The internet has a
returns problem.
Every day it produces thousands of investment claims — strategies, signals, systems, "proven" methods. Almost none of them come with evidence. We're building the place that asks for it.
The loudest claims travel furthest
Social feeds reward confidence, not accuracy. A bold promise spreads; a careful caveat doesn't. So the promises keep getting bolder.
Archetypes, not quotes — but odds are you've scrolled past all six this week.
This isn't cynicism. It's measurement.
People have been counting for decades — funds, traders, forecasters, influencers. The numbers are remarkably consistent:
Professional US large-cap funds that trailed the S&P 500 over the 15 years to 2024. Nine in ten — with research teams and Bloomberg terminals.
Day traders who lost money among those who persisted 300+ sessions in Brazil's futures market. Only 1.1% out-earned minimum wage.
Average accuracy of 6,582 public market forecasts by 68 named gurus, tracked over eight years. A coin manages 50.
What happens to a trading edge once it's published. Across 97 academic strategies, returns dropped by more than half. Markets eat public edges.
Financial influencers who are measurably anti-skilled — following their advice costs about 2.3% a month. The kicker: the anti-skilled ones have more followers than the skilled ones. The algorithm can't tell the difference. Neither can your feed.
If nine in ten professionals can't beat a simple index fund over 15 years, the stranger with the rented Lamborghini probably can't either.
Why the nonsense keeps winning
Not because everyone is lying. Because the system that surfaces claims never checks them — and three quiet forces do the rest.
Survivorship bias
You only hear from winners. Start 1,000 accounts and luck alone makes a handful look brilliant. They post screenshots; the rest go quiet. The screenshot is real. The odds aren't.
Backtest alchemy
Test enough strategies on the past and one fits it perfectly — by accident. That's the one that gets packaged and sold. Out of sample, published edges lose over half their returns.4
Upside-down incentives
When the product is a course, confidence pays better than accuracy. If a strategy truly printed money quietly, it wouldn't need a checkout page.
So we do the unsexy part: we check.
One claim at a time, in public, with our work shown. The method is old-fashioned on purpose:
Catch the claim
Write it down exactly as made. "This strategy returns 1% a day." No strawmen, no paraphrasing it into something easier to knock over.
Ask what evidence would prove it
A real track record. Fees and slippage included. Out-of-sample. Risk-adjusted. All trades — not the highlight reel.
Grade it in public
We publish what we find, whichever way it points. When a claim holds up, we'll be the first to say so. Extraordinary evidence is rare — not impossible.
One thing we're not: advisors. ProofOfReturns is educational — we check claims. No buy/sell calls, no price targets, no portfolios.
What you'll never see here
Be there when the checking starts
The first checks are in the works. Leave your email and they'll land in your inbox — evidence, receipts and all.
No spam, no sharing — unsubscribe anytime.
Sources — checked, obviously
- S&P Dow Jones Indices, SPIVA U.S. Scorecard, Year-End 2024 — 89.5% of US large-cap funds underperformed the S&P 500 over the 15-year period ending Dec 2024.
- Chague, De-Losso & Giovannetti (2020), "Day Trading for a Living?" — of Brazilians who day-traded equity futures 300+ sessions (2013–2015 cohort), 97% lost money; 1.1% earned more than minimum wage.
- CXO Advisory Group, "Guru Grades" — 6,582 public US stock-market forecasts by 68 experts (2005–2012); average terminal accuracy 47.4%.
- McLean & Pontiff (2016), "Does Academic Research Destroy Stock Return Predictability?", The Journal of Finance — across 97 published predictors, returns were 26% lower out-of-sample and 58% lower post-publication.
- Kakhbod, Kazempour, Livdan & Schürhoff (2023), "Finfluencers", Swiss Finance Institute — 56% of finfluencers are "antiskilled" (−2.3% monthly abnormal returns when followed); antiskilled finfluencers have more followers than skilled ones.