Under the hood: Adverse Selection

What spreads are made of

May 19, 2026

When I moved to Spain, the apartment I had pre-arranged turned out to be further from the amenities than expected. I needed a car on short notice. So I went to a dealership, where I was shown their selection. Antonio — sales — quickly asked about my price range and started pointing to all the different models on display. Great price, very few kilometers. This one will serve you for decades! The previous owners handed it over in better condition than it came out of the factory!

Knowing less about cars than the average high schooler, I barely paid attention to what was being said, only registering mechanically what was being presented. I watched Antonio grow more and more compelled to impress. By the third loop through the 20-30 cars we’d narrowed down to, I realized there was one he kept skipping over. When I pushed him about it, finally, he admitted it was a “good car.” I closed the deal quickly, used the car for many years, and ended up selling it for more than I had paid for it.

I am not a bargainer, nor do I easily see through all sales tricks. But the words adverse selection lit up in front of my trading-geek eyes. This short story, adverse selection, and lemons all have something in common — and I will show you how.

Actuarial origins

In the last piece we covered three important market participant categories: the informed, the uninformed traders, and the market makers. We also showed that informedness is only spread-deep — that is to say, it can only be defined in relation to the market maker. This explains something intuitive: the market maker is trying to avoid being matched with informed traders, and adjusts their quotes accordingly to achieve a dual purpose. First, to skew the price in their favor whenever they do have to face the Informed. Second, to be matched as often as possible with uninformed counterparties to offset the losses caused by the former.

The market maker being picked off by an informed trader is a phenomenon called adverse selection.

The notion originates outside of finance, in the late 19th century, in the insurance industry. Insurers realized roughly a century and a half ago that if they followed pure statistical probabilities when underwriting claims, they would end up going under imminently. They identified an effect they termed anti-selection, or adverse selection — namely, that the people most willing to purchase their life insurance products were the ones likely to already suffer serious illnesses. The information asymmetry between insured and insurer left the insurer unable to predict claim probabilities correctly. Disaster — pun intended — was hard to avoid without coping strategies.

The same effect plays out with market makers, who may not be privy to the information that informed — or “toxic” — traders possess. So they developed several coping strategies.

One strategy is to stop offering the product to a specific target audience. To stop selling life insurance to those with terminal illnesses. Or to skip over the car that is priced correctly — shoutout to the Antonios and car dealerships of this world. For the market maker, this equates to refusing to quote to select counterparties. Not always readily available in regulated markets, but it very much happens in more fragmented financial marketplaces — selective quoting in CFD and interbank feeds is the obvious example.

Another option is to raise the price. Increase the insurance premium, up the sale price of the car, widen the quote. Enough to make the transaction financially viable. The reader will recognize that both strategies are widely followed in all the cases listed.

About a century after the insurance industry coined the term, Akerlof posited that this information asymmetry can have serious consequences. His example was close to home: sellers of used cars know whether theirs is a car with hidden issues; buyers do not. Rational buyers therefore adjust the price they are willing to pay downwards to take this into account. As a consequence, sellers of good cars withdraw from the market — finding this price too low. After just a few cycles of this we are left with: you guessed it — only Lemons.1

The mother of all costs: the Spread

The year was 2013. The country: Switzerland. The room set up for “Ultimate” Agile: a Kanban board, a sprint-board, a 3-min meeting board, and a room-sized whiteboard with never-ending post-its all pushing back on any and all deadlines. I know my strategy is the basis for the next unicorn, but whenever we test-execute it drifts... — I was told. This was familiar territory. This time it was at the strategy design phase. It works on all instruments, so we decided to start with spot FX. The alarm bells were deafening me. Let’s understand the underlying concept first. Statistical pattern deviation from noise. Ok.

So then the buy signal triggers, he said. And we buy at the bid. I know there is slippage, but...

Throat clearing. It was me, but it should have been the strategy author. I exhaled, inhaled, and again several times — I need to say this in a tone that is neither alarmed, nor surprised, nor demeaning, I thought to myself. What do you mean at the bid?...

The strategy was assumed to execute at the prevailing inside bid and ask, not even at the mid-price. So the discrepancy from the get-go was 2× the spread plus commission, plus slippage. It was a long conversation. And many more followed.

It isn’t controversial to say that the biggest and most significant cost to trading is the spread. What others term slippage is generally just the difference between the spread at the time of the order intent and the time of it arriving to the market. The former is a virtual spread that drives the decision. The latter is the spread and price in effect.

A strategy developer needs to understand exactly what the spread is, and what it isn’t. Why it exists. What it means for a strategy. How it interacts with your system.

The spread is considered to comprise three structural components:

Order processing costs — exchange, settlement, and other transaction fees.
Inventory costs — the price of the maker having to hold excess positions.
Adverse selection — the cost of the maker being picked off by more informed players.

In modern markets, adverse selection accounts for 50-80% of the spread. The other two have been compressed by technology — exchanges run leaner, settlement is cheaper, processing is automated. What is left is the cost of the maker not knowing who you are. So you are paying the cost of what the market maker has to defend against. Namely, their inability to identify you, or any other market participant.

How Adverse Selection reaches all

Adverse selection affects both limit orders and market orders. A market order surrenders price for timing. A limit order surrenders timing for price. The trade-off sounds straightforward.

Most developers I’ve watched come at this from the wrong angle. They treat the order-type choice as a tactical question — limit if there’s time, market if there isn’t. The realization takes a while: the choice doesn’t decide whether you pay the spread. It decides how the spread comes back for you.

The market order’s timing guarantee in particular deserves more scrutiny than it usually gets. It assumes what you see as the market price is not a mirage. That the price won’t move significantly by the time you hit the market. That this instrument’s volatility is within the range you expect on the micro time scales. That is a bigger bet than it appears, which is why most professional and systematic trade designers rarely use full market orders. They use marketable limit orders when they need to be the aggressor.

The deeper observation is that adverse selection affects both order types — through different delivery mechanisms.

On a limit order, you are the one quoting a price. You get filled when someone hits your bid or lifts your offer. But who chooses to hit you? I have some bad news and more bad news. Most likely it will be an informed trader. That means you lose. Assume instead that it is incidentally an uninformed trader that wants to dip their paw into the book while you are there. Do you think the market maker — with state-of-the-art infrastructure, fee structure, incentives, and near-perfect microstructure algorithms — will let you lead the queue to face the uninformed player?

On a market order, you are the one crossing the spread to demand execution. The maker moves their price to protect against you and everyone else trading in your direction. They have to — that is their function. The cost lands on your fill price, and you call it slippage. When there isn’t much slippage, well, it was just too obvious for them that you were not informed to begin with.

Either way, you are paying for the maker’s uncertainty about who you are.

No free order type

The common framing that limit orders capture the spread and market orders pay the spread is simply untrue. Limit orders do not avoid the cost. They transmute it. The spread you didn’t pay shows up as adverse fill selection. The trade-off between price certainty and timing certainty simply becomes: which form of adverse selection you choose to bear.

If you haven’t ever done the exercise to check all-passive (limit) versus all-aggressor (market) PnL for several trading hours, I highly suggest you do it for any market or instrument. What you find will surprise you and make you understand the point viscerally: sometimes the aggressors out-trade the limits, sometimes the other way around. There is no consistency. No determinism.

This matters to you. It sure matters for your strategy. If your signal has urgency, the slower fill rate of limit orders will likely erode it. If you think you can operate without urgency, posting limits could be profitable — but be sure to check your assumptions about infrastructure, costs, and latency, because your competitors are of the most sophisticated type. And overall, if your signal is genuinely informed — predictive of near-term price movement — both order types will cost you, just differently.

Understanding which form of adverse selection your strategy is exposed to, and navigating that interaction deliberately, is paramount if you want to succeed and be consistent in trading.

In the next piece I will turn to a related practitioner discussion — the notion of a good trade and a bad trade, why I used to push back on it, and what changed my mind.

Trading Reality — Markets in Production

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