[btheshoe]

ya so notice the pattern before anyone else and extract money by filling the inefficiency. this isn't a profound insight, the nature of alpha is that it's temporary and limited. doesn't mean it doesn't exist and you can't take advantage of it - as long as you're better than everyone else (faster, smarter, cheat-ier). tbh it's just like entrepreneurship - starting a business will attract competitors. but you can just outcompete them.

[mg]

You don't know if "anyone else" already noticed the pattern. That does not reflect in the historical prices you can see. It will reflect in future prices which you can't see. And waiting for those does not help either. You are always at square one. Because others are sitting somewhere, watching the data and thinking about it just like you.

[beagle3]

They don’t all notice a long time pattern at exactly the same time. Alpha decays over time as more people notice and exploit it, or the market slowly changes.

When alpha disappears overnight, it’s almost surely for reasons unrelated to other statistical event players - e.g. a tectonic shift caused by some bankruptcy, interest change, political decision, etc.

However, many traders do not know how to properly model and backtest. It works on backtest, fails in the real world, and they explain that “alpha is gone” when in reality it wasn’t there to begin with - it’s just that their backtest was bad - usually overfit or unrealistic assumptions.

[mg]

You describe a world where you can watch the pattern slowly fade out and stop when it is gone.

But in reality, there is noise.

Say you start exploiting the pattern. You buy on Tuesday and sell on Friday. After 3 weeks of doing so, you lost money every time. Is the pattern gone? Or is this just statistical noise? Should you stop or plow through? You don't know.

Another way to look at it: We would have the exact same discussions if stocks prices were just random walk series.

To make a point in favor of pattern arbitrage, one would have to show that stocks differ from random walk series. Enough to be worth trading against this difference. As far as I know, nobody ever came up with a good argument in favor of this assumption.

[beagle3]

The way to tell signal from noise is with enough statistics. That’s one benefit HFT has over “traditional” trading - it gives you thousands of data points per day. The other way to quickly get thousands of data points per day is to trade slowly but across many assets (which is a much harder game, granted).

Either way, if you know what you are doing, your backtest should also give you a good idea of the variance, and that should tell you if a 3 week loss is statistically probable or not. Personally, I guess I’d stay away from such strategies - I’d prefer a much lower alpha with much lower variance - so that I have effective feedback from the market.

Some firms, e.g. RenTech and Virtu , manage to have very consistent alpha. You haven’t seen a good argument because people who make money don’t care to convince you.

[Galanwe]

> You describe a world where you can watch the pattern slowly fade out and stop when it is gone.

Yes, this is a phenomenon that happens very often during alpha research. You discover something that is decaying already, and you join the wagon until there is no anomaly to correct anymore, at which point you should have found other wagons to join. It's an eternal race of finding new alphas while your previous ones decay.

The rest of your argument doesn't really make sense. You seem to be just against any form of statistical inference.

"Alpha" is called like that on purpose because it is _not_ noise anymore. If you regress it against your benchmark, you should definitely see a difference between alpha and epsilon, given you have enough points to reach statistical significance.

> one would have to show that stocks differ from random walk series

There is easily 40 years of litterature on the subject. You can convince yourself in 5m by running a PCA of stocks returns against beta, sector and country. Then you can run a second round of PCA of these residualized returns against momentum, size, value and quality. Quants funds find alpha against these latter residualized returns.