In Trade Like a Quant Bootcamp, we talk about win-win risk premia harvesting. It’s a game where no one’s really competing for the edge.
Think about VTI (Vanguard’s Total Stock Market ETF). You expect to make more than implied by the stock market’s cash flows (a risk premium) because holding these stocks is uncomfortable. They’re sensitive to all kinds of nasty surprises.
When you buy something like VTI, you’re essentially saying, “I know I could lose 50% of my money at some point, but I expect a 7% annual return over cash, in the long run and on average, for my trouble.”
Many people look at that trade-off and say “no thanks.” They’d rather hold something less risky, even if it means lower returns.
This creates an opportunity for those of us who are prepared to step up.
We say, “I don’t like the idea of losing 50% either, but I know how to size positions properly, diversify across other trades, and manage my risk. I’ll take that 7% expected return, thank you very much.”
It’s a win-win situation. The risk-averse person is happy avoiding what they perceive as excessive risk. You’re happy collecting the risk premium. Nobody has to lose for you to win.
But there’s another kind of game too. One where assets get temporarily mispriced because someone has to trade rather than wants to trade.
Trade Like a Quant digs into these situations too. These are players whose objective isn’t to get the best price, but to achieve some other goal:
- They’re forced to liquidate positions
- They have weird regulatory constraints
- They need to window-dress their books before reporting
- They have redemptions they need to meet
All of these scenarios can push prices away from fair value.
Let’s say without these forced sellers, an asset would be trading at $100. But because some fund manager has to unload a position right now, it’s bid at $94 and offered at $96.
If you can recognize this situation, you can step in and buy at $96, capturing that $4 expected return. You win, the forced seller loses.
It’s like buying a used car from a dealer: the guy who needs to buy a car immediately versus the dealer who can wait for better terms. The rushed buyer gets a worse price, and the dealer gets a better one because he can afford to be patient.
This is what I would consider a true inefficiency. You’re exploiting someone else’s constraints.
But there’s another question we need to ask: Why do you get to capture this inefficiency instead of the big players?
If it’s obvious that someone is selling down an asset, everyone will know. Competition should drive the price back to fair value almost instantly.
So why doesn’t it? Why do you get to step in and take the trade?
Simple answer: because these trades are usually a little bit crappy.
Maybe they’re:
- Too small for bigger players to bother with
- In markets that are difficult to access
- Unreliable or noisy or slow to converge
- Operationally awkward
- Capital intensive
Take the bond window dressing trade as an example. Some traders seem to want to buy bonds toward month end for reasons other than maximising their expected return (such as window dressing their portfolios). If you’re willing to take the risk of buying bonds ahead of these traders, you can, on average, sell them back to them richer than they should be.
This is a trade that has made money on average over the long run. But:
- It only happens once per month
- And it doesn’t work out every month – it has had drawdowns throughout its history (it’s in one right now)
- So it could take a long time for the edge to play out profitably.
In short, it’s not the sort of thing that Citadel would go out of its way to chase.
The key insight: most persistent market inefficiencies have similar characteristics.
Someone is making the asset mispriced because they need to get something done (they’re being liquidated, facing redemptions, trading for reasons other than maximising their expected return, or meeting regulatory requirements).
And you get to exploit it because the opportunity isn’t attractive enough for the big players to completely eliminate.
Think of it like plumbing. It’s a useful service that most people don’t want to do themselves. The nastier the job, the more you can charge. Trading inefficiencies work the same way.
Accept that these trades have something gross about them and manage them accordingly (Bootcamp covers this too).
Don’t do what I did when I started, and try to design strategies that give you the equity curve you’re looking for.
That won’t work. You can only eat what you’re fed by the market. No amount of will can create an edge out of nothing.
Be happy that these trades have something sucky about them because if they didn’t, we likely wouldn’t be able to participate in them.
Here’s what you should look for:
- Who is creating the mispricing? Nearly always, it’s someone who has to make a trade, not someone who wants to.
- Why do I get to trade this instead of Citadel? Because there’s something unattractive about the opportunity – it’s small, noisy, operationally annoying, or requires access to markets most players avoid.
Don’t believe in magic. Don’t hope that the RSI crossing over the MACD is going to make you rich. That’s absolute bollocks and has nothing to do with actual market mechanics.
Focus on cause and effect. Who’s pushing prices around? Why are they doing it? And why aren’t bigger players stepping in?
You need a plausible explanation for why your strategy makes money. One that doesn’t require wild leaps of faith or assume you’ve discovered some magical pattern no one else has noticed (this is exceedingly unlikely).
Here’s a practical framework for finding these inefficiencies:
Step 1: Identify forced sellers or buyers. Examples (there are more in Trade Like a Quant):
- Index rebalances force passive funds to trade regardless of price
- Month/quarter-end window dressing creates predictable patterns
- Futures expiration can create temporary dislocations
Step 2: Assess why bigger players aren’t eliminating the edge
- Is it too small?
- Is it too noisy? (Sharpe ratio under 1.0, say)
- Is it structurally difficult to access? (Requires special accounts or approvals)
- Is it operationally intensive? (Requires constant monitoring)
- Does it have nasty tail risk? (Works 95% of the time but blows up spectacularly 5% of the time)
- Is the counterparty risk high? (Hello crypto)
Step 3: Test your hypothesis with simple analysis
- Don’t dive straight into complicated backtesting
- Start with basic analysis that makes good use of your data
- Check if the effect persists across time and shows up where you would expect it to
The COVID crash of 2020 is illustrative as an extreme example of constraints leading to opportunities. For example, we saw ETFs trading way below their underlying value – because there was a tsunami of forced selling hitting the market.
Under normal conditions, buyers would have stepped in when the discount hit, say, 1%. “Hey, I can make 1% on this trade? I’ll take that edge all day,” they’d say.
But 2020 wasn’t normal. Risk managers had veins popping out of their foreheads. Nobody was getting approval to deploy capital for a measly 1% edge in the middle of a global pandemic meltdown. Their buying power was severely constrained.
So instead of the discount staying at 1%, it blew out to much wider levels. Maybe you needed an 8% edge before risk managers would even consider the trade.
That’s when serious opportunities emerge – when forced sellers meet constrained buyers.
For a mispricing to persist long enough for you to profit, you need two things:
- Someone forced to trade regardless of price (institutions meeting redemptions, funds being liquidated, regulatory requirements forcing position closures)
- Barriers preventing stronger players from stepping in (the opportunity is too small, in an obscure market, operationally complex, or even the big players are themselves constrained)
When you understand these mechanics, finding edges becomes much more straightforward. You stop chasing random technical patterns and start looking for structural reasons why prices might deviate from fair value.
It’s not particularly scientific or glamorous. Once you identify where your edge is coming from, you simply find the most direct way to exploit it and keep hammering that trade until it stops working.
No need for elaborate theories or complex implementations. Just understand the market mechanics, find the simplest way to capture the edge, and execute relentlessly.
The good trades aren’t hiding in sophisticated indicators or algorithms. They’re hiding in plain sight, in the constraints and forced actions of other market participants.
Summary
There are two types of edges:
- Win-win edges (like risk premium harvesting) where you’re providing a service by taking risks others don’t want.
- Win-lose edges (market inefficiencies) where you’re taking advantage of someone else’s constraints.
The win-win edges tend to be more reliable and persistent (we can be confident they’re not going away), but have a lower return. They’re the foundation of your trading pyramid.
The win-lose edges can be more profitable but typically require more work and eventually get competed away.
A smart trader builds a portfolio with both:
- Use risk premium harvesting as your foundation (bonds, equities, volatility)
- Layer on specific inefficiencies
- Diversify across multiple unrelated strategies
- Size each component according to what you understand about it
In my experience, most beginners spend their time chasing complicated win-lose edges (often ones that don’t actually exist) while ignoring the simpler win-win opportunities right in front of them.
I know I certainly did.
Instead, start by harvesting the obvious risk premia. Then, gradually add inefficiencies as you develop the skills to identify and validate them.
In this game, you really can afford to go slow. The markets aren’t going anywhere.
