To Trend or Not To Trend?
(Wrong Question)
Someone asked me recently whether strategies based on mean reversion, trend following, and momentum are “good” or just data mining. It’s a reasonable question, but it reveals some confusion that arises from mixing up two things that sound similar but are very different.
Mean reversion, trend, momentum: these aren’t edges. They’re labels for how prices move. They describe patterns, not reasons. And patterns without reasons are exactly the kind of thing that gets you into trouble.
However, there’s a link between the pattern and the reason, and once you understand it, everything becomes a lot clearer.
First thing’s first: a pattern is not a reason.
Say you run a scan and find that when a stock drops 5% in a day, it tends to bounce back the next day. You’ve found mean reversion. But you haven’t found an edge. Not yet.
An edge requires a why. Who is on the other side? What’s causing the pattern? Is there a structural reason it persists?
Without that, you’ve got a statistical regularity that could be real, could be noise, and no way to tell the difference. A good edge will have the stats on its side, sure. But so will countless non-existent ones, because financial data is noisy and finite. You need the 1-2 combo of a plausible “why” and supportive data. Neither is sufficient on its own.
The good news is that the “why” is often not that hard to find, and it gets easier to identify as you do more of this stuff. Do it enough, and it becomes second nature.
So when someone says “I trade mean reversion” or “I’m a trend follower,” the right question is: why do you think this particular thing mean-reverts, or trends? The answer to that question is your edge. The pattern is just the implementation.
Here’s a mean reversion example to make it a bit more real.
A stock dropping 5% might mean-revert for very different reasons:
Someone got margin called. A leveraged fund hit its risk limits and had to dump stock, regardless of price. That’s forced, price-insensitive selling. The move had nothing to do with the company’s value, so you’d expect it to bounce when the dust settles. Your edge here is identifying price-insensitive forced sellers and trading against them. Not the specific forced sellers of course... but places where this sort of thing tends to happen on average.
A leveraged token rebalanced. On some crypto exchanges, leveraged tokens mechanically rebalance at a specific time each day. If the market dropped, the token has to sell more futures to maintain its target leverage. If you know when that selling is coming and roughly how big it is, you can trade around it. It shows up in the data too: the bigger the rebalance relative to normal volume, the bigger the impact and subsequent reversion. Your edge is a known, predictable, price-insensitive flow at a specific time.
Wealth managers rebalanced. A big chunk of the wealth management industry holds something like a 60/40 stock-bond split. When stocks outperform bonds, they sell stocks and buy bonds towards month end or whenever they rebalance to get back to target. The opposite when bonds outperform. These are massive, systematic, fairly predictable flows. Your edge is trading against them: buying what they’re selling, selling what they’re buying, around month end. Again, you’re not identifying a specific institution doing price-insensitive rebalancing... you’re just positioning yourself to take advantage of something that happens on average.
Three mean-reverting patterns… each with a different “why.” Once you see it that way, the path forward for each trade becomes much clearer. Scanning for “stuff that dropped and bounced” would lump them all together. Asking “why did this drop?” separates signal from noise.
The trend case is a little trickier and a little murkier.
Trend effects don’t fit as neatly into the “find the price-insensitive counterparty” framework. I find trend a harder pitch than flow-based stuff.
The standard explanations go something like: investors under-react to new information, or they anchor to old prices, or structural barriers mean information takes time to propagate. Early movers recognise new information, push price up, then slower participants figure it out and pile on, pushing price further, until it’s trading where it should be.
These are plausible stories. And there’s data to support them. You tend to see trend effects in markets where fair value is hard to pin down and there’s no obvious anchor for what something should be worth. Crypto is a good example. If everyone forgot the Bitcoin price and we asked them what it should be, there’d be zero consensus. It’s fragmented, hard to value, heavily retail, traded with lots of leverage. The conditions for trend effects to persist appear to be in place. And we do see them in the data, across a bunch of crypto assets.
Contrast that with the E-mini S&P 500, where you’ve got the world’s most sophisticated, well-capitalised, fast-moving participants all competing to price it correctly. You’d expect much less trending there. And that’s generally what we see.
Trend is harder to be confident in than flow-based edges. With the rebalancing game, I can tell you who’s trading, why they’re trading, and approximately when. With crypto trend, I can tell you a story about why it might exist and show you data that’s consistent with it, but the mechanism is fuzzier. So I trade trend effects with less conviction and I’m prepared for them to disappear. That’s just trading the hand you’re dealt (you can’t force a trade to do what you want it to).
Now, you might read all that and think your edge hypothesis needs to causally explain every price movement in a market. It doesn’t.
Your “why” doesn’t need to be complicated.
“Wealth management tends to hold stocks and bonds, and rebalances towards month end by buying the underperformer and selling the outperformer” is more than fine as an edge pitch. It doesn’t explain every tick in SPY or TLT. But it gives a solid reason for a noisy effect that happens on average at the margins. And that’s a reasonable bar.
In Trade Like a Quant, I talk about elevator pitches. You should be able to describe your edge so that a sceptical ten-year-old would go “yeah, that seems reasonable, I can see why that would make money.” Three sentences:
1. What causes the inefficiency? (Who’s trading, why, and why are they price-insensitive?)
2. Why can you exploit it? (Why hasn’t someone faster or smarter eaten all of it?)
3. How might you harness it? (What would you actually do?)
If you can answer those, you’ve got something worth testing. If you can’t answer them yet, that’s fine. The questions themselves point you in the right direction.
Back to the original question. Is building a mean reversion or trend strategy data mining?
It depends entirely on the order you do things.
If you start by screening for patterns, find something that looks good in-sample, and then bolt on a post-hoc explanation: that’s data mining. The “why” you come up with after the fact tends to be exactly as convincing as it needs to be to make you feel good, which isn’t the bar you want.
If you start by understanding why a particular price-insensitive flow exists, formulate what you’d expect to see in the data if your hypothesis is correct, and then go look for it: that’s research. The pattern backs up the hypothesis. The hypothesis came first.
And yeah, sometimes you notice something in the data first. That happens. Don’t sweat it. The market doesn’t care if you followed the scientific method perfectly. But be a grown up about it. Don’t kid yourself with bullshit backwards rationalisations. You still need to stop and answer the “why” before you trade it. Not as an afterthought. As a load-bearing part of the argument. Take it seriously.
Mean reversion and trend following are descriptions of price behaviour. Whether your strategy is edge or data mining depends on whether you can explain, simply and honestly, who’s paying you and why. And once you start asking that question, you’ll find the good trades start to separate themselves from the noise surprisingly quickly.
