We ask a single question with unusual discipline: using only free, point-in-time public data, can you predict which stocks or crypto tokens will rise sharply — and if not, what can you reliably do? Across two asset classes and four pre-registered ex perimental lines, with pre-committed gates, sealed consume-once hold-outs, deflated performance metrics, survivorship-aware label construction, and permutation tests, we find a consistent asymmetry. Return / pump prediction has no tradeable edge. An apparent cross-sectional ranking signal collapses under realistic exit rules, transaction costs, and survivorship correction; on a 29-name mega-cap equity backtest the deflated Sharpe is −1. 377 (held-out, GO bar +0. 50) and the strategy underperforms buy-and-hold by 7. 3 per centage points of CAGR. On the broader S the equal-weight composite ranks stocks backwards (IC t = −4. 73). In crypto, a pump classifier achieves AUC 0. 708 and top-decile lift 2. 59× — but every long-only basket loses money at every realistic cost (top-decile −1. 6 / −6. 6 / −11. 6% at 5 / 10 / 15% one-way cost on a 30-day horizon), and labelled “risk flags” (buyₜax, nᵣugflags) carry the wrong sign — they raise measured pump rate, an artifact of survivorship and degenerate-trader behaviour. Forensic defense is real and provable. A crypto rug/scam detector validated at AUC 0. 83 on a 3, 677-token balanced evaluation sample drawn from a 61, 339- label pool (ETH 0. 921 via Honeypot. is, SOL 0. 754 via RugCheck) catches 89 of 100 ETH and 97 of 100 SOL scams at a fixed 0. 5 threshold. A separate price/volume only coin death detector achieves AUC 0. 743 on 1, 267 cached CoinGecko coins (36 died, 1, 231 survived). On the equity side, an abstention-first 3-way forensic screen reduces false-alarm-on-SOUND from 5. 34% to 1. 03% on its diagnosis set and 1. 65% on a held-out date the tool never saw, with a permutation-real recall of 22. 7%–25. 0% on blow-ups (perm p = 0. 0005). The defensive screens are honestly one-sided: they reliably refuse to insult healthy names, but they are weak on clean accounting fraud (full-set fraud recall ~3. 5%) and on growth/biotech blow-ups that lack a balance sheet tell (held-out false-clear 2 of 8, 25%, PRLB/TNDM). We explain the asymmetry mechanistically: ex-ante, big winners and look-alike non-movers are statistical twins on every directional factor (rocket-vs-flop AUC ≈ 0. 70, bullish-factor max |Cohen’s d| = 0. 187 ≈ 0), separable only by a volatility / issuance / illiquidity capacity signature (realized 3-month volatility d = +0. 56). Di rection is set by future catalysts absent from any public snapshot; structural failure leaves verifiable forensic traces in present-day fundamentals. We release all code, figures, and negative results in full
Mohamed Alaya (Mon,) studied this question.