Enter any ticker and it estimates what one share is actually worth — not what it's trading at. It pulls the company's live financials, projects the cash it should generate, and discounts that back to today (a discounted-cash-flow model), then sense-checks the answer against what similar companies trade for.
You get a fair value under bear, base and bull cases, the upside or downside versus today's price, and a 1–100 opportunity score weighing valuation, quality, growth, financial health and momentum. It also shows what growth the current price already implies, so you can judge whether the market's expectations look reasonable. Every assumption is visible at the bottom and you can change any of them and re-run.
A model, not advice — the output is only as good as its assumptions, so treat it as a starting point for your own research rather than a verdict.
Every headline number on this site, the threshold it was judged against, and how each one came out — including the bars it cannot clear and the ones it cannot score. Nothing on this page is typed by hand — it is read from the backtest artifact and the placebo study in the repository, so it cannot quietly go stale.
$10B large-cap tier, score-weighted, 8% position cap, 0.30 no-trade band. This is the book the site actually serves, and these are its own backtested figures — net of modelled trading costs.
These figures are measured on the corrected 9,645-name point-in-time panel, 69 quarterly dates (2009-03-27 to 2026-04-09). The previously published figures came from the 2,531-name point-in-time panel, 69 quarterly dates (2009-01-15 to 2026-01-28), and the two panels share ZERO rebalance dates — the grid is derived from the universe's own trading calendar, so widening the universe 3.8x shifts every date. So each figure moved under a universe change AND a grid change, and the two are not separated.
The panel's forward returns carry no dividends, so dividend income is absent from the return and dividend tax is absent from the after-tax figure. That understates a real taxable investor's drag, most of all for a large-cap book.
Source: INDEX-BOOK,
2026-10-07 —
data/free_analysis/INDEX_BOOK_CORRECTED.json.
The figures above are a backtest. This is the live record, and it is 42 recorded days long.
The obvious objection to any backtest is that the machinery itself manufactures the result. So we destroyed the signal and kept the machinery: within each rebalance date the scores were randomly reassigned between companies — preserving the distribution, the missing data and the correlation between themes, destroying only the link between a company and its own score — and the whole pipeline was run again, 100 times, on seeds 1000..1099.
Every alpha below is gross of trading costs — the same basis as the noise runs it is compared against, which is what makes the comparison fair. The net figure is in the costs section.
Grey is where those 100 deliberately worthless runs landed. The dark line is the real result.
All 4 of these beat every single noise run outright — not one worthless signal matched the real result on any measure. That is a stronger reading than the one this page carried before, and it is worth exactly as much as the three sentences that follow it, so read them.
The noise runs shown are the 84 of 100 that did NOT adopt a tuned weighting, matched to the deployed flat-weight book this page is about. The 16 that adopted are excluded rather than pooled, because adoption is worth about +1.4 of a t-statistic even on a signal known to be worthless, and pooling them would move the bar.
This is not the same measurement that used to be on this page. It is the same model and the same code on a wider universe — 9,645 companies instead of 2,531 — and because the rebalance calendar is derived from the universe's own price history, the old and new panels share 0 of their 69 rebalance dates. So the figures moved under a change of universe and a change of dates, and this project has measured that the dates alone can move the long-short t-statistic by 0.814 (2.703 to 3.517 across seven equally valid calendars on one universe). The result above is the right one for this universe; what is not separable is why it moved.
PLACEBO_CORRECTED.json,
which retains all 100 draws
(pre-registered as PREREG_ma19_ma13_recalibration.md).
The "noise bar" is the 95th percentile of those draws. A tie counts against us: a noise run
that merely equals the real result is counted as having matched it.The top-tenth ranked by our score, rebalanced quarterly, held for one quarter, equally weighted. Measured against three different comparisons rather than the single most favourable one.
This is the RESEARCH DECILE (all caps, equal-weighted), not the Valquo Index. The Index holds a $10 billion large-cap tier, score-weighted with an 8% position cap and a no-trade band. Those are different books and they do not earn the same thing: the Index's own measured figures are on the Index tab, and they are lower.
| Benchmark | It returned | Top decile | Difference | t | Quarters ahead |
|---|---|---|---|---|---|
| equal-weight universe (incumbent) uninvestable — you cannot buy this every name in the panel, cost-free — uninvestable, the number every historical alpha figure in this project used |
14.3% | 20.4% | +6.07% | 4.41 | 70% |
| cap-weighted panel average
closest investable analogue buildable from the panel itself |
14.9% | 20.4% | +5.45% | 2.03 | 59% |
| SPY total return over the same windows
what the user's obvious alternative actually returned |
16.4% | 20.4% | +4.02% | 1.33 | 54% |
All figures annualised and gross of trading costs. The equal-weight universe is listed first because every historical figure in this project was measured against it — and it is the hardest of the three, because an equally weighted basket of every company in the panel, charged nothing to trade, beat the S&P over this window. The SPY row is the one that answers "versus just buying the index".
And net of trading costs this book does not beat SPY. The 4.02% above is gross; the measured cost of running it is 4.29% a year, which leaves -0.27% — and a separate measurement of the same book through the Index machinery puts it at -1.14 pp a year. Two different constructions, two different numbers, both negative. The gross figure also carries a t of 1.33, so it is not separable from zero in the first place. Nothing on this site claims to beat SPY.
A top decile can look good by luck. The question is whether the whole ranking orders returns. Annualised return by score decile, best-ranked first.
It sorts, and imperfectly — deciles 4 through 7 are effectively tied and one is out of order. The ordering statistic is -0.964, where −1.0 would be a perfect ladder. What carries the result is the top decile and the bottom two; the middle is noise.
An annualised average is one number standing in for 69 quarters, and quoting it alone is how a strategy that is often behind gets described as though it were always ahead.
"Net of costs" asks you to believe our cost estimate. The breakeven does not: it is the cost at which the edge reaches exactly zero, so you can compare it to whatever you think you would really pay.
After costs the top decile's edge over the equal-weight universe falls from 6.83% to 2.54% a year.
4 standard thresholds. For the book this site actually runs it clears 2 of the 3 that can be scored on it, and the other 1 cannot be scored on it at all — they are shown as unmeasured rather than dropped. All of them are here because a page that showed only the passing ones would be advertising, not evidence.
| Threshold | Result | Bar | |
|---|---|---|---|
| Long-short t vs. this project's own placebo floor The 95th percentile of 100 shuffled-signal runs. Beating it means the result is bigger than 95 out of 100 runs on a signal known to be worthless. This bar is re-measured on the SAME 9,645-name panel as the result, from the 84 shuffled runs above that did not adopt a tuned weighting. The artifact still carries the previous panel's figure (2.2837), which is not the bar for this one. |
4.594 | 1.364 | clears |
| Long-short t vs. the Harvey-Liu-Zhu multiple-testing hurdle A bar that rises with the number of tests you have run. We have run a lot. On the previous 2,531-name universe this was the headline's clearest FAILURE (2.62 against 3.29); on the corrected 9,645-name universe the same model clears it, so both bars now pass at once. That is a restatement rather than a new result -- the same composite on a wider universe and a rebalance calendar that shares no dates with the old one -- and the artifact records both sides of the argument either way. CLEARS the bar derived from counting its own trials, and CLEARS the bar measured against a shuffled-signal placebo. The trial-counting bar prices the best of N attempts, and the model this site runs is flat-weighted and was never tuned, so the tests counted are overwhelmingly alternatives that were rejected. One qualification: on this universe the selection step did prefer a tuned weighting. The flat weights are kept anyway, because the tuned book earns less and because nothing is adopted without the owner approving it. The run's own stored wording for this is out of date and is not shown: it says the headline fails the trial-counting bar, and this run clears it; it says no weighting was ever adopted, and this run adopted one. The figures above are the run's; only that sentence was hard-coded, and the code that writes it now derives it. |
4.594 | 3.362 | clears |
| Probability of backtest overfitting UNMEASURED FOR THE BOOK THIS PAGE IS ABOUT, and the number beside it is not a pass to quote. This statistic scores the step that CHOOSES between the eight candidate weightings -- it never reads which one was adopted, so it returns the same figure either way. The book this site runs chooses nothing: its weights are flat and were never tuned, so there is no selection for this measure to score and it is not evidence about the deployed book in either direction. Re-measured for the deployed book on 2026-10-13 it comes back as a pure ranking of the eight schemes rather than a probability, so it is reported as unmeasurable rather than filled in. The bar is close to useless here as well: on this panel's own shuffled runs 41 of 100 worthless signals 'pass' it. Shown rather than dropped, because a measure we cannot score for the deployed book should be visible as unscored. This figure describes the SELECTION STEP over the eight candidate weightings -- not any one book, and not the deployed flat-weight book. |
0.0 | 0.5 max | unmeasured |
| Deflated Sharpe ratio THE NUMBER BESIDE THIS IS NOT THE BOOK THIS PAGE IS ABOUT. It is computed on whichever weighting the selection step chose, and on this universe that is a tuned one rather than the flat weights the site runs -- the two differ by a factor of about 462, so the figure beside it is not evidence about the deployed book in either direction. The deployed book's own figure WAS measured on 2026-10-13: 0.72 against the conventional 0.95 bar, which it FAILS, and against this panel's own shuffled floor, which it CLEARS on either reading of that floor -- 0.59 across all the shuffled runs and 0.57 across the ones whose weights were left flat, as the deployed book's are -- exactly the relationship the previous universe's 0.79 had to the same two bars, so the finding is unchanged and only the figure moved. Most of that move is this project's own trial counter rather than the data: about two thirds of it is the count of tests rising from 224 to 297, which raises the bar the statistic deflates against. This figure describes the CPCV-adopted book; the deployed book's own reading is stated beside it and was measured separately. |
0.002 | 0.95 | fails |
The reason a backtest is weak evidence is that you can keep trying until something works. The defence is to write down every attempt, including the ones that failed, so the successes can be discounted by how many shots were taken. Every test below was registered in writing — hypothesis, threshold and direction — and committed to version control before it was run.
None of these were discovered by an outside auditor. They were found, written down and published by the project itself, which is the only reason you are reading about them.
Four things you can verify without any special access, in rough order of how much effort they take.
Every figure above is read at request time from
BACKTEST_RESULTS.json and MA19_RECALIBRATION.json. Nothing is
typed into this page. The run is stamped
01b1b83, dated
2026-10-09 — if that date is old, these numbers are old,
and this page will say so rather than quietly refresh the label.
The SPY row above says the S&P returned 16.4% a year over 2009-03-27 to 2026-04-09. That is a public number you can look up in thirty seconds. If our benchmark is wrong, everything measured against it is wrong, and it is the easiest thing on this page to falsify.
The usual killer is look-ahead: scoring a company in 2012 using a number that was not published until 2013. This panel is rebuilt as of each date from filings with their real filing dates, prices with no future adjustment, and institutional holdings lagged by their actual disclosure deadline — and companies that went bankrupt or were delisted stay in rather than being dropped. The How it works page explains the construction and the specific places it is still imperfect.
And one correction to what this page used to say here. It claimed the record was "not a survey of survivors". Dead companies' returns were always kept — that part was true — but the list of companies was itself a 2,531-name subset of a 9,645-name export, and nobody had checked what selected it. It has now been re-run on all 9,645. The headline got worse (top-decile alpha 7.17% → 6.07% a year, both gross of costs so the two are like for like), which is the direction that tells you the check was worth doing. Every figure on this page is from the wider universe.
Each one was registered before it ran, with the threshold written down first, and the verdict recorded whichever way it came out. That record is what makes the 307 figure above meaningful rather than decorative — a count of attempts is only honest if the failures were logged at the time rather than reconstructed afterwards.
The most convincing test this project has run is an out-of-sample replication on international data: the same untuned model, mapped onto a different vendor's dataset, different countries, a different construction and a different period. It works there, and the United States is the weakest region tested. Those figures are derived from a dataset licensed for non-commercial research only, so they cannot be published on a product page. That is a licensing constraint, not a hedge — and a page claiming to show proof should say what it is leaving out and why.