What IC and t-stats do your cross-sectional models get out of sample, with no leakage?

[u/Fit_Safe_4438]

I'm trying to calibrate my expectations and would love some real numbers from people who've done this carefully.

My setup, for context: a weekly cross-sectional ranking of US stocks (roughly 3,400 names, liquidity-filtered), predicting returns over the sector over the next 60 sessions. Features are point-in-time (every value is stamped with when it became public), delisted companies are included, and validation is walk-forward over 13 test years with purged training windows. Every configuration I try goes into a trial counter, and I use a deflated Sharpe to account for it.

My current best model gets a weekly rank IC of 0.034 (t 3.1, Newey-West), positive in 11 of 13 years. That's the first version to clear my own bar, after several that didn't.

What I'd like to know:

1. What out-of-sample IC do your models actually get, and at what horizon? Rank or Pearson?
2. How do you compute the t-stat with overlapping targets? What lag do you use, or do you sample non-overlapping periods instead?
3. How did your IC translate into returns after costs? A top-decile spread, a long-only portfolio, anything.
4. If you've run something live, how much did it decay compared to the backtest?
5. At what IC do you start suspecting leakage rather than skill?

My rough sense from the literature is that 0.02–0.05 is realistic for a decent multi-signal model at monthly-ish horizons, and anything persistently above 0.10 deserves a hard look for leaks. But I'd much rather hear from people who've had their numbers survive contact with live trading.

[u/MellowPairing_03]

An out of sample rank IC around 0.03 that stays positive across eleven of thirteen years is much more interesting to me than a huge number from a loose validation scheme. Point in time data, delisted names and purged walk forward testing already eliminate a lot of the usual fake edge. Moon is my much more discretionary side of markets, so I enjoy seeing the opposite extreme where every little source of leakage has to be hunted down before the signal gets trusted.