Statistics13 August 2026· 7 min read
Statistics for Traders #13: Information ratio via today's slot-1 CAD Ivey PMI × CADJPY sample — Sharpe against a real benchmark rather than cash, and how it becomes a Welch t-statistic for bucket-filter strategies
Stats for Traders #8 introduced the Sharpe ratio: strategy excess return over cash, divided by strategy volatility. The information ratio replaces “over cash” with “over a real benchmark”. For a bucket-filter strategy on FX event data, the natural benchmark is “trade every event long” — passive equal-weight event exposure. The natural strategy is “trade only when the bucket says so”. IR measures how much filtering the events adds over just trading them all.
Today’s slot-1 anchor is Canada Ivey PMI on CADJPY — the 197-release 5-bucket monotonic walk. The big-beat bucket had 14 releases with mean +6.28p per trade at 15 minutes; long CADJPY on big-beat is a real strategy with a real per-trade Sharpe of 0.488. What’s the equivalent IR against the passive-benchmark of “trade every release long”? Let’s compute it.
The formula
Given a strategy A trading n_A periods with mean return μ_A and standard deviation σ_A, and a benchmark B trading n_B periods with mean μ_B and standard deviation σ_B, the information ratio is:
IR = (μ_A − μ_B) / TE where TE = tracking error = standard error of (μ_A − μ_B) = √(σ_A² / n_A + σ_B² / n_B) ← when A and B are independent samples
The numerator is alpha— the mean amount the strategy beat the benchmark by per trade. The denominator is the standard error of that alpha estimate — how uncertain that number is. Divide alpha by its own uncertainty and you get a t-statistic-shaped ratio. That’s the IR.
For a bucket-filter strategy — trade only bucket A releases; take μ_B as the full-sample or non-A mean — this collapses to Welch’s two-sample t-statistic for the difference of means.The IR you compute IS the t-statistic your risk manager runs to ask “is your active strategy distinguishable from the passive baseline?”
The worked example
Full sample: 197 CAD Ivey × CADJPY 15m non-contaminated releases. Bucket table (verified live 2026-08-13):
| Bucket | n | mean (p) | sd (p) | SE (p) |
|---|---|---|---|---|
| big_miss | 14 | −12.95 | 17.54 | 4.69 |
| small_miss | 45 | −7.02 | 15.57 | 2.32 |
| in_line | 75 | +0.70 | 13.06 | 1.51 |
| small_beat | 49 | +5.80 | 16.83 | 2.40 |
| big_beat | 14 | +6.28 | 12.86 | 3.44 |
| ALL 197 | 197 | −0.37 | 15.94 | 1.14 |
Strategy A — long CADJPY 15m on big-beat releases only:
n_A = 14, μ_A = +6.28p, σ_A = 12.86p
Per-trade Sharpe = μ_A / σ_A = 6.28 / 12.86 =0.488. Above zero, but not dramatic. That’s where the Sharpe framing stops.
Benchmark 1: trade every event long
The passive-benchmark alternative is: on every CAD Ivey release (all 197 of them, regardless of bucket), go long CADJPY 15 minutes before the print, close 15 minutes after. That’s a naive event-timing exposure.
n_B = 197 μ_B = -0.37p ← passive benchmark loses money on average σ_B = 15.94p Excess (alpha) = μ_A - μ_B = 6.28 - (-0.37) = +6.65p per trade TE = √(σ_A²/n_A + σ_B²/n_B) = √(12.86²/14 + 15.94²/197) = √(11.80 + 1.29) = √13.09 = 3.62p IR = 6.65 / 3.62 = +1.84
+1.84 is a t-statistic-shaped number — it says the big-beat bucket-filter strategy outperforms the passive all-events benchmark by about 1.84 standard errors, which is roughly significant at the one-sided 5% level (t = 1.65 threshold). Directional evidence that the filter helps, not conclusive proof.
Benchmark 2: trade every NON-tail event long
A slightly stricter benchmark: only the 183 non-big-beat releases. This excludes the tail bucket the strategy trades, so it’s a genuinely disjoint benchmark.
n_B = 183 μ_B = -0.88p ← non-big-beat releases go down on average σ_B = 16.07p Excess = 6.28 - (-0.88) = +7.16p per trade TE = √(12.86²/14 + 16.07²/183) = √(11.80 + 1.41) = 3.64p IR = 7.16 / 3.64 = +1.97
Slightly higher IR (+1.97) because the non-tail benchmark tilts slightly more negative than the all-events benchmark. In practice both benchmarks give the same qualitative answer: the bucket-filter alpha is real but marginal.
The short-big_miss version has a higher IR
Now run the same calc for the OTHER tail bucket. Short CADJPY on big-miss (n=14, mean of −moves = +12.95p, sd = 17.54p — same absolute stats, sign flipped):
n_A = 14 μ_A = +12.95p σ_A = 17.54p Sharpe = 0.738 Benchmark: short CADJPY on all 197 releases: μ_B = +0.37p, σ_B = 15.94p Excess = 12.95 - 0.37 = +12.58p per trade TE = √(17.54²/14 + 15.94²/197) = √(21.98 + 1.29) = 4.82p IR = 12.58 / 4.82 = +2.61
+2.61, well above the +1.84 for the long-big_beat variant. Two reasons: (a) the big_miss bucket has a bigger absolute mean move (12.95p vs 6.28p) — the miss side is louder on Ivey PMI, and (b) the symmetric-benchmark subtraction (short CADJPY on all releases) has a small positive mean, so the strategy diverges from the benchmark cleanly. The big_miss bucket is where Ivey PMI carries the most edge.
Combining both tails
The natural composite: long CADJPY on big-beat, short CADJPY on big-miss. n=28 combined trades, mean +9.61p, sd 15.47p. Per-trade Sharpe 0.622 — significantly higher than the base big_beat Sharpe of 0.488, because the combined mean is +9.6 vs +6.3 while sd rises only modestly. The two-tail strategy annualizes to a decent per-trade Sharpe even before you consider that the sample volatility of returns overstates the volatility of a trade-selection strategy that spends most of the year in cash.
Why this collapses to Welch’s t
In active-portfolio management, the IR’s denominator is the tracking error — the standard deviation of the sequence of active returns (portfolio minus benchmark, sampled every period). When A and B trade the same periods, active returns paired at each period give TE = σ(r_A − r_B).
In our bucket-filter setup, A trades only on big-beat dates; on non-big-beat dates A returns zero (no position). The benchmark trades every date. So the active-return series is r_A(t) − r_B(t) = 0 on big-beat dates (matched) and −r_B(t) on non-big-beat dates. That’s ugly.
The cleaner formulation for bucket-filter strategies: treat A and B as independent samplesfrom potentially different populations, and ask “is the difference of sample means significantly different from zero?” That’s Welch’s two-sample t-test, and its test statistic IS (μ_A − μ_B) / √(σ_A²/n_A + σ_B²/n_B) — literally the IR formula I used above. Same math, different framing.
The equivalence isn’t coincidental: both are asking whether the strategy’s expected return is provably different from the benchmark’s expected return, given the sampling noise in each estimate. Portfolio management calls this the information ratio; econometrics calls it a t-stat.
Caveats and gotchas
Small n on the tails.n_A = 14 gives a wide CI on both the mean and the SE. Read IR = +1.84 as “plausibly good” not “proven good”. Per Stats for Traders #3, with n=14 the CI on μ_A alone spans roughly ±7p — bigger than the estimated alpha.
Autocorrelation. As Stats for Traders #10 established, releases weeks apart aren’t perfectly IID in absolute-move terms (volatility clustering exists). For the Ivey PMI case releases are month-apart, so the autocorrelation contribution to the tracking error is smaller than for, say, NFP-density data — but not zero. The formula assumes independence; the truth is slightly correlated. Real IR is marginally lower than the formula gives.
Benchmark choice matters. Trading every event long is one benchmark; a hedged benchmark (long on beats, short on misses using SIGN of surprise_z as the filter) is another; a market-timing benchmark (long CAD- strength periods, short CAD-weakness periods) is yet another. Each gives a different IR. Pick the benchmark that represents what your capital could otherwise be doing.
Frequency mismatch with textbook IR.Grinold’s IR rules of thumb (0.5 = good, 0.75 = very good, 1.0 = exceptional) assume annual portfolio return periods with many periods per year. Our IR is per-event, and there are ~13 Ivey PMI releases per year. Annualising by √13 would give 1.84 × 3.6 ≈ 6.6, but this only makes sense if the trades are truly independent (which they aren’t) and if all 13 releases per year will produce big-beat events (they won’t — only ~1 per year based on the historical bucket rate). Don’t annualise this number.
What’s next in the series
Ledger’s Stats queue still has BCa (bias-corrected accelerated) bootstrap as a follow-up to #12 (plain percentile bootstrap), plus higher-moment cumulants, the block-bootstrap installment for autocorrelated series, and Fisher r-to-z as its own installment. Today’s installment closes the Sharpe (#8) → Sortino (#9) → correlation (#11) → info-ratio trilogy the ledger flagged as the natural next step; the next Stats installment can move to whichever of those four topics has the best anchoring in the following day’s slot-1 data.
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This is a deeper claim than the 2026-08-05 post which framed GBPNZD as 'the loudest pair' — GBPNZD isn't just loud in its peak hour, it's louder EVERYWHERE across the week than any of the 25 lower-volatility pairs are in their single loudest hour.Statistics for Traders #12: Percentile bootstrap 95% CI for today's big-miss n=15 CAD Retail median is [+11.1, +50.5] — narrower than the order-stat CI, wider than the t-CI for the mean, and the right choice for asymmetric tail-bucket samplesThe plain (i.i.d.) percentile bootstrap resamples with replacement 20,000 times and takes the 2.5% and 97.5% percentiles of the resampled medians. On today's slot-1 anchor — CAD Retail m/m × USDCAD big_miss n=15 — that gives 95% CI [+11.10, +50.50], width 39.4p. Contrast: the distribution-free order-stat CI from Stats #3 is [−5.30, +52.30] (wider, safer), and the t-CI for the MEAN from Stats #7 is [+8.23, +39.01] (narrower, assumes normality). 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Explains why: the small-miss population is dominated by publisher-error / algo-reversal outliers that the big-miss samples don't see.Canada CPI m/m on CADJPY: 15 of 19 big-beat prints sent the pair up, and the 5-bucket median walk is monotonic — the JPY cross beats USDCAD once again194 non-contaminated Canada CPI m/m releases on CADJPY at 15 minutes. Medians walk −11.1 / −4.8 / +1.1 / +10.4 / +18.7 pips across big_miss → big_beat, and pct-up walks 15.4 / 35.4 / 55.6 / 63.6 / 78.9 percent — both monotonic through all five buckets. The same event on USDCAD is directionally right too, but its miss-side medians are non-monotonic (big_miss +12.8p is smaller than small_miss +12.95p). 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No non-oil release comes close.Statistics for Traders #11: Pearson r across the whole CAD CPI × CADJPY sample is +0.44 — but within the big-beat bucket alone it's −0.15, and the bucket labels carry the signalThe 5-bucket monotonic walk we see across the tool is what a positive Pearson correlation of +0.44 looks like when you discretize a continuous relationship. But when you drop back into each of the five buckets and ask 'does knowing the exact surprise-z inside this bucket help me forecast the move?', four of five bucket-level r values have 95% CIs that straddle zero. The bucket boundary IS the signal — trying to squeeze extra edge from the exact z within a big_beat print doesn't work.Paper Trail #11: On Persistence in Mutual Fund Performance (Carhart, 1997) — the paper that added momentum to Fama-French and killed most of the 'hot hands' persistence storyMark Carhart's 1997 Journal of Finance paper extends Fama-French's 1993 3-factor model with a fourth factor: PR1YR, a zero-investment portfolio long last year's top 30% return firms and short last year's bottom 30%. On a survivor-bias-free sample of 1,892 diversified equity funds from 1962-1993, the 4-factor model plus investment costs explain 6.3 of the 8 percentage points of annual spread between decile-1 and decile-10 past-return funds — leaving the residual 'hot hands' story with almost nothing left. 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Practical implication: rate decisions are pre-priced by construction, not tradeable via the standard bucket-response framework the tool was built for.Australia Employment Change on AUDJPY: 21 of 26 big-beat prints sent the pair up 35.6 pips at 15m, with a cleaner beat-side walk than on AUDUSD198 non-contaminated Australia Employment Change releases on AUDJPY at 15 minutes. Medians walk −18.2 / −21.9 / +1.7 / +19.5 / +35.6 pips big_miss → big_beat, monotonic across all five buckets. Pct-up on the beat side walks 53 → 75 → 81 percent. Compared with the same event on AUDUSD (covered in the 2026-08-02 two-employment-reports post), the JPY-cross wins on beat-side pip magnitude and the USD-pair wins on miss-side unanimity (0-of-8 big-miss prints went up on AUDUSD versus 2-of-8 on AUDJPY).Tokyo takes lunch at 03:00 UTC — and every liquid JPY-cross gets 14-18% quieter for the next 90 minutesAll seven liquid JPY-crosses have a local minimum in typical half-hour range between 03:00 and 04:30 UTC. That window is Tokyo's lunch break (12:00-13:30 JST), and it dips 14-18% below the bracketing 02:00 UTC and 05:00 UTC half-hours. Japan doesn't observe DST, so the dip is stable year-round.Statistics for Traders #10: autocorrelation and why 196 NFP releases behave like 115 independent samples once you look at the volatility clusteringThe 196-release NFP history on USDJPY shows near-zero autocorrelation in signed move direction (as efficient-markets weak-form predicts) but a striking +0.26 lag-1 autocorrelation in absolute move size. Volatility clusters. Treat that 196-sample history as if it were 115 independent observations for confidence-interval purposes — otherwise your CIs will be systematically too narrow by about 30 percent.Paper Trail #10: Generalized Autoregressive Conditional Heteroskedasticity (Bollerslev, 1986) — the paper that gave us GARCH(1,1)Tim Bollerslev's 1986 Journal of Econometrics paper generalizes Engle's ARCH by letting past conditional variances enter the variance equation. The GARCH(1,1) form — h_t = α_0 + α_1·ε²_(t−1) + β_1·h_(t−1) — became the workhorse volatility model for the next 40 years. Verified against the full 21-page PDF Bollerslev hosts on his Duke faculty page.NFP on USDJPY: 14 of 14 big-beat prints sent USDJPY up at 15 minutes, and the small-miss bucket falls harder than big-miss196 non-contaminated NFP releases on USDJPY at the 15-minute window. Medians walk −16.2 / −26.0 / +1.8 / +24.5 / +30.2 pips big_miss → big_beat, monotonic. Big_beat n=14 lands 100% of prints on the up side. The small_miss bucket actually falls further than big_miss (−26 vs −16 pips) — a small-sample-on-tail artifact that shows up on several employment-print pairs.CAD Unemployment on USDCAD: 22 of 26 big-beat prints sent USDCAD down, and the whole 5-bucket walk is monotonic across every window from 1 minute to 4 hours196 non-contaminated Canada Unemployment Rate releases on USDCAD at 15 minutes. Medians walk +15.1 / +19.5 / +4.5 / -10.9 / -39.3 pips across the five surprise buckets. Pct-up walks 70 / 79 / 54 / 36 / 15 percent. This is the third Canadian labour post in the batch — completing the trilogy after CAD Employment Change on USDCAD (2026-08-01) and CAD Employment Change on CADJPY (2026-08-08). Both Canadian labour prints trade the same way: a Canadian jobs improvement pushes CAD stronger against the US dollar.Thursday 13:30 UTC — jobless-claims hour — is the loudest half-hour of the week for 9 pairs, more than Friday's NFP hour is for 7Across the 28 liquid FX pairs, the single loudest half-hour of the week lands on Thursday 13:30 UTC for 9 pairs, on Friday 14:30 UTC (the post-NFP echo) for 8, and on Friday 13:30 UTC (NFP itself) for 7. NFP-hour is not the modal peak. Weekly US Initial Jobless Claims plus the rotating monthly cluster of Retail Sales, GDP, Philly Fed, and PPI at the same 08:30-US-Eastern slot turn Thursday 13:30 UTC into a structurally loud cell for USD-, EUR-, and CHF-based pairs.Statistics for Traders #9: Sortino ratio via the CAD Unemployment × USDCAD big-beat bucket — a Sortino of 4.79 vs a Sharpe of 0.98, and why the gap tells you the loss distribution is asymmetricThe 26 big-beat CAD Unemployment prints on USDCAD produce a per-trade Sharpe of 0.98 for a short-USDCAD strategy — respectable but not extraordinary. The Sortino ratio on the same 26 trades is 4.79. That's not a computational quirk; it's the loss distribution telling you something concrete: 22 of the 26 trades won, and 3 of the 4 losers were small (-3.2, -3.5, -9.0 pips). Only one trade (2017-08-04 at -35.1p) was a meaningful loss. Sortino divides mean return by the RMS of downside returns, ignoring the upside variance that Sharpe's denominator sums over. When the ratio Sortino/Sharpe is well above the sqrt(2) that a symmetric distribution would predict, the loss side is genuinely rarer or smaller than the win side. This is the follow-up to Stats #8 (Sharpe ratio) and grounds today's slot-1 anchor in a distributional-asymmetry lens.Paper Trail #9: Mutual Fund Performance (Sharpe, 1966) — the paper that introduced what we now call the Sharpe ratio, applied to 34 mutual funds over 1954-1963William F. Sharpe's 1966 Journal of Business paper introduced the reward-to-variability ratio (R/V) as a single-number risk-adjusted performance metric, applied it to 34 open-end mutual funds over 1954-1963, and found the best fund's R/V of 0.78 was only 1.8× the worst fund's 0.43. Same-period rank correlation of R/V vs Treynor Index across the 34 funds: +0.974 — they rank the same funds almost identically. Cross-decade predictive rank correlation (1944-53 → 1954-63): +0.360 — modest, but a full standard error above zero. The paper also observed that median 'percentage of variance due to comovement with the Dow-Jones Industrial Average' was 90.33% across the 34 funds — most mutual funds were, at that point, well-diversified market proxies. Full primary-source verification against a JSTOR-sourced PDF.UK Unemployment on GBPUSD: 18 of 23 big-beat prints sent GBPUSD up, with the pct-up walk cleanly monotonic across all 5 buckets198 non-contaminated UK Unemployment Rate releases on GBPUSD at 15 minutes. Pct-up walks 25 / 47 / 57 / 60 / 78 percent across the five surprise buckets — monotonic. Tail medians walk -21.7 / +2.3 / +16.7 pips. Contrarian: a print that BEATS (comes in lower than consensus, fewer unemployed) pushes sterling UP against the dollar. Second unemployment-prints-are-contrarian post in the batch after CAD Unemployment on USDCAD (today's slot 1). The G10 pattern holds: labour-market improvements strengthen the home currency, and 15-minute FX prices reflect the surprise-direction almost immediately.UK CPI on GBPJPY: 42 of 47 small-miss prints sent GBPJPY down, and the whole 5-bucket walk is cleaner than on GBPUSD196 non-contaminated UK CPI (y/y) releases on GBPJPY at 15 minutes: medians walk −20.90 / −22.20 / +2.60 / +9.75 / +11.45 pips across the five surprise buckets. Pct-up walks 31 / 11 / 54 / 69 / 81 percent. The small-miss bucket is unusually clean — 42 of 47 prints sent GBPJPY down. The sterling leg dominates both crosses at the print (176 of 196 GBPJPY and GBPUSD moves share a sign), but the JPY-cross magnitudes are ~35% bigger at the tails and the small-miss unanimity is a touch stronger.The half-hour AFTER NFP is louder than NFP itself for 17 of 28 liquid FX pairsFor 17 of 28 liquid FX pairs, the Friday 14:30 UTC half-hour — the second half of the NFP hour, after the release-time bar closes — has a larger typical 30-minute range than the Friday 13:30 UTC release cell itself. Four pairs (EURUSD, AUDUSD, NZDUSD, GBPUSD) peak at Friday 14:00 UTC instead. Only 10 pairs (mostly JPY-crosses) have Fri 13:30 as their loudest of the NFP hour. If you're trading NFP with a hold-through-the-hour plan, the follow-through cell is where more of the risk actually shows up.Statistics for Traders #8: Sharpe ratio via the UK CPI × GBPJPY big-beat bucket — a +16-pip mean move looks great until you divide by 23 pips of noiseThe 16 big-beat UK CPI prints on GBPJPY had a mean 15-minute move of +16.06 pips and a standard deviation of 23.39 pips. Divide the mean by the standard deviation and you get a per-trade Sharpe ratio of 0.687 — well below the ‘decent’ threshold of 1. Sharpe is the trader's answer to “how much edge per unit of noise?” — a bigger number means the edge is bigger relative to the round-trip variability you'll actually experience. Worked here on today's slot-1 anchor, with an explicit comparison to the same-event GBPUSD version, an annualization illustration, and the four assumptions Sharpe quietly makes about your data.Paper Trail #8: Common Risk Factors in the Returns on Stocks and Bonds (Fama & French, 1993)The paper that supplanted CAPM in the industry's default asset-pricing toolkit. Fama and French argued that the single-factor market beta of CAPM misses two systematic sources of return variation — the size premium (small stocks beat big stocks over long horizons) and the value premium (high book-to-market stocks beat low book-to-market stocks). Adding SMB (small-minus-big) and HML (high-minus-low) as factors alongside the market takes the R² on their 25 test portfolios from CAPM's 2-of-25 above 0.9 to the three-factor model's 21-of-25 above 0.9. Every quantitative claim in this post is traced to the fetched PDF at bauer.uh.edu.Canada Employment on CADJPY: all 23 big-beat prints had CADJPY higher 15 minutes later, and the whole 5-bucket walk is stronger than USDCAD197 non-contaminated Canada Employment Change releases on CADJPY at 15 minutes. Medians walk −19.3 / −13.4 / +5.2 / +27.9 / +42.9 pips across the five surprise buckets. Pct-up walks 22 / 32 / 61 / 81 / 100 percent. The big-beat bucket has never sent CADJPY down — 23 for 23 up prints, the largest unanimous-up-rate sample in my published set. The USD cross I posted on 2026-08-01 showed the same directional shape but the JPY cross amplifies it: two same-sign pushes (CAD stronger + JPY weaker on the commodity-friendly print) versus one on USDCAD.US ISM Services PMI on USDJPY: 18 of 19 big-beat prints saw USDJPY higher, and the whole 5-bucket walk mirrors ISM Manufacturing on USDJPY almost exactly197 non-contaminated ISM Services releases on USDJPY at 15 minutes: median moves −25.9 / −9.35 / +1.2 / +11.55 / +12.4 pips across the five surprise buckets, pct-up walk 9 / 20 / 57 / 83 / 95 percent. Compare that with ISM Manufacturing on USDJPY, published four days ago — same walk, same signs, beat-side tail-unanimity is actually a touch stronger for Services. The two ISM releases print two days apart every month and move USDJPY the same way.Every liquid FX pair's loudest weekday half-hour is at least 2× its quietest — and EURUSD's is nearly 5×For each of the 28 liquid FX pairs, divide the median range of its loudest weekday half-hour by the median range of its quietest. Every one of the 28 lands above 2×. 13 land above 3×. Six land above 4×. EURUSD tops the list at 4.97× — its Thursday 13:30 UTC cell is nearly five times louder than its Monday 22:30 UTC cell. If your default risk model uses a session-average range, you're systematically over-sizing your quiet-window trades and under-sizing your loud-window ones.Statistics for Traders #7: the t-distribution 95% CI for the mean, worked on the 11 ISM-Services big_miss USDJPY moves — and why the answer is narrower than the median CI, but you should trust it lessThe 11 big-miss USDJPY moves following ISM Services PMI: mean −38.69 pips, standard deviation 39.81 pips, standard error 12.00 pips. Applying the t-distribution formula, the 95% CI for the true mean move is [−65.43, −11.95] pips — a 53.5-pip window. That's narrower than the distribution-free CI for the same sample's median (Stats #3 method: [−87.60, −6.00], width 81.6p). But the t-CI's tightness depends on assuming the data are normally distributed, and Stats #6 already showed that FX bucket distributions are fat-tailed. The narrower CI comes from an assumption the data violates.Paper Trail #7: Trading Is Hazardous to Your Wealth (Barber & Odean, 2000)78,000 households, six years of discount-brokerage trades, and one of the most cited findings in retail-investor research: the households that traded most earned an annualised 11.4% net-of-costs, while the market returned 17.9%. Buy-and-hold households earned 18.5%. The gross-return spread was ~3-4 percentage points; the rest of the seven-point gap was pure transaction cost. What the paper actually says, in the paper's own numbers.US CPI on EURUSD: 196 releases walking a clean 5-bucket monotonic response — and the mirror image of UK CPI on GBPUSD once you correct for pair convention196 non-contaminated US CPI y/y releases on EURUSD at 15 minutes: median moves +11.8 / +8.6 / +3.0 / −6.35 / −16.2 pips across the five surprise buckets. 11 of 13 big-beat prints — including the entire summer-2022 inflation panic — pushed EURUSD down. Compare that with UK CPI on GBPUSD (published 2026-08-02): same shape, flipped sign, because base-currency convention flips too. Both are the same underlying rate-differential re-pricing.AUD Unemployment Rate on AUDUSD: all 8 big-miss prints pushed the pair down, monotonic across all 5 buckets198 releases of Australia's Unemployment Rate on AUDUSD since 2010. Median 15-minute moves: −24.10 / −15.30 / −0.10 / +16.45 / +24.50 pips (big_miss → big_beat). Up-rate column: 0 / 28 / 49 / 70 / 72. All 8 big-miss prints moved AUDUSD down. Cleanest 5-bucket monotonic labour-print I've pulled from the tool.CADCHF is the quietest of the seven CAD-crosses — typical 30-minute range 7.25 pips, and its loudest cell of the whole week barely beats USDCAD's medianAcross every 30-minute weekday cell of CADCHF, the typical range is 7.25 pips — smaller than USDCAD (9.1p), CADJPY (9.9p), EURCAD (11.85p) or GBPCAD (15.0p). Its loudest cell of the entire week — Friday 13:30 UTC, on NFP — prints just 14.2p. Which is smaller than GBPCAD's median. If you want a CAD cross without a peak, this is the one.Statistics for Traders #6: fat tails, kurtosis, and why NFP's 'in-line' bucket contains the biggest outlier moves in the datasetKurtosis is tail-thickness — how far the min-to-p25 and p75-to-max whiskers reach relative to the IQR. On NFP × EURUSD 15m, the in_line bucket has BOTH whiskers over 2.3× IQR (Tukey's outlier threshold is 1.5×). The 'quiet' bucket contains the largest observed candles in the dataset: −104.8p from a 165k print in 2013, +86.0p from a 162k print later that same year. Fat tails are what happens when 'in-line' meets tape you didn't schedule for.Paper Trail #6: Post-Earnings-Announcement Drift (Bernard & Thomas, 1989)The paper that turned a curious 1968 observation into a durable anomaly with a name. Bernard and Thomas took the drift that Ball & Brown had found — stock prices continue moving in the direction of an earnings surprise for weeks after the announcement — and showed that no risk-based story could explain it. Fama would later call PEAD 'the granddaddy of underreaction events'. It's the equity-market cousin of everything the News Impact Explorer measures on FX.NZ CPI on NZDUSD: 0-of-3 big-miss prints went up, 6-of-6 big-beat prints went up — the cleanest tail unanimity across every G10 inflation print I've published this week63 quarterly NZ CPI prints on NZDUSD at 15 minutes: median moves −54.9 / −29.0 / +2.1 / +12.45 / +13.45 pips across the five surprise buckets. Every one of the 3 big-miss prints pushed NZDUSD down; every one of the 6 big-beat prints pushed it up. Small samples on the tails but strong pattern — and it's the fourth 'inflation-print monotonically moves the domestic USD pair' finding in a row, after Switzerland, Canada, and Australia.Swiss CPI on USDCHF: clean 5-bucket directional response with 198 releases behind itAcross 198 monthly Swiss CPI prints since 2010, USDCHF's 15-minute median move climbs monotonically from −4.5 pips on a big beat to +6.6 pips on a big miss, and the pct-up rate walks from 36% up through 78%. Beats and misses each get 11+ tail-bucket observations — enough sample to say the shape is real.GBPNZD is the loudest liquid FX pair — its typical 30-minute range beats USDCHF's loudestAcross every 30-minute weekday cell of GBPNZD, the typical range is 21.9 pips — bigger than USDCHF's single loudest cell of the entire week (16.0p). The single loudest cell across all 28 major pairs is GBPNZD Friday 14:30 UTC at 34.55p. The pair is flat, hot, and gives you nowhere to hide.Statistics for Traders #5: how to read skew off the tool's p25/median/p75 columnsSkew is distributional asymmetry, and the tool exposes it two ways: is the median centered inside the IQR (or does it tilt toward one edge?), and are the whiskers the same length (or is one much longer)? On NFP big_miss XAUUSD, the IQR is centered but the right whisker is 2.56× the left — 'inside-symmetric, outside-right-tailed'. Read both signals; sometimes they agree, sometimes only one signals.Paper Trail #5: ARCH (Engle, 1982)The paper that formalised what every trader already knew: volatility clusters. Big-move days sit next to other big-move days; calm days sit next to other calm days. Nobody assumes a stock's variance is 'a constant' anymore, and the reason nobody does is this paper.Canada's Trimmed CPI is the second BoC-preferred inflation metric to move its USD pair monotonically — and 5 of 5 big-miss prints pushed USDCAD up82 releases of Canada's Trimmed-Mean CPI y/y on USDCAD, split into five surprise buckets. Medians climb monotonically from −9.0p on small_beat to +15.8p on big_miss, and 5 of 5 big-miss prints went positive. Same shape as yesterday's AUD Trimmed Mean CPI post on AUDUSD — different currency, same rate-differential mechanism.AUDUSD trades Australia's Trimmed Mean CPI like a rate decision — cleanly and monotonicallyAcross 67 Trimmed Mean CPI releases, AUDUSD's median 15-minute response climbs monotonically from −36 pips on a big miss to +21 pips on a small beat, with 0% and 17% up-rates on the miss buckets. It's the cleanest directional shape any AUD data point produces on the pair.27 of 28 liquid FX pairs' quietest half-hour of the week is on MondaySweep every pair in the Calm Zones catalogue for its minimum-range weekday cell and Monday wins 27 times out of 28. The one exception (EURCAD, Wed 04:00 UTC) is in the same pre-London Asia sleep window as the Monday holdouts. The finding is more consistent than any single pair's loudest-cell story.Statistics for Traders #4: what the tool's p25 and p75 columns actually tell you (and why they're not the same as ±1σ)The median tells you the center. The IQR (p75 − p25) tells you the width. On NFP big_beat n=14, half of all outcomes landed between −52 and −16 pips, but the outer half stretched from −124 to +9. Position-sizing depends on which of those windows you're planning against.Paper Trail #4: Efficient Capital Markets (Fama, 1970)The paper that put 'you can't beat the market' on formal footing. Fama's three forms of efficiency, plus the empirical work he cites — including Jensen's 1968 study where 89 of 115 mutual funds underperformed a comparable-risk market portfolio by an average of 14.6% over ten years, net of fees.US GDP surprises move gold in the textbook direction — miss up, beat down, monotonic across all five bucketsAcross 72 US GDP releases on XAUUSD, the median 15-minute move climbs monotonically from −25 pips on a big beat to +56 pips on a big miss, with 88% up-rates on miss-side prints. Same directional mechanism as NFP-on-gold, on a different macro event class.US ISM Manufacturing PMI on USDJPY: 0 of 12 big-miss prints had the dollar higher an hour laterAcross 196 ISM Manufacturing PMI releases since 2010, USDJPY responds monotonically to the surprise: -23 pips on a big miss, +14 on a big beat, and only 1 of the intermediate buckets breaks the pattern. The direction holds cleanly through end of day.AUDNZD is the only major pair whose loudest hour of the week isn't in the London/NY overlapAcross all 28 pairs in the Calm Zones catalogue, 27 have their loudest half-hour cell fall in the London/NY overlap window on a Thursday or Friday. AUDNZD is the exception. Its loudest cell is Wednesday 21:00 UTC — the Sydney/Wellington open.Statistics for Traders #3: why the big_miss bucket's +12 pip median could actually be +105 or −24Sample size is the difference between a tool that shows you a signal and a tool that shows you noise pretending to be one. Here's what a small-n bucket median actually tells you — and why the tool marks small buckets faint on the chart.Paper Trail #3: The Disposition Effect (Odean, 1998)Ten thousand discount-brokerage accounts, seven years of trades, and one uncomfortable finding: retail investors realized 50% more of their winners than their losers — and the winners they sold went on to outperform the losers they kept by 3.4% over the following year.NFP moves gold in the exact opposite direction it moves the dollar — and the mirror is remarkably cleanThe typical NFP big-beat prints XAUUSD 108 pips lower in the first 15 minutes and 92% of big-miss prints send it higher. That's a cleaner directional signal than NFP produces on any USD pair, and it makes physical sense.UK CPI on GBPUSD: the clean directional edge is gone by end of day196 clean releases show a textbook first-hour reaction — miss and pound falls 20 pips, beat and it rises 8. By end of day, the big-beat bucket has flipped sign. The move is real; the drift is not.EURGBP is the quietest liquid major, and its loudest cell isn't close to anyone else'sAcross 237 weekday half-hour cells since 2010, EURGBP's typical median 30-min range is 5.8 pips and its single loudest cell is 11.4. Both numbers are smaller than the equivalent numbers on every other G10 major.Statistics for Traders #2: what "±35 pips of noise" actually means for your stopThe typical NFP small_beat moves EURUSD -28 pips at 15 minutes. The standard deviation of that same distribution is 35 pips — almost as large as the median move itself. Here's what that number really tells you.Paper Trail #2: Momentum (Jegadeesh & Titman, 1993)The paper that turned 'buy what's going up' from folk wisdom into a documented anomaly. 1.31% per month on a winners-minus-losers zero-cost portfolio, 1965–1989 US stocks, with half the return giving back over the following 24 months.Two employment reports, both textbook at 15 minutes. Only one is still textbook by end of day.AUD Employment on AUDUSD and CAD Employment on USDCAD look nearly identical in the first fifteen minutes. By the New York close their miss sides have gone in opposite directions.CAD Employment on USDCAD is the textbook surprise-response event196 clean releases show a symmetric five-bucket distribution: big miss +40 pips, big beat -41 pips, in-line does nothing. No mean-reversion at EOD. Reproducible in the tool.GBPJPY's loudest half-hour of the week is Friday 13:30 UTCAcross 802 Fridays of GBPJPY data, the median 30-minute range in the 13:30 UTC slot is 28.3 pips — 2.5x the Asia-session median. The full Wed–Fri overlap band dominates every other slot in the week.Statistics for Traders #1: what "z-score" actually tells you about an economic surpriseA 14k NFP beat and a 113k NFP beat can bucket into completely different surprise sizes. Z-scoring against the release's own history is why. Worked through the last three months of NFP prints.Paper Trail #1: Prospect Theory (Kahneman & Tversky, 1979)The paper that reframed how economists model risk. Losses feel bigger than equivalent gains, people flip from risk-averse to risk-seeking when framed differently, and the S-shaped value function that describes both is still the reference decades later.156 out of 174 RBA meetings didn't move AUDUSD. The 18 that did, moved it in one direction.Since 2010, 90% of RBA cash-rate decisions have printed exactly on consensus and moved AUDUSD by a median 3 pips. The other 10% were 9 misses (100% AUDUSD down, median -64p at 15m) and 9 beats (100% AUDUSD up, median +46p). RBA meetings are all about the surprise.EURUSD Thu 12:30 is quiet 15% of the time. Here's why.127 of 832 Thursdays at 12:30 UTC were calm on EURUSD. Why the number isn't zero, and why the tool marks this cell DST-ambiguous.Four XAUUSD reaction records fell in four weeksFed, GDP, NFP, CPI: four US macro events between June 17 and July 14 each broke the largest 15-minute XAUUSD reaction on record, by 1.74x to 2.33x. What the data shows, and what it doesn't.How to check what a release does to your pairEvery scheduled release has a track record. Here is how to read it in News Impact Explorer, panel by panel, using NFP on gold as the worked example.What actually happens to EURUSD in the 15 minutes after NFP (and the next five days)195 releases, sorted by surprise, measured from 15 minutes out to 5 days. The story isn't drift. It's asymmetric: beats mean-revert, misses hold, in-line does nothing.Why your backtested win rate doesn't survive contact with real news eventsSome of your backtested wins and losses came from news flow that had nothing to do with your setup. Here's how much, and how to filter it out.The Bloomberg terminal problem: why institutional edge on macro events was never available to retailThe pros really do have data you don't. Here's exactly what it is, why the gap stayed open for so long, and why it's finally closing.