hard earned trading wisdom

Euan Sinclair needs no introduction from me.

I’ll cut straight to the gold.

He’s been a repeat guest on Erik’s Outlier Trading podcast a few times. His writing and interviews mince no words. Despite never sugar-coating the reality of trading, I found his most recent interview even bolder. It’s this witch’s brew of insight that is somehow both timeless and underreported.

I’ll start with an idea from the interview he did from late 2025 that always bears repeating before moving to the more recent chat.

The most common misconception about trading volatility

In Euan’s 2025 chat with Erik, he was asked “What is the most common misconception about trading volatility?”

He zoomed in on the mistaken logic that because volatility is mean-reverting, selling it when it’s high assures a profit since it always comes back down.

There are several facets to the mistake. One is with respect to how volatility can cluster based on a market regime.

Saying volatility’s mean reverting is true, but the means also change, you know? So, if you sold volatility in, I don’t know, March 2020, right? Volatility didn’t go back to 15 for about a year, right? Volatility had a new normal. So just because something’s mean reverting doesn’t mean you’ll make money because it comes back.

Another flaw is in understanding that you are exposed to both realized and implied vol.

The other thing that can be wrong is that you’re not directly trading volatility. You’re trading the spread between implied and realized. And that doesn’t have to be mean-reverting and it doesn’t have to be negatively correlated with the level of volatility either. So just because the implied V comes down, that’s not necessarily going to help you if the realized vol still is higher than the implied.

[Kris: I remember suffering through a short option position in nat gas in the expiry right before I got married. The V09 option cycle (expiry date was late Sep 2009). Implied vol got up to 110% but it realized more than implied for our entire holding period. We chopped ourselves up on the short gamma even though we had “positive vega p/l” on some of the marks.]

It’s easier to understand this when you scrap the concept of “vol” for a moment:

Forget about volatility, right? Volatility is just a way of turning option prices into another thing. It relies on a model. Forget about all that, right? Let’s say you’ve got a straddle and the straddle’s trading at five bucks and that straddle normally is trading at two bucks. So you’re like, “Oh, that’s really high. I’ll sell it.” And then as soon as you sell it, it moves by $10. You know, the stock moves by 10 bucks. You’ve just got hammered, right? But you know, the straddle drops down to a dollar. So you were right about the price of the straddle, but you weren’t really just trading that.

Finally the VIX chart illusion:

That’s one of the problems people get wrong is selling volatility spikes isn’t as appealing as it looks as when you look at the VIX graph over time and you’re like, “Oh, the VIX these spikes. It always comes down again.” Yeah, but you that’s not what you’re trading when you’re trading options.

See this post from the Liberation Day period if this is not clear: you can’t trade spot VIX.

The rest of these excerpts are from Euan’s 4/1/26 appearance on Outlier. Emphasis mine.

Where to start: known effects

Erik asked Euan where retail traders should start. Euan’s answer is a tour through what he actually believes works, why it works, and how to think about learning from past data without fooling yourself.

If you’re prepared to have low enough expectations that they’re realistic, and then really work at this, um, and it doesn’t have to be a full-time job, but you can’t just think this is my extra income. You know, this isn’t like driving for Uber.

Erik: Thinking a little more deeply then about the kind of things retail traders should look at — I went through an interesting exercise on my own, because people would ask me for ideas on places they might want to start looking, and I always struggled to give a good answer. There’s a million variables. But I’ve recently started directing people toward really well-known market effects — stuff you could go look at research papers on SSRN in mass, well-researched stuff. Something like time-series or cross-sectional momentum as a general market effect.

The reason I’ve been going that route is that there are so many things you have to do right as a retail trader. Even if you’re doing all the right things but it’s centered on an effect that isn’t really there but you think it is, that’s massively detrimental long term. Even though the returns might not be — as you talk about — stuff that would give you Lambo money this week, you’re at least building the infrastructure around something we know exists.

Are there effects or markets that you think are better suited for those first few repetitions for a trader?

Euan: It’s not so much about finding a market where you have an edge. It’s not like, “Oh, you’ve got to trade shitcoins,” or “You’ve got to trade options on pharmaceuticals.” The actual instrument and sector are of lesser importance than the concept of what makes this thing work.

There are a few things in finance that we know broadly are real. Momentum is one. The other one I’d say, if you’re just starting out and you want to set up what I’d call a real trading operation, would be carry.

If you really understand the concept of carry, particularly as it applies to futures basis trading — in theory, we know that a future is going to coalesce to the spot price at expiration. But that doesn’t tell you how it’s going to do it. It doesn’t say the future’s going to stay there and the spot’s going to go up toward it. It doesn’t say the other way around. But what actually happens most of the time is that the future moves toward the spot price. Naively, that’s exactly what you wouldn’t think happens — you’d think the future was an expectation of the future or whatever. But no. It doesn’t matter what you naively think. That’s a very strong effect.

If you know about that effect, you’ve now got lots of places you can look for ways to apply it. And if you understand the effect, you’ll know places that are better than others. You look for something with a big basis. You look for something with high volatility, because that also gives you more of a basis. And you want something to move a lot, because if nothing moves, you can’t make any money.

That leads you to something like the VIX. The VIX futures have a very high basis to the cash usually. If you look at the difference and you annualize it, if that entire basis is realized, that can be 100% a year. Are you going to get 100% a year? No, because lots of other things are going to happen, and you’re not going to realize all of that, and there’s volatility. But that’s now an effect where you can start saying, “Okay, now I know this. What can I do to harvest it?”

Then you start saying, “Well, clearly I want to be short the future if it’s above the cash. That’s risky. What do I do to hedge that risk until I’m happy?” And maybe you’re like, “Okay, I’ll do a future spread. I’ll be short the front one, long the back one. What ratio? What futures?” Eventually you’re going to come up with — and there are papers written on this exact effect, I haven’t just pulled this one out of nothing.

A lot of the stuff’s out there. People tend to have this idea that no one’s going to tell you anything that works, whereas literally there are thousands of pages written on stuff that works. The universe isn’t going to give you money the way you want it to necessarily, but this carry effect — once you understand it in the VIX, then you’re like, “Holy shit, this also works on a ton of other commodities. It works in bonds.” And then, “Okay, if it works in bonds, does it work differently in treasuries, credit, corporate bonds?” And the answer is yes. So now you’ve got spreads. So relative value is the next thing you start looking at.

Pick a bunch of carry situations, learn to put them on, learn to manage them. They largely take care of themselves, but you have to adjust, you have to understand the risks. It’s like when you fly a plane — you’re learning on a Cessna, learning slowly. You don’t just get into a MiG and blast off. But this is that Cessna. It’ll get you money, and it’s a real trading operation.

Then you move on to relative value. These spreads move around. Maybe I can scalp those spreads. How do they move around? What’s the range? And then, “Well, sometimes they don’t move around.” Okay, and that’s going to lead you to momentum. That’s your next one.

Everyone should read that book by Antti Ilmanen, Expected Returns. It breaks down — it’s about 400 pages long, that’s how detailed it is — and it talks about the volatility premium for about four pages. It goes into hundreds of things like this that work. Carry is a huge unifying feature. Relative value. Momentum. The variance premium and options — again, that’s there. I wouldn’t recommend people start with it. It’s kind of slippery and a good way to lose a ton of money unless you’ve got everything else sorted out. But it’s another one.

That’s where I’d start. I’d start with one thing, move to the next, keep adding. Once you got to three or four things, that’s probably all you can handle. You’re not going to be able to have 10 different strategies and keep things together. That becomes a major logistical operations issue.

Where to expect edge: the hard leg is where the money is

[Kris: My biz partner would say the “hard leg” is where the money is.]

Typically in the world, you get paid for doing something that makes the world somehow better. You provide a service, and typically there’s something unpleasant about that. Otherwise people will just do it themselves.

If you’re selling flood insurance, that’s a tough business because you make money, make money, make money, and then you get absolutely blitzed. That’s a tough thing to live through. Everyone thinks they can, but in reality it’s a lot harder than you think. We’re very bad at figuring out how we’ll feel when something bad happens. Similarly, the guy in the flood who’s cleaning out the sewers, he gets paid.

If you’ve got a clear idea of, say, the carry trade — why am I getting paid to do the carry trade? Largely because I’m providing a source of risk insurance for other people. I’m short those futures that are going to go massively up when the market — like a couple of weeks ago — when the VIX spikes from 18 to 30, those short front-month futures are going to hurt you way more than the long back months. And they have to. Because if you hedged that, then there’s not going to be any premium for you to take out of the trade. All of these things are risk premia. In order to get risk premia, you have to accept that risk. Over time you’ll be fine, but you’ve got that unpleasant nature of the payoff.

Another good rule of thumb: anything where you think this is a bad idea because the risk looks unpalatable — there’s probably edge in there somewhere. If you as a professional trader are like, “Yeah, this looks dangerous,” you’ve got to accept that most of the rest of the world also thinks it looks dangerous, and you get paid to manage that fear. You are the bomb disposal guy. That’s why you’re getting paid.

So if it’s like, “Ooh, there’s no way I’d sell options over the weekend, that’s dangerous” — all right, what side do you think the edge is on? The guy who is prepared to sell options over the weekend, or the guy who wants to buy options over the weekend?

Walking into danger — I’m not saying you always have to, and it’s a risk judgment; you don’t have to take every risk that’s out there. But if it makes you nervous, there’s probably edge in there somewhere. If you can get it to a point where you’re comfortable with it, or can diversify, manage, or hedge it, that’s a good place to start making money. That’s why you get paid for selling options. You don’t get paid for buying options.

Again, difficult thing to say, because for the last three or four years, retail made a ton of money buying 20-delta calls in the indices. By the way, historically, what’s the worst option to be long? Index 20-delta calls. If you think that’s your edge — no. Take that money and good for you. But that’s not the way the world has typically worked. It might work like that for the rest of my life, who knows. You’ve got three years where that worked really well, and 100 years where it didn’t. Just as long as you know that.

Look for any situation that legitimately makes you uncomfortable, and there’s probably something in there.

Learning from the past

Erik: Two follow-ons. I’ll give you both, and you can pick. The first is how to use past information to inform future projections or predictions. The second is pricing of convexity and some of the internals behind that. You pick whichever one sings to you.

Euan: I think the first one is probably more suited for a podcast format.

Basically, the only thing we have to predict the future is the past. And there are two things you can say: the future will be somewhat like the past, or it won’t be like the past. Of those, clearly the better one is to say it will be somewhat like the past. That just makes sense, and usually that’s the way it’s been.

The problem, particularly in finance, is it’s an adaptive system with people on the other side of, and everyone is going through this thought process to a certain degree as well. Everyone’s looking at the same information. Everyone’s saying, “This is the way it’s behaved.” Everyone’s trying to predict on the same thing. Largely coming to a spread, but broadly the same conclusions.

So using the past to predict the future is always going to be murky. And there’s an unfortunate tendency we have now. Like, 30 years ago, no one did backtesting, because you couldn’t. There was no data, no computers. Even before Excel — you’d have to write a program in Fortran. It was hard. No one did it.

Now, everyone does backtesting. The problem is that means most people do it wrong. They see backtesting as a way to find patterns, and they’ll test stuff, do cross-validation, test walk-forward, go out of sample, and they’ll find things. The problem is they look at so many things. Of course they’re going to find something. That’s the big problem with using the past — you think you’re doing statistics correctly, but really all you’re doing is looking at stuff over and over again until you find it.

You’ve also got to remember — and this is an Aaron Brown thing as well — as soon as you’ve looked at some data once, you’re done. That data is in sample forever. You can’t say, “Oh, I’ll try my trend-following system on the S&P. That didn’t work. I’ll try a mean-reversion system on the S&P.” The only reason you’re trying that mean-reversion system and think it might work is because your trend-following one didn’t. So you’re already overfitting. It’s not so much overfitting to the numbers. It’s applying information you’ve learned by looking at it already, even if you’ve looked at it for a different thing.

At this point, there is practically nothing I can do to study the VIX that’s going to tell me anything, because I’ve looked at the VIX for so long that I know what’s in there already. I’m not actually making any sort of new judgment. All I’m doing is applying what I know has worked in the past because I’ve looked at it so many times.

The way to address that is to start with something you believe first. Do you believe in carry? Yes. Do you believe in momentum? Yes. Cross-sectional momentum? Yes. Risk premia? Yes. And then you come up with an idea, and then you say, “Does this work in the past?” And you test it. If it works, great, you can make it better. If it doesn’t, you give up.

We see this with options particularly. People are like, “Well, I tried selling strangles on Monday and holding them all week and it didn’t work. But then I found if I sold straddles on Tuesday and got out on Thursday, it did work.” Both of those are driven by the same effect — they’re both variance premium plays. If one of them didn’t work and the other one did work, all it means is you got lucky picking the entry point for one and not the other. As soon as you’ve looked at that second one, you’ve just completely overfit the whole thing.

That’s one of the big problems people make by using past data. You use past data to come up with the overall belief. Carry means something. Credit spreads mean something. We’ve got, I don’t know, 5,000 years of stories about credit. Credit’s older than money. It’s a real thing.

Looking at numbers should be the last thing you do, and only to confirm something you’re already pretty sure works. My risk premia thing — I’m sure it works. I can test does it work better in a one-month option or a one-year option, but I should not be just blasting combinations in until I find something that is the best.

One of the most dangerous words that’s come out in quant finance is certainly “alpha drift.” This isn’t a retail problem either. I see this in quant firms all the time. They optimize and think they’re doing it out of sample, but they’re not, because they’ve already — this is the fourth model they’ve run on the same data because they keep looking at the S&P 500 or something.

Optimization is a horrific thing. It’s one of the worst things to ever hit the world of finance — that concept that you can make something better, or perfect, or optimal.

The other one, by the way, is theta. I can’t think of a thing that’s cost any more money than theta — this idea that options decay over time. That is literally not what the Black-Scholes equation is telling you. The Black-Scholes equation is literally telling you they don’t. If everyone knew an option was decaying over time, no one’s going to pay for it now — they’ll buy it tomorrow, it’ll be worth less. The number of people who’ve fallen into that “I’m harvesting theta” thing — and there’s plenty of influencer types who are peddling that story, typically also owning a brokerage at the same time.

The difficulty of learning from the past is that people think they can learn too much from the past. You have to discount everything you know based on how much you actually believe the thing. Show me an effect you found in the market — I don’t know what it is. A few years ago, “gold goes up on Fridays” was a big thing. Everyone looked at the data — look, gold went up on Fridays. Okay, why would that happen? “Well, it’s because fund managers are scared of risk and they go into gold on Fridays.” All right, find me one of those people. Not you, or some other person who buys gold on Fridays. I mean someone who runs an appreciable amount of money — doesn’t have to be Pimco, but people who run hundreds of millions or billions. Show me one of those people. No one’s ever managed to do that.

Does it go up on Fridays? Well, it looked like it did. T-stats, sure it did. Now how much do you actually trust that? On a scale of zero, which is candlestick charts, to 10, which is carry is a real thing — I’ll give gold on Fridays a three. Could it be true? Sure. But I don’t see a compelling reason. You look at enough things, you’re going to find that.

That’s the problem people get. They look at the t-stats and go, “Look, statistics, man.” All right — now how many other things have you looked at that didn’t work? Because you’ve got to include those.

The philosophical problem of what you can learn from testing

Starts with sound reasoning:

I’d like to start with the idea that people buy bonds at the end of the month because of window dressing and rebalancing of 60/40 funds. There’s an idea. If the stocks go up more than bonds in a given month, people rebalancing have to sell the stocks and buy the bonds. That should have an effect. That’s your hypothesis.

So let’s look for situations where that hypothesis has a market effect. Do you see it? Do you see it in situations where your hypothesis would tell you you should see it? Equally, do you not see it in situations where your hypothesis tells you you should not see it? If this also happens on the second week of the month, maybe it’s not what you think it is.

I’d always like to start with a reason and then test the reason.

…But there’s thousands of years of philosophers who pointed out all the contradictions in this stuff. Nothing in the world is out of sample. The only person in the world who’s out of sample is a baby who doesn’t know anything. That’s the great curse — the more you know. With me and the VIX, it’s not that I’m the greatest VIX person in the world or even close. I’ve just looked at it, I know it, I can’t be surprised by anything it does. That’s the downside of experience: nothing is out of sample…

The best you can do is find a situation that looks like it’s somewhat constant over time. If you look at the statistics of the VIX, the distribution is pretty constant. If you look at what it did in the ‘90s and in the 2000s, break it down in big blocks — it kind of looks the same. Whereas stocks tend not to. You can’t look at Tesla 10 years ago and Tesla two years ago. The business models were totally different, but in one case the stock was a dollar, and over the last two years it’s been between 200 and 400. These are different situations. Stocks are not stationary.

But really, one of the things you have to do is accept how little you know about the world. The good traders never say, “I’m right because of this, and this, and this.” The good traders are like, “I think I might be right because of this. But on the other hand, I could be wrong because of this and this and this.”

Aaron Brown, who’s probably thought more about this stuff than anyone I know, says that a bad trader is always saying “and furthermore,” and a good trader is saying “on the other hand.” A good trader is looking for holes in their argument. A bad trader is continually trying to find other reasons why they’re right.

David Hume — smart guy, all philosophers do is think about thinking — they were pointing out the problems of trying to learn from induction hundreds of years ago. There’s no answer. That’s the only information we have, but we can never really draw certain conclusions from it. So I always apply a big whacking discount to everything. And it’s not statistics. It’s a meta level above that. It’s like belief. I’m not just saying there’s a statistical rule for how you judge if this is good or bad. In fact, literally there isn’t. There’s an actual degree of belief that is independent from what the numbers are telling you.

Vibes vs quant is a false dichotomy — there’s vibes in everything

[The general construction of his portfolio isn’t] based on statistics. It’s based entirely on my degree of belief. Like that carry trade we talked about — how much do I believe that? A lot. Given what I know about its volatility — and I mean “things move around” volatility, not standard deviation of returns — given I know how much that is, given I know how scary it can be, but given I know the belief, I might give that 40%. Whereas there might be another trade where statistics are just as good, but I’m like, I don’t know, I don’t really believe in that one. That gets 20%.

That gives me my baseline, and then I go run all the statistics, and then I say, “Well, okay, I’m shrinking it to this vibes portfolio, because that’s the one I kind of wanted to be at in the first place.” There’s a lot of people who’d be like, “Well, that’s not quantitative. That’s not systematic.” Well, first of all, who gives a shit what they say? You don’t get a medal for being quantitative. You make money or you don’t. Being quantitative is seen as this kind of goal, and it’s not the goal. It’s a tool toward the goal.

Every time you make a portfolio or sizing decision, you are at some point applying that kind of thing. If you’re going to go through all the math, eventually you’re going to have a utility function, and that utility function is going to have a risk aversion parameter. So you’re going to go through all this math, and then at the end it’s going to say, “Oh by the way, what’s your risk aversion parameter?” You have to make that decision somewhere. I’m just doing it at the start. It’s much more of an exogenous thing that’s obvious.

It’s more a reflection of what I think I actually know than a mathematical statement. It’s not a mathematical statement. It’s an epistemological statement.

A lot of people get hung up on “insult found: the discretionary trader.” How is that an insult? Everything you do is discretionary at some level. Literally, you have chosen to do something. You have exercised your discretion to do something. Whether you do that in the math you choose, the problem you attack — you can’t go through life without exercising discretion. That’s actually called knowledge or experience. It’s a useful thing. If you don’t use that, you’re an idiot.

The cost of systematization

There’s certainly this whole idea that if you’re systematized, it’s emotionally easier to stay with things. I don’t think that’s true. I have operated completely systematic stuff when I’ve had jobs, and it loses money. It’s not easier. It just isn’t.

The reason I think you should systematize something is because you have to. Every time you’re removing yourself from interacting with the market, you’re removing yourself from an opportunity to learn. I think you should do things as manually as possible — and I mean it. If you can get away with writing stuff down on a legal pad, then that’s the way to go. If you need to use a spreadsheet, use a spreadsheet. But if you can do it on a spreadsheet, yet you choose to write some massive API call and do the whole thing in some language — you’ve done that because you wanted to, not because you needed to. That’s a mistake.

The places I’ve systematized things are because I wanted to avoid operational risk and key-man risk. If I’ve got a strategy I want to run for an ETF every day, it has to operate if I crash my car and go into a coma. That has to be systematized. But if I’m running something on my PA because I find it interesting and I’m still messing around with it — systematizing that, all I’m doing is removing the opportunity to learn.

It’s one of those things where retail look at institutional and go, “Well, it’s all systematized. I have to do that.” But they don’t think of the reason we systematize things. It’s not to make the strategy better. Honestly, it probably makes it worse, because you’ve locked it in. You’ve said, “This is it, I’m not going to be learning anything more about that now.” But it definitely removes operational risk. It makes it cheaper to run, because you can just let it run on its own. You don’t need a person doing it all the time.

People on the outside are making a judgment of how the things on the inside work, and they’re missing the point.

How do you avoid it? I don’t know. Hopefully if I pointed that out to you now, you’d be like, “Oh yeah, now I get it, this is the important thing.” Think about anything else you’ve ever learned. You’ve learned by doing, by the immersion in it. Trading now, the actual act of trading, is click, click, click, done. You don’t learn anything from that.

But what you do learn about — especially things like adverse selection and the ability to execute — like, “Oh, it looks like there’s plenty of volume. Oh, but every time I put a 100 lot in, all the bids disappear.” That’s the sort of thing you’ll learn so much about just doing it and interacting with it. You’ll never learn that if you automate the trades.

[Kris: To insert something here. I’m reminded by an interview with Agustin Lebron where he talks about getting ideas from just staring at an order book for ours. Seeing how it updates, how bids and offers change after trades, or in the absence of trades but while the rest of the market is doing things. He recommends doing this in medium or low-liquidity names where you can possibly see a story unfold in the order book. I agree with all of this. I mean I don’t think I never explained it quite like that because it’s just the water you swim in. If you trade something relatively illiquid, every movement matters. If some 20 delta put trades on the offer, you have to triangulate whether it’s skew expanding or volatility in general. See Mermaids, Fireflies, and the Bid-Ask Spread.

But there’s a corollary hidden in wisdom of learning from sparse order books: edge is inversely related to liquidity. The more liquid the market, the more whatever’s left looks like a risk premium. SPY is one of the most liquid markets in the world. If you had any edge in trading it, the edge would be so scalable your heirs would be set for an eon. Remind me to rant about this sometime.]

If you’re in a situation where you have to automate the trades, you’re probably playing in a game where you’re not going to win as a retail trader. You shouldn’t be trading, “Oh, I have to run this through the API so I can trade 15 times a day.” That’s one of those quant-envy things where you’re like, “I’m going to do this the way Citadel would do it.” Again, you’re playing basketball one-on-one with LeBron. Good luck. Don’t care how tall you are.

Inefficiency vs risk premia

There’s a split between inefficiencies and risk premia. With an inefficiency, price competition is going to drive it to zero. Nothing’s purely risk and nothing’s purely inefficiency, but market making in options has a lot of inefficiencies. It’s like, “I’m going to buy this option because I can sell this option. Those things should be worth the same. They’re off by a little bit.” That kind of thing. The act of providing liquidity is a risk premium, but the way you do it is looking at all these little inefficiencies.

If you look at the way equity market making has gone in the last 30 years or whatever, ever since the floors have gone and we’ve gone electronic, that has been driven to almost zero. It’s not at the point now where any sort of random person with an off-the-shelf piece of software is going to consistently make money making markets in equity options anymore. It’s gone from profit margins like Tiffany’s might have, and now it’s Walmart. Everything’s driven to zero.

Risk premia are different, though, because they’re there for legitimately a reason. It’s a different utility function. You’re doing something you know is wrong because you don’t want to deal with the risk. You’re buying insurance. You’re not buying insurance because you’re dumb, you’re buying insurance because you want insurance. And I’m selling it to you because either I can take the risk, or I can lay off the risk, or whatever reason — I’m doing it to maximize expected value. You’re doing it to maximize another utility function. We’re both happy.

That kind of thing has got more legs to it. If you look at the variance premium over time it’s been reasonably constant. Obviously it goes up and down. You’d think that after 2008 it would get wider; surprisingly, didn’t much. You’d also think that now, because of all these zero-DTE covered call funds, it would collapse. And it kind of has. But sooner or later people will realize a lot of these funds don’t make money, or some of them will blow up, and it will go back to where it was. All these risk premia are cyclical, but there seems to be a natural limit that doesn’t get driven to zero.

It helps to be able to say: is this an inefficiency that people can’t access, or is it genuinely a thing that everyone knows is there and people are doing it for different reasons? Because that’s the sort of thing you can build a trading life around. Inefficiency — if you find one, good for you, knock it out of the park as hard as you can. It’s not going to be there forever. If it is there forever, Citadel is going to come along and build a desk on it and crush you.

Adaptive markets, but not adaptive traders

Erik: The follow-on, though, even within something like risk premia that we know is there for a reason — an example you’ve given previously is there’s a $20 bill on a busy highway. If I’m standing on the sidewalk and there are five crackheads, and I say, “Hey, I’ll give you 10 bucks to go get that.” And then one of the other crackheads says, “I’ll do it for eight. I’ll do it for five.” And then at some point we get to a floor where the crackheads are like, “I am not going to risk my life for this anymore.” Do you think that with something like risk premia in index options, based on that consistency, we’re generally about at that level? Or do you think it’s likely to shift around more?

Euan: That’s difficult to say. But with your crackhead example — one crackhead might say, “I’ll do it for six bucks,” and he runs out there and he gets killed. And then the next crackhead’s going to come along and say, “Actually, I want seven now, because I saw what happened to this guy.” It’s not the same people all the time.

What we tend to see is a new cohort of traders come in and blow up doing the wrong thing, and then a bunch of other people come in and end up doing the same wrong thing. The market never completely adapts, because it’s never exactly the same group of people who learn. Markets learn, but traders as a whole don’t. For things like this, a lot of it’s propped up by new people coming along making the same mistakes as the old people. Adaptive markets, but not adaptive traders. In a lot of cases people think they’re the same thing. I don’t think they are. There were a ton of floor traders, good floor traders, and then everything went electronic, and they tried to do what they did on the floor on the screen. It just didn’t work. And I don’t know any of those guys who were like, “It’s not working, I’ve got to do this and this and this instead.” That didn’t happen. Instead, a whole bunch of other people — studied computer science at Cornell or whatever — came in. It was a whole different group of people who made money at that point.