# Is target\_ender\_60 volatility-scaled before it is binned?

**URL:** <https://forum.numer.ai/t/is-target-ender-60-volatility-scaled-before-it-is-binned/8362>\
**Category:** Tournament\
**Created:** [October 11, 2026, 5:37pm UTC](https://forum.numer.ai/t/is-target-ender-60-volatility-scaled-before-it-is-binned/8362 "2026-10-11T17:37:04Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![pumplerod](https://yyz1.discourse-cdn.com/flex009/user_avatar/forum.numer.ai/pumplerod/32/3553_2.png) [@pumplerod](https://forum.numer.ai/u/pumplerod)\
**Post date:** [October 11, 2026, 5:37pm UTC](https://forum.numer.ai/t/is-target-ender-60-volatility-scaled-before-it-is-binned/8362/1 "2026-10-11T17:37:04Z")

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I’m trying to understand what kind of return target\_ender\_60 ranks, so I know what to expect from models of extreme outcomes (the 0.0 and 1.0 bins).

My understanding: each era, stocks are ranked by a residualised forward return of roughly 60 trading days, then binned 5/20/50/20/5% into 0, 0.25, 0.5, 0.75 and 1. Is that right?

The questions:

1. **Volatility scaling.** Before ranking, is each stock’s return divided by its own volatility (a z-score or Sharpe-like return), or neutralised against a volatility or risk factor? Or is it a plain residual return, so that naturally volatile stocks land in the tails more often?
2. **Residualisation.** Which exposures are removed: market beta, country, sector or industry, size, other style factors? Roughly how are they removed (one regression per era, or a risk model)?
3. **Horizon.** Is “60” trading days, starting from the era date or after a short delay?

Why I ask: on raw S&P 500 prices, a stock’s trailing volatility alone puts about 30% of the most volatile tenth into the top or bottom 5% of residual returns. With Numerai’s features, my models get to about 17%. If the target is volatility-scaled, that gap is by design and nothing is being missed. If not, the features may be carrying less volatility information than raw prices do.

Even a yes or no on question 1 would help a lot. Thanks!

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**Plain version:** I’m asking whether Numerai grades each stock’s move relative to how jumpy that stock normally is. If it does, a jumpy stock doesn’t get an advantage at landing in the extreme bins, and my models are already near the limit.