How the Opulence Alpha studies are measured: data and methods
Opulence Alpha Research · Published Sep 25, 2026 · Data through Aug 21, 2026
Every Opulence Alpha study measures one claim about US stocks the same way: on one universe of 1,767 companies across 11 sectors, every Wednesday from Jan 4, 1995 to Aug 19, 2026, against the same day's median stock, so that chance is 50 in 100 and a market-wide rally cannot flatter a result. A relation is called proven only when its t-statistic on non-overlapping dates reaches 3 and it holds in most years. This paper sets out the data, the methods and their limits.
1Purpose and scope
Popular signals make claims about what a stock does next: a chart pattern, an oversold RSI, heavy short interest, an insider's purchase. The studies measure those claims on one dataset, with one method and one bar for calling a relation proven, so that the answers can be compared with each other.
The studies describe tendencies across hundreds of stocks. None of them forecasts a single stock's price, and none is investment advice.
2Data
The universe is 1,767 US-listed common stocks. It was fixed when the platform was built and has not been re-sampled since. It has two layers. The first is every company that belonged to the S&P 500 at any time since 1996 and whose price history could be recovered: 1,027 companies, 413 of which have since delisted. They enter with their full history, which largely removes survivorship bias from the large-company layer: on 30 June 1996 the universe held 76% of the index's members with prices, and from 2010 on at least 96%. The 137 historical members it lacks are mostly companies that left the index before 2010 (119 of them) and whose prices could not be recovered. The second layer is 740 companies outside the index, chosen at construction to follow the market's sector proportions, mid capitalisations included, subject to minimum price and trading-value screens.
Measured today, the universe reflects that design. By latest market capitalisation, 55% of the 1,338 active companies are large (at least $10bn), 38% mid ($2–10bn) and 6% small. Its sector mix is close to that of the S&P Composite 1500 (the S&P 500, MidCap 400 and SmallCap 600 together): 7.9% of companies would have to change sector for the two to match exactly (Table 1). Sectors follow each company's current GICS classification throughout its history.
Of the 422 companies that have delisted, 302 left through a merger or acquisition, 31 through bankruptcy, 38 continued under a successor ticker and 51 for other or unrecorded reasons, as classified from their SEC EDGAR filings. Because the non-index layer was chosen from companies listed at construction, only 9 of its 740 companies have delisted, against 413 of 1,027 in the index layer: smaller companies that failed or were acquired earlier are under-represented. Section 5 therefore repeats each lead result on the stocks that were S&P 500 members on each date, the subset free of that bias.
Companies ever in the universe; size by latest market capitalisation (active companies); share of the study's stock-weeks; share of S&P Composite 1500 constituents.
| Sector | Companies | Delisted | Large | Mid | Small | Stock-weeks | S&P 1500 |
|---|---|---|---|---|---|---|---|
| Information Technology | 283 | 67 | 122 | 76 | 18 | 13.9% | 12.7% |
| Industrials | 257 | 53 | 126 | 67 | 11 | 15.9% | 17.5% |
| Financials | 256 | 60 | 114 | 74 | 8 | 14.6% | 17.2% |
| Health Care | 237 | 47 | 92 | 81 | 16 | 12.4% | 10.9% |
| Consumer Discretionary | 209 | 39 | 69 | 87 | 12 | 12.3% | 12.9% |
| Energy | 105 | 30 | 39 | 31 | 5 | 5.9% | 4.7% |
| Consumer Staples | 101 | 38 | 40 | 17 | 5 | 5.9% | 4.9% |
| Materials | 95 | 34 | 35 | 24 | 2 | 5.6% | 5.1% |
| Real Estate | 84 | 10 | 40 | 32 | 2 | 5.7% | 6.9% |
| Communication Services | 80 | 31 | 28 | 13 | 5 | 3.9% | 3.3% |
| Utilities | 60 | 13 | 35 | 12 | 0 | 4% | 4% |
| All sectors | 1,767 | 422 | 740 | 514 | 84 | 100% | 100% |
Large ≥ $10bn, mid $2–10bn, small < $2bn, for the 1,338 active companies with a recorded capitalisation. S&P 1500 shares count the constituents of the S&P 500, MidCap 400 and SmallCap 600 (1,506 companies; constituent lists read Sep 25, 2026). Delisting reasons from SEC EDGAR filings.
The studies observe the universe every Wednesday from Jan 4, 1995 to Aug 19, 2026: 1,634 Wednesdays spanning 7,959 trading sessions. Wednesday keeps one observation per week away from the start and the end of the trading week; weeks whose Wednesday was a market holiday (17 of 1,651) are left out. A stock enters a Wednesday's cross-section when it has the signal and a measured outcome. The cross-section grows from a median of 832 stocks in 1995 to 1,331 in 2026 (median 1,148, range 820 to 1,371; Figure 1). In all the sample holds 1,889,758 stock-weeks from 1,763 companies; 331,954 of them (17.6%) come from the 422 companies that have since delisted.
The formation studies observe the universe every Wednesday from Jan 4, 1995 to Aug 19, 2026: 1,550 Wednesdays spanning 7,959 trading sessions. They leave out the weeks the platform's regime model flags as a likely change of market regime, when a formation's window straddles two regimes; with market holidays, 101 of 1,651 Wednesdays are left out. They hold 1,361,444 formation detections, one per stock and Wednesday, from 1,764 companies; 217,945 of them (16%) come from the 420 companies that have since delisted. The median Wednesday has 906 stocks with a formation, from 452 in 1995 to 1,065 in 2026 (Figure 1).
Median number of stocks per Wednesday, by calendar year.
The outcome is the stock's return from the close on the signal's Wednesday to the close 1, 5, 21 and 63 trading sessions later, roughly a day, a week, a month and a quarter. Closes are adjusted for splits and, where the source records them, for dividends. Sessions follow one market-wide calendar, which matches the NYSE's trading days exactly from 1980 to 2026 (11,755 sessions). Outcomes start in 1995 because adjusted closes before then are unreliable in the source data, and run through Aug 21, 2026.
Each return is compared with the same Wednesday's cross-section: with the median stock for hit rates, and with the equal-weighted average for average excess returns. Rank correlations are unaffected by either. Returns are not winsorised or capped. A return that would run across a price bar flagged as bad data is left out rather than filled, and no delisting return is added: a company's outcomes end at its last trading day.
The outcome is the stock's return from the close on the detection's Wednesday to the close 5, 21 and 63 trading sessions later, on split-adjusted closes, in excess of the S&P 500 (the SPY fund) over the same sessions, as recorded by the pattern detector and capped at plus or minus 100%. Each detection uses only price bars up to its own day: a turning point is recognised only once later bars have confirmed it, never with hindsight. A bullish detection counts as a hit when that excess return beat the same Wednesday's median stock; a bearish one when it trailed it. Half of all stocks beat the median by construction, so chance is 50 in 100 on every date.
The detector also records whether the price reached the formation's drawn target before its stop, on daily closes, within 21 sessions for bullish formations and 42 for bearish ones; a stop or a timeout counts as a miss. Outcomes run through Aug 21, 2026. No delisting return is added.
3Methodology
Each Wednesday the cross-section is ranked on the signal and cut into five equal groups. For each group the study records the share of its stocks whose return beat the same day's median stock, and its average return in excess of the equal-weighted average. Every Wednesday counts once, whatever its size; the reported figure is the average over Wednesdays. Because half of all stocks beat the median by construction, chance is 50 in 100 on every date, whatever the market did.
On each Wednesday the study computes the Spearman rank correlation between the signal and the forward return, over the stocks that have both, with tied values given their average rank; a Wednesday needs at least 50 such stocks. The reported information coefficient (IC) is the average over Wednesdays (Grinold and Kahn 2000). A positive IC means higher readings were followed by relatively better returns; a negative one, relatively worse.
A zone is a reading traders act on, such as RSI(14) below 30. Each Wednesday the stocks in the zone are scored against the same day's median stock. A state (RSI below 30, price above the upper Bollinger band) counts on every Wednesday the stock is in it; an event (a MACD or moving-average cross, a new 10-day high) counts only when it happens on the Wednesday itself.
A formation is scored on the Wednesday it is detected. Its hit rate is the share of detections whose excess return beat (bullish) or trailed (bearish) the same Wednesday's median stock; the edge is that rate minus 50. Bullish and bearish formations are never pooled.
The 95% interval is clustered by date: the pooled rate plus or minus 1.96 times the standard deviation of the per-date edge, divided by the square root of the number of non-overlapping dates. Formations detected on the same date share that date's market, so treating each detection as independent would overstate precision.
Horizons longer than a week overlap from one Wednesday to the next, so consecutive dates are not independent. The t-statistic therefore uses non-overlapping dates only: every Wednesday at 1 and 5 sessions, every fifth at 21 and every thirteenth at 63 (1,634, 327 and 126 dates). It is the average of those per-date values divided by their standard error.
A relation is stable when its yearly average has the overall sign in at least 60% of the calendar years with enough data (ten or more Wednesdays). Table 2 gives the verdicts every study uses.
| Verdict | Rule |
|---|---|
| Proven | |t| ≥ 3 on non-overlapping dates, the same sign in at least 60% of years, and large enough to matter: |IC| ≥ 0.01, or a hit rate at least 1 point from 50 |
| Faint | |t| ≥ 2 but short of one of the other conditions |
| No measurable edge | |t| below 2: measured, and no measurable edge |
| Not enough history | Too little history to measure |
4Multiple testing
Across all studies, 1,470 t-statistics were computed: 828 rank correlations, 160 indicator zones, 57 formation outcomes and 425 formation conditions. If no signal worked, about 67 would reach |t| ≥ 2 and about 4 would reach |t| ≥ 3 by luck alone; 336 and 176 did. Many of the tests are correlated (the same signal at four horizons, related signals), so those counts are a guide, not an exact rate. The |t| ≥ 3 bar follows Harvey, Liu and Zhu (2016). As a cross-check, a Benjamini–Hochberg control of the false-discovery rate at 5% keeps 190 results (|t| ≥ 2.72); every result called proven here clears it.
| |t| ≥ 2 | |t| ≥ 3 | |
|---|---|---|
| t-statistics computed | 1,470 | 1,470 |
| Expected to pass by luck alone | 67 | 4 |
| Passed | 336 | 176 |
5Robustness
The lead relations are re-measured four ways: by decade; by the market's volatility regime on the date; inside each GICS sector, with stocks ranked only against their own sector; and among the stocks that were S&P 500 members on each date, a median of 404 per Wednesday. That last subset largely avoids the survivorship bias described in Section 2.
6Limitations
- Before costs. Averages are before trading costs, taxes and market impact. A relation that is concentrated in illiquid stocks or needs frequent trading may not survive them.
- Same-close timing. The return starts at the close the signal is computed from, with no delay. A trader acting on the signal would enter later; the shortest horizons are the most sensitive to this.
- No delisting returns. A company's outcomes end at its last trading day. Acquisitions usually delist at a premium and bankruptcies at a loss, so leaving both out can bias results for stocks near either event (Shumway 1997).
- Smaller companies are survivors. The non-index layer was chosen from companies listed when the universe was built. Section 5 reports each lead result on point-in-time S&P 500 members, which do not have this bias.
- Equal weight. Every stock counts the same, so the results describe the typical stock in the universe, not a capitalisation-weighted portfolio.
- Recomputed history. Signals are recomputed over the past with today's definitions and data. A relation found this way is a measured tendency, not the record of a strategy run live.
- Data vintage. The figures are frozen as of Sep 25, 2026. Later data corrections can move them slightly; the snapshot is re-measured and re-dated when they do.
7Reproducibility
Every study runs read-only against the platform's recomputed tables, aggregating inside the database so that each figure can be re-derived from the same rows. Its results are frozen, with the date the studies ran and the last trading day their outcomes reach, in one evidence snapshot. The pages quote that snapshot and nothing else; a sentence never carries a number typed by hand.
When the underlying data is corrected, the studies are re-run and the snapshot is re-dated. The date on every page tells which vintage it quotes.
This version: studies run Sep 25, 2026, outcomes through Aug 21, 2026.
The studies
References
- Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57(1).
- Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management, 2nd ed. McGraw-Hill.
- Harvey, C. R., Liu, Y. and Zhu, H. (2016). … and the Cross-Section of Expected Returns. Review of Financial Studies 29(1).
- Shumway, T. (1997). The Delisting Bias in CRSP Data. Journal of Finance 52(1).
Research, not advice. A measured tendency across hundreds of stocks is a nudge for any one of them, never a forecast of its price.
Data availability and citation
Every figure in this paper is quoted from one frozen snapshot, published as JSON with the sample description, the robustness results and the test counts. The same snapshot feeds the research console, so the two cannot disagree.
Opulence Alpha Research (2026). How the Opulence Alpha studies are measured: data and methods Opulence Alpha Studies, Sep 25, 2026. https://opulencealpha.ai/studies/methodology