure of what those trades look like and how you manage them.
Wayne Himelsein:
Sure. So high level, I would start by saying, I don’t know which way the market’s going to go tomorrow. I don’t think many people do. And in fact, I don’t think anybody does. So given that agnosticism or rather directional agnosticism, one has to bet long and short. That’s piece number one. Set that aside for a moment. Piece number two is that, I don’t want to be wrong and I want to make as much as I can when I’m right. So that idea translates into this beauty to me of an asymmetric payoff structure. To me, the best part of investing, in fact, all investing in my mind comes down to the right payoff structure. Rights and wrongs if you assess the good traders over history of markets.
Wayne Himelsein:
I think a Market Wizards, the book wrote about this, that some of the best traders have maybe a 54 55% edge. It’s not like they’re right 80% of the time, that’d be great. It’s literally in the low 50s. Ability to be right is so small, if your edge is so small over 50-50, then the only way to maximize your profits over time is to be what’s called convex to being right and concave to being wrong. Meaning when you’re right, you want to be really right and when you’re wrong, you want to reduce that very quickly. So a trader, a typical trader, my background was a trader. The way you do that is you have risk reward trades and you want trades that have more upside than downside and you have stop-losses, you cut your losses quickly.
Wayne Himelsein:
That’s creating synthetically an asymmetric payoff structure where you cut your losses quickly and you ride your winner. So you have maybe a two to one or a three to one upside versus downside. The way to do that large scale, is through options structure which is obviously any option has a delta, has a rate of change with respect to the underlying and has a gamma which is the rate of change of that rate of change, also called convexity. So convexity is how fast you are moving with the underlying versus moving away from the underlying and losing less or gaining more. So now we have these two things in mind. We have the fact that we want this asymmetric payoff. And then we have the fact that we want to be agnostic to the market because we don’t know what’s going to happen. The number one way to merge those two ideas is one, simple trade.
Wayne Himelsein:
Which is a long straddle. A long straddle is effectively to be long puts and calls. And in this case on the S&P, so on the index. So what one is doing there is satisfying those two conditions. Your long puts and your long calls because you’re betting on the market going up or down, you’re agnostic to direction. Because as I said in the beginning, we don’t know tomorrow. And number two, you’re in both directions asymmetric to pay off. So if the market goes up a lot, your calls are going to make convexly. Market goes down a lot, your puts are going to make convexly and the other side will lose concavely I.e it’ll start to lose less and less because you’ve only put up premium. Altogether, this is to me, the absolute trade. So all the time we’ve spent in R&D in studying volatility is how to have this trade on most efficiently.
Taylor Pearson:
Just to simplify and you can tell me if I’m over simplifying here. Let’s say the S&P is trading at 100, you’re going to go buy one call for $1 and one put for $1. So you’re paying $2 in premium. And then the market can move in either direction. If the market goes up to 120, your call is going to appreciate and you’re going to make an asymmetric return on that. And if it goes down to 80, the put is going to appreciate and you’re going to make an asymmetric return on that. Is that at a high level, sort of a fair overview?
Wayne Himelsein:
Yeah. To make your example a little bit more precise, you’re buying a long call with a strike of 100 and a long put with a strike of 100. Let’s say in your example, to use your example, you pay a dollar for each. Usually that puts costs a little more than the calls. Let’s set that aside for a second. So you pay a buck for each and now you have $2 spent. So you need one of the sides to move $2 before your break even. If your market goes up $5, you lost your put dollar, but you’ve made $4 on your call. Not quiet because there’s an adjustment for delta and it’s not exactly one for one. But big picture, that’s the idea. The more the market will go up, the more your call will go to a delta one, which I.e it’s going to start to mirror the market more and more.
Wayne Himelsein:
The put has just lost its dollar. So you’ve maximized your loss. You have concave to losses. As I said earlier, if the market drops a lot to your call, you’ve only lost your dollar and your put will start accelerating making more and more for you as the market falls until the position is delta one, which means you’re effectively just short the market one for one. And once you have that in place, you know where you are every day. You know what your max loss is on either side. You don’t know what the market’s going to do, but you don’t care. That’s the beauty. And all you have to worry about is when are the right times to trade it.
Taylor Pearson:
And so talk to us about how you think about that. You’ve obviously spent a lot of time learning the right time to trade it. And one of the things we’ve talked about in previous episodes [inaudible 00:37:17] one of the challenges with being long volatility is historically there’s been a significant bleed associated with that. How do you think about sort of managing that bleed? Paying that $2 premium over and over and over with the market not moving?
Wayne Himelsein:
Yeah, so the bleed is easy to explain. To use again, the prior example, if you pay the dollar and a dollar and so your $2 in, if the market moves up or down over the course of that next time period, anywhere from zero cents to 99 cents, you’ve lost. The market moves up 50 cents, you lost in your put you lost in your call. So you have this buffer zone where you have to get in. In fact you have to beat the $2. If you don’t which a lot of the time the markets are just what I call noisy. They don’t make big moves one way or the other. They just kind of bounce around. And they’re a little bit, to use an earlier thing we were talking about it, a little bit mean reverting on a daily basis.
Wayne Himelsein:
Jumping up and down until a big move actually comes. So while you’re waiting, you’re spending this $2. You suffer and you bleed it, which is the word you used. So for me it’s, as my background as a trader, the focus has been is how do you trade around that position to try to effectively scalp. And scalping is a form of taking a little bit off the table on a position that’s expensive to rebuy it cheaper. And if you could scalp appropriately, I.e trade, well then you could take those scalping gains and hopefully pay for the bleeding.
Taylor Pearson:
The analogy we talk about sometimes, tell me if you think this is fair as sort of you’re using some different factors to look at, say, you’re evaluating the forest for the forest fire. There’s different things you can see around. How dry the area is or what the wind speeds are that could influence the probability of a forest fire or the potential damage and you’re using something like that to figure how to sort of scalp around and only should you be buying that insurance at the times when it’s more likely to pay off. Is that a fair summary or how do you think about it?
Wayne Himelsein:
Yeah. I mean, if you want to run with a forest fire analogy, so the forest is on fire and I’m surveying it and I get up in my helicopter and I fly around and I see that there’s a wind coming in. And it actually sways the helicopter, so I can feel it and it’s going leftward. Let me start to throw some more water on the trees on the left and take all the water away from the trees on the right. And by doing that, you’re concentrating your bet a little bit more with the direction of the wind. Now of course, the wind can shift in a minute and you’re wrong, but probabilistically, you’re slightly favored because most of the time wind doesn’t shift instantly. So if you can study and understand some of these characteristics of volatility that are consistent, then you can, let’s call it, watch the flow of the wind to make decisions.
Jason:
I don’t want to stretch your idea of ergodicity non-ergodicity too far, Wayne. But like if you think about a lot of times people map over passive investing onto ergodicity. And when we start talking about non-ergodicity, then we’re maybe talking about long volatility or tail risk investing in the sense that you can’t really use common statistics or probabilities as much as it takes much more active management. You have to be able to assess a vol surface. You need decades worth of trading to understand which way the wind’s blowing in that analogy or where to put the water or to add a little bit or remove a little bit. And it’s more as like was saying, you needed to know the vol surface, you needed to know the players in the field, you need to know who’s a natural buyer, who’s a natural seller, who’s a forced buyer, who’s a forced seller. So it takes a much more complexity and almost 3D puzzle solving and it takes a much more active management than putting on like passive index trades if you want to speak to like the complexity of dealing with that long volatility tail risk puzzle.
Wayne Himelsein:
Yeah, absolutely. I couldn’t have said it better than you said it. But the complexity in dealing with vol, I mean, if you think about options, there’s you called it the vol surface, there’s strikes up and down, there’s the chain, the vertical chain, and then there’s horizontally in time, any option you can buy. And then there’s the changing of that surface with respect to time. The vol surface at a moment in time, it’s like taking a photograph and you’re at a car race and you’re watching these cars zipping around at 200 miles an hour and you snap your camera and you take a photo and you have this picture of this car moving by.
Wayne Himelsein:
But a second later, it’s moved over in another lane. If there were lanes on a race track, yeah. But you get the point. So this whole thing is moving so dynamically and the surface is kind of wiggling up and down, kind of almost like looking at the surface of an ocean and waves are moving and then you have some extreme event like 08 and the surface is going to change it’s going to curve on both sides and become more kind of parabolic or less parabolic. That’s what’s talked about is called the volatility’s smile, which is also called skew volatility. And how more out of the money options get more expensive.
Wayne Himelsein:
So the whole surface can curve at the edges and then move up and down and then fold backward and forward. Here’s a lot of, I call them characteristics or properties that want us to understand. And as the life of a trader, I think there’s two things that I think could compliment one another. Number one, a deep understanding of mathematics. And then number two, a trading experience and the expertise in trading. Those two things come together. As a trader, I can know intuitively what to look for. And as a mathematical person, I can understand certain ways to categorize the data for what I’m looking for. So I come up with a trading thesis and I use a lot of this mathematics to model my expectations and my decision.
Jason:
And then we talked about previously about buying these [inaudible 00:43:27] on S&P 500, but can you talk a little bit about when you think that maybe they’re overpriced or you can find cheap convexity and proxies. How do you look at that as other tools in your tool belt?
Wayne Himelsein:
Yeah. I mentioned earlier that when we were talking about correlation or naive versus thoughtful diversification that there are assets out there that are naively diversifying, I.e buying more S&P stocks to make a bigger basket would be naive. The more thoughtful diversification would be offsetting exposures. The analog or sorry, the example I used earlier was a risk on, or sorry, a risk off asset treasuries. It’s also called flight to quality. When people believe there’s going to be risk, they’re going to sell their equity portfolios and buy more bonds or fixed income and particularly treasuries being the safest there is.
Wayne Himelsein:
So in the same way, there’s historically there’s assets like treasuries, there’s gold, there certain currencies over other currencies, currency pairs I’d rather be long the dollar over let’s say a European basket for a flight to quality a given a global situation. So the answer is that when puts are too expensive or to address your question rather, when puts her too expensive, I like the idea of utilizing some of these other instruments that amazingly so number one, they have offsetting characteristics to equity markets because they’re considered flight to quality. But number two, they have some of them at times put like behavior, meaning they have convexity. So they will spike up dramatically because there’s such a rush to quality. And a perfect example of this in fact is 08.
Wayne Himelsein:
If you looked at gold and treasuries during October, November of 08, there’s huge spikes. They look like options, they look like puts. And that’s because there was just this madness to get quality on people’s books. To reallocate their portfolio to risk-free. And so for me, I use these tools knowing that they’re semi dependable. If you call an option the absolute, or a put option, the absolute perfect negative correlation with perfect thesis against the market, it’s mathematically pure. The slightly less pure are these things that have tendencies to behave that way. They have tendencies to be opposite to equity markets, but they’re not always dependable. They’re not as ergodic as a put option is to the S&P. So we can use them sparingly. But when necessary.
Jason:
As Wayne, I find it fascinating what you do, especially because you’re always in the market and you’re using your history, your algos, your ideas to dynamically toggle your exposures. Where we have other managers that like to dip in and out of the market when, to use the forest fire analogy, when they think it’s going to spike off. That’s when they’re going to have their options in the market. Otherwise, if they think the market’s quiet, they’re going to be completely out of the market. So you’re one of the only managers we have that is always in the market. But you’re able to dynamically adjust your position sizing and without giving away your secret sauce. Is there a way you can kind of talk about that at a high level?
Wayne Himelsein:
Yeah. Let me first start with always being in. The problem to me with not always being in is that you can’t have half insurance. It’s like you have car insurance and then you go driving somewhere and you turn down some side street and you get hit by someone and you call up the insurance company and they’re like, “Wait a second. I don’t know if we can cover you because you’re on a side street. We only cover freeway accidents.” Well that doesn’t work. When we buy car insurance, we need to know we’re insured whatever neighborhood we drive into. So with the market, for me, always being in is a function of, I don’t know when the thing or the event is going to happen. Tonight could be the worst news that any of us have seen.
Wayne Himelsein:
And tomorrow’s the day that the market gaps down 9% or it’s black Friday. Whatever could happen in a non-ergodic environment or universe can happen tomorrow. So I fear not being in all the time, especially when I know I have beta somewhere else in a portfolio. So most people, it’s not just that they’re like, it’s okay not owning puts this week. Meanwhile, they’re fine just being effectively long the market and a bunch of other exposures they have elsewhere in their life. So to me the problem with not being in with insurance is that everyone’s always in with long the market in some form of another. So that’s number one. So how we do that goes back to I guess, expertise that I was discussing earlier. It’s trading.
Wayne Himelsein:
I use the word scalping. I use the word trading. It’s finding the right buy and sell points, staying in this position, but sizing differently. So it could be that today I find that it’s both as a trader and characteristically understanding using mathematics to analyze all these different aspects of the, if we go back to the forest, it’s the wind and it’s the height of the trees and it’s how dry or new the leaves are. So you’re looking at all these things you know you should look at when it comes to fires. And you’re a longterm firefighter because you’ve been with a fire station for 25 years. So you know what to look for. You’ve modeled this out and so as a mathematician, as a trader, you find times where, no, I don’t need 50 gallons of water right now.
Wayne Himelsein:
I’m fine with 30 gallons. I’m still in, in case there’s a disaster and it’s only with 30 gallons, but I still have 30 gallons. Most people have nothing. Then you see valves getting cheaper. Well now, I’ll pick up 70 gallons or another. I had 30, I’m going to pick up another 30. So now I’m in 60 gallons. Now I’ve got more than I need, but that’s okay. They’re cheap. So I’ll hold onto them. I think I’m going to make some money. So by effectively trading or scaling the size, one can make money consistently day by day while you’re waiting for this unknown timed event to happen. And I love that as a trader because I’m doing what I love and trading. I’m using mathematics to become as precise as possible, but I’m always there in case something happens, which is the absolute best part of it all.
Jason:
Fantastic. And we used several analogies throughout this and I think that’s the first time I’ve heard do the analogy with how many gallons in your bucket. So I liked that you stepped up on that.
Wayne Himelsein:
And it’s making me thirsty.
Jason:
In thinking about, I always like your overarching philosophy is one of expansion and contraction and how that helps you from a 30,000 foot view. Think about markets in general, if you want to kind of speak to that. What’s your overarching thesis with expansion and contraction?
Wayne Himelsein:
We were talking earlier about mean reversion. So the two sides of the market as markets are oftentimes mean reverting and sometimes they’re mean expanding. Earlier we were discussing, you asked me what is a very typical meaner reversion strategy in the market. And my answer was arbitrage and many forms of arb. And if we look down the list of arbs, they also align very well with the list of blowups. So we have for example, longterm capital management in 1998 was arbing all of the world, finding all of these mean reversion trades.
Wayne Himelsein:
And then mean expansion occurred and it kept on going away from them. So the market at times will revert and a times, in fact, a lot of the time it’s just momentum. It keeps ongoing. The difficult job of any trader is to toggle between expansion, reversion I.e you find a position if, go back to the, what we were talking about the value trap is stock’s getting cheaper. One has to decide is this cheap, where the reversion is going to play or is this going down and down the momentum’s still taking over and this whole thing will be cheaper than cheap if I wait another few months.
Wayne Himelsein:
I play at the edge of that decision. In trading, I love volatility because it’s a little bit easier to find those inflection points of reversion expansion. But knowing the market is always toggling that line of reversion expansion. It wants to keep on doing what it’s doing. And then once in a while will just expand dramatically in one direction. So knowing that that’s its behavior, we want to build the best ways to take advantage of those inflection points to understand them and to catch them. And we believe that we can do that best with a landscape of volatility and our experience.
Taylor Pearson:
We talked earlier about sort of like naive diversification verses thoughtful diversification, which I liked that that dichotomy. When you think of naive diversification, is that frequently based on mean reversion or how do those two pieces fit together?
Wayne Himelsein:
Yeah. I mean naive diversification kind of aligns with mean reversion in that if you have a portfolio of stuff, a larger number of constituents, then they’ll all mean revert to the average. You buy one stock and you’re purely idiosyncratic and that number two and number three. And as I said earlier, by the time you get to 100 stocks in the S&P, you’re very close to being the S&P 500. All right. So you’re mean reverting to the mean, which is the market. That that aligns in thesis. And if you go the opposite of that idea. So if you unwind your positions, you go from 100, 99, 98 and you come down to five or 10 positions, now you’re taking specific mean expansionary bets and that kind of aligns with concentration. So those two things are, to your point, diversification and expansion reversion are very much I’ll use the word correlated
Jason:
And as you know, we’ve talked about correlations. Another way we look at it, and maybe I want to get maybe some push back from you on this. There’s a lot of times we look at what people call correlations are just statistical correlations and those statistical correlations only hold up in a risk on environment. And then when we go risk off and volatility starts to cluster, those correlations that we saw in 2008 go to one. So, what a lot of times what we’re looking for, as you alluded to, we’re looking for fundamentally negatively correlated asset classes knowing that crazy things can happen in markets that we can’t even imagine. So maybe as far as mean reversion, mean reversion is only necessarily applicable with statistical correlations during risk on environments. Does that hit it or not really?
Wayne Himelsein:
Yeah, absolutely. Statistical correlations another way of saying that is that it’s the correlation is just in the numbers. That there’s no core thesis behind why that correlation should exist. So statistically you can look at two different time series over 200 days and say, “Look, these things are correlated or not.” But you have no idea what they are. So you can’t depend on any continuance or any stability of that relationship. But if you know that time series one is the equity markets and time series two is gold, then I can stand back and say, “Okay, I understand why these two things should behave differently at certain times.” The difference between really statistical correlation or just correlation and true offsetting, I think comes down to the knowledge of what you’re dealing with. What lies underneath.
Wayne Himelsein:
The second piece of it, which we haven’t touched on, but it’s just as important, is what’s called confounding. Which is that’s confounding variable, this hidden variable that lies inside of correlations and people can find a couple of things that look like they’re not correlated. But as we talked about earlier, in a time of illiquidity, those things become correlated. So liquidity is the confounding variable. It steps in at the awkward times and starts making other things behave differently. And there’s other confounders out there, there’s many of them. So when looking at correlation as a standalone, one is ignoring confounders and one is ignoring thesis.
Wayne Himelsein:
So come back to reversion, if one’s looking at a reversionary trade, if they understand the thesis behind the reversion. These two things should come together because, finish the sentence. Whatever that might be as your thesis. And I’ve thought about potential confounders and those are safe because I’ve protected those with perhaps some put options around my position. Then you fall into a safe reversionary trade. Otherwise you’re relying on numbers that have no basis. Long answer. But that’s what I was trying to, I thought it all mattered to the question.
Jason:
No, it does. So if we think about your using expansion and contraction, is in the ideal world is to have both expansion and contraction trades on at the same time?
Wayne Himelsein:
Absolutely. It’s more than necessary because most of the time the market’s mean reverting. And so if you say, I’m just going to be an expander, we’ll then you’re going to not participate. At the same time, there’s always a few names in the market that are expanding. So you want to be involved with those. The point is that the market has pockets and at large scale tends to behave mean reverting and small scale there’s mean expanding. But itself, both scales undulate between reversion and expansion where the large system, which has acted mean reverting for a while becomes an inflex at a point and then becomes mean expanding because some other crisis has occurred such as China devaluing.
Wayne Himelsein:
So all of these things just point to the fact that when I spoke earlier about agnosticism to direction, I don’t know which way the market’s going tomorrow. I also don’t know whether it’s going to be reversionary or expansionary. So you can not only not call direction, you can’t call the thesis. If you can’t call the thesis, the safest thing, the most ergodic approach is to be both reversionary and expansionary at all times and find the best alpha you can in both pieces or in both buckets. And then of course, protect both buckets in the best way you know how
Taylor Pearson:
One thing we’d love to get your opinion on and talk in terms of like fundamental correlation obviously like an option on the S&P you can make a very clear case for why that’s fundamentally uncorrelated. What the question is there. I think a lot of people think about, often financial advisors talk about stocks and bonds in a 60-40 portfolio and that correlation. Just curious on your, your thoughts there.
Wayne Himelsein:
Yeah. I mean that was similar to what I was talking about earlier in our portfolio where we use these, I call them put proxies. When puts are too expensive, we might pick up a little bit of exposure to gold or treasuries as things that are offsetting but not pure put. It’s the same thing as really, the put is the purest form of offset to the index. The purity, the relationship is mathematical. It is never going away. It is so pure that it is ergodic. It’s like the only ergodic thing you can get in the market is if you’re long the S&P and your long of S&P put, that second thing is going up when that first thing goes down all the time, like bar none.
Wayne Himelsein:
My concept or my core desire all the time is to always be in the most ergodic position. If you can’t be for whatever reason, you can’t be all the time because you’ll bleed to death when the market runs up for 10 years, I.e the prior decade, then you find other means of being a little bit lesser ergodic, you’re in proxies. So whether that’s bonds like in your example the 60-40, whether that’s a bit of gold. We find other things that can give us a little bit of put like behavior with not the same reliability but not the same cost. So with everything, there’s a trade off. When you can have the best, you have the best. When you can’t, you take a little bit of trade off and you have to manage when it’s time to go with only the best versus to take the trade off, which is also a tough decision.
Jason:
Just a quick question, put a finer point on it. When you’re talking about treasuries, what tenor are we talking about? Are you looking at all the tenors? Are we talking short term treasuries, medium, notes, bonds?
Wayne Himelsein:
I like the longer term. I like 10, 20, 30 and farther out as a better judge of or as a better tool for flight to quality.
Jason:
Is it also because you get more convexity on the long bonds as well or?
Wayne Himelsein:
I believe you do. Yeah.
Jason:
And then to Taylor’s, going back to maybe what Taylor’s question and maybe I’m looking at it in a different context. Taylor’s maybe asking what your general opinion on is on a classic 60-40 portfolio and those correlations or how that holds up in a in a walk forward basis when we don’t know what the future looks like.
Wayne Himelsein:
Sorry. Ask that one more time?
Jason:
Your general thoughts on a classic 60-40 portfolio. How you view that 6040 portfolio. How do you view that on a walk forward basis? When you don’t know what that world looks like, how do you view that, those correlations that have held up for the last 35 years? What do you in about that? I don’t know. on a work forward basis?
Wayne Himelsein:
Yeah, so everything on a walk forward, first of all discount the last 35 years in the sense of just saying, let’s set all data aside for a second and say, does the thing make sense rationally? That’s where I always start. And so it’s not about 60, 40, or 55, 45 or 62, 38. That’s just optimizing to a T. The idea is do bonds and stocks bring a better outcome than just stocks alone? And I think if we all sit back and think to ourselves, there’s a capital structure equities are not as safe as debt think to ourselves, yes. That that’s a, there’s a, there’s a capital structure. Equities are not as safe as debt. They’re seniority to the debt and the capital structure.
Wayne Himelsein:
There are real fundamental reasons why bonds are safer. So once we believe this, we can look at all the data in the world, we can look at 35 years or 100 years. We know that there’s some safety in taking equity risk off the table to be in a preferred security which is the bond. And so yes, I believe that that’s a safer thing to do. Do I think that it’s going to act like an acted over the last 35 years, that’s up in the air? You probably pretty similar. I’d probably want to look at how it acted out over the last 50, 45, 40, 35 30, and see how much change there is, how much stability there is to they’re acting alike or, or not, right. Maybe there’s a variance to their co relationship or covariance.
Wayne Himelsein:
So I’d want to understand that and I want to see what are my extremes if I got going back to the ergodicity or lack thereof, if I got the worst possible path. If in the 60, 40, I looked at the, let’s say, the three years, maybe that was the 1970s. I don’t know, I’m just throwing this out there. I have no idea. But let’s say the early 70s was when a 60 40 portfolio for a period of two years was demolished. I’d assume that that path could happen a few times more or perhaps even worse. So to know the extremes would tell me what kind of boundaries I’m in relying on this relationship. But I’d be comfortable from the get go knowing that there’s a fundamental reason why I’m safer with both combined versus just the risky one.
Jason:
That’s primarily it [inaudible 01:03:18]. What do you mark that’s primarily due to the position sizing on your equities more so than even the bonds is from what I gathered what you’re saying too as well.
Wayne Himelsein:
Well, position sizing is of one to the other. The bonds versus equities is really just, I’m trying to normalize, let’s say to volatility if. If you have of course it … call it even simpler than that. If you don’t do bonds, you just did cash and you said, “I’m going to go 60% or 50% equities and 50% cash for a simple.” Then you’re half sized. You’re just going to be safer. I mean, that’s the most evident, clear point there is. But that’s to make the point as part of the reduction in risk is just taking equity volatility off the table. So if we had to bifurcate the two kind of risk savings, one is reducing sizing on the equity or on the risk on the higher risk piece.
Wayne Himelsein:
And the second is that the other side has a potentially offsetting exposure that when one goes down, the other might like to go up. So they’ve got this synergy of relationship or this complimentary nature. I’m not sure of the exact attribution, but there’s some of the risk savings that not just the fact that bonds behave opposite because they’re more of a flight to quality. I said, it’s because you’ve just downsized some of your risks. To your point, yes. Sizing matters. And all of that is part of the combo of how to build optimal portfolio.
Taylor Pearson:
I think we can wrap with that. Thank you very much for joining us Wayne, this was great.
Wayne Himelsein:
Thank you.
Taylor Pearson:
Thanks for listening. If you’d like more information about Mutiny Fund, you can go to mutinyfund.com or better yet drop us a message, IamTaylor@mutinyfund.com and Jason is jason@mutinyfund.com and we’ll get back to you. You can find us on Twitter @mutinyfund, and I am @TaylorPearsonME.
