01 · Macro Data Reading: Expectation Gaps, Structure Breakdown, and a Complete Interpretation Template
The same "CPI 3.2%" — why do some shout bearish while others shout bullish? Because the number itself means nothing; the gap between the number and expectations is what matters. This article builds the "expectation gap" mindset, then unpacks reading details for CPI, PMI, nonfarm payrolls, and the unemployment rate one by one, closing with a replicable complete template for interpreting macro data.
1. Expectations and Expectation Gaps: Lesson One of Data Reading
Markets are arenas of trading against expectations: asset prices already embed the market's shared expectations of the future; on release, price reacts only to the part where "actual ≠ forecast". That gap is the expectation gap.
Numeric Example: Is CPI 3.2% Bullish or Bearish?
Suppose the Fed is in a "rate-cut debate" phase and the market expects this month's CPI YoY at 3.0%:
| Scenario | Actual release | Expectation gap | Market reading | Equity reaction (reference) |
|---|---|---|---|---|
| A | 3.0% | 0 (in line) | Business as usual, no new information | Muted |
| B | 2.8% | 0.2pp below forecast | Inflation falling faster than expected → rate-cut hopes rise | Bullish for stocks |
| C | 3.2% | 0.2pp above forecast | The "prices returning to 3.0%" expectation fails → cuts delayed | Bearish for stocks |
Key conclusion: in scenario C, 3.2% isn't high in itself, but it is "above expectations" — the easing path the market had bet on is interrupted, and assets get priced as bearish. Bullish and bearish are never a function of the number's level but of the gap between "actual vs forecast".
Lesson One of Data Reading
Bullish and bearish are never a function of the number's level but of the gap between "actual vs forecast". 3.2% isn't high in itself, yet "0.2pp above expectations" makes assets price as bearish — trade the expectation gap, not the number.
Where Do Expectations Come From?
- Median of Bloomberg/Reuters economist surveys (consensus forecast)
- Institutional nowcasting models (Atlanta Fed GDPNow, New York Fed Nowcasting, etc.)
- Rate market pricing (hike/cut probabilities implied by fed funds futures)
💡 In-Line Data Almost Never Moves Markets
Data matching forecasts almost never produces a move; big deviations from forecasts are the source of volatility. Many beginners chase right at the release only to watch price barely twitch because the data matched forecasts — they were trading the "number", not the "expectation gap".
2. Breaking Down the CPI Structure
Core CPI vs Headline CPI
| Indicator | Definition | Use |
|---|---|---|
| Headline CPI | Full basket of goods and services prices | News headlines; shapes public perception |
| Core CPI | Excludes food and energy | Policy reference; less volatile, better reflects inflation trend |
Food and energy prices are heavily influenced by weather, geopolitics, hog cycles and other external factors, swing violently monthly, and easily mask trends. Central banks watch trends, which is why core CPI is the number with the strongest policy implications.
How to Read the Components: Shelter, Energy, Food
- Shelter: about 1/3 of US CPI weight, the largest single component. Rent is extremely sticky — only when the shelter component peaks and turns down is US inflation's substantive decline confirmed.
- Energy: transmits directly from oil prices with large monthly swings — MoM beats YoY for reading it; oil surges/slumps often distort headline CPI beyond recognition.
- Food: same logic — driven by weather and supply events; single-month swings say nothing about trend.
The "Hog Cycle": A Distortion Common Knowledge in China's CPI
Pork carries a significant weight in China's CPI; the hog cycle (breeding-sow inventory → slaughter supply 10-14 months later → pork price) periodically pulls/drags CPI:
- During pork upcycles, CPI may be pushed higher even if other prices are stable;
- During pork downcycles, CPI can be pressed to extremely low or even negative readings — a low CPI then does not mean deflation pressure; it's just the hogs helping.
So when reading China's CPI, always pull the pork component out separately: CPI excluding pork is the reading that reflects real demand.
Why the Fed Watches PCE Instead of CPI
The Fed's preferred inflation gauge is the core PCE price index (the Fed's 2% target definition), not CPI. Differences:
| Difference | CPI | PCE |
|---|---|---|
| Compiled by | Bureau of Labor Statistics (BLS) | Bureau of Economic Analysis (BEA) |
| Data sources | Consumer expenditure survey (small sample) | Business sales data (broad coverage) |
| Weighting | Fixed basket | Dynamic weights (auto-reflects shifting consumption structure) |
| Healthcare | Mostly consumer out-of-pocket | Includes employer-paid medical spending |
| Level tendency | Usually ~0.3-0.5pp above PCE | Milder; the policy target gauge |
💡 CPI Is the Dress Rehearsal; PCE Grades the Final Exam
After every CPI surprise, markets turn to the same month's PCE released two to three weeks later — CPI is the rehearsal; PCE is the Fed's official grading.
3. Reading the PMI
Boom-Bust Line 50: The Passing-Mark Logic
PMI uses 50 as its boom-bust line:
| PMI | Meaning |
|---|---|
| Above 50 | Manufacturing expanding MoM |
| Equal to 50 | Flat vs previous month |
| Below 50 | Manufacturing contracting MoM |
💡 The 50 Line Is Not a Good/Bad Boundary
50 only divides "expanding vs contracting MoM"; it is not a good/bad boundary. 50.5 counts as lackluster-but-stable, 49.5 as slight contraction. What truly matters is trend and components, not any single month's print.
New Orders − Finished-Goods Inventory: The "Inventory Cycle" Signal
PMI is a survey-based forward-looking indicator; its most valuable part is component combinations. Watch the relationship between two core components:
| New orders | Finished-goods inventory | Signal |
|---|---|---|
| Rising | Falling | Demand improving, inventory being digested → precursor to active restocking, bullish |
| Rising | Rising | Demand warming but firms restocking cautiously, or demand overheating superficially |
| Falling | Rising | Demand weakening, inventory piling up → likely future discounting to destock, bearish |
| Falling | Falling | Firms actively destocking and cutting output → digestion phase before a bottom |
The "new orders − finished-goods inventory" spread works as a simple leading index: when the spread bottoms and turns up, it often leads industrial profits and equity turning points by several quarters (see Industry Data Reading for the full inventory cycle).
Caixin vs Official PMI: Sample Differences
China has two manufacturing PMIs:
| Aspect | Official (CFLP/NBS) PMI | Caixin PMI |
|---|---|---|
| Compiled by | China Federation of Logistics & Purchasing + National Bureau of Statistics | Caixin + S&P Global |
| Sample | Large/medium/small firms, weighted toward large-medium/SOE names | Weighted toward small-medium/private/export-oriented firms |
| Release | Last day of each month (9:30) | First business day of each month (9:45) |
| Use as reference | Aggregate, policy orientation | SME and foreign-trade health |
💡 The Two PMIs Are Convincing Only When They Agree
The two datasets are convincing only when directionally consistent; divergence (official weak, Caixin strong) usually signals "big firms weak, small firms strong" or vice versa — structural differentiation rather than an aggregate signal.
4. Reading Nonfarm Payrolls
The NFP report (US Department of Labor, first Friday of each month, 20:30/21:30 Beijing time) contains a trio: nonfarm job additions, the unemployment rate, and average hourly earnings growth. The three numbers often contradict each other.
How to Weigh the Three Signals
| Signal | Meaning | Market impact (reference) |
|---|---|---|
| Job additions beat | Economy resilient | Bullish dollar and stocks; but if it delays cuts, good news turns bad |
| Unemployment rate unexpectedly rises | Labor market weakening | Bearish dollar, bullish cut expectations |
| Hourly earnings rise YoY | Inflation pressure persists | Bearish for cut expectations |
Weighing Three Contradictory Signals in One NFP Report
Numeric example: one month's NFP report —
- Job additions 250k (forecast 180k, a large beat)
- Unemployment 4.3% (previous 4.0%, unexpectedly up 0.3pp)
- Hourly earnings +3.9% YoY (forecast 4.1%, below expectations)
Surface contradictions: jobs strong → yet unemployment rose; wage growth slowing → wage-price pressure easing. How does the market weigh them?
- First look at the driver: if the unemployment uptick is driven by "rising labor force participation" (more people entering to look for work), that's supply-side improvement and actually benign; only if driven by falling employment does demand weaken.
- Then look at wages: below-expectation earnings mean the wage-price spiral is cooling, relieving the "strong jobs → sticky inflation → no cuts" transmission chain.
- Reference conclusion: such a combination is often read as "resilient economy but easing inflation pressure" — neutral-to-positive for risk assets.
Common knowledge: the first wave after NFP often takes direction from whichever number looks most extreme, but the 30-60 minute post-open direction is professional money's true verdict after weighing. Don't chase the first wave.
The First Wave After NFP Is a Trap
The 30-60 minute post-open direction is professional money's true verdict after weighing. The first wave after release is usually just an emotional reaction to "whichever number looked most extreme"; chasing it means using retail money to fill institutional orders.
The "Revisions" Trap in Employment Data
- The NFP initial print gets heavily revised up or down over the following two months as samples are supplemented; revisions exceeding 100k are routine — e.g., the March 2024 initial print was later cumulatively revised down by hundreds of thousands.
- Common knowledge: the initial value is a provisional number treating "probable" as "certain". Heavy betting on initial prints means building decisions on numbers that will be rewritten.
- Right approach: follow the three-month moving average and cumulative revision trend; don't fixate on a single month's initial print.
5. Details of the Unemployment Rate
U3 vs U6
| Measure | Definition | Numerical relation |
|---|---|---|
| U3 | Official unemployment: unemployed and actively seeking / labor force | The headline rate, most quoted |
| U6 | Broadest measure: U3 + marginally attached workers + involuntarily part-time for economic reasons | Usually ~1.5-3pp above U3 |
U3 falling while U6 rises says the surface unemployment rate looks fine, but "hidden unemployment" (people giving up the search, those forced into part-time) is growing — labor-market quality deteriorating.
Labor Force Participation
Labor force participation rate = labor force / working-age population; it explains the unemployment rate's "illusions":
- Falling participation (people exiting the labor force) passively lowers the unemployment rate — the denominator shrinks, the number improves, but employment hasn't.
- Post-pandemic US participation has stayed persistently below pre-pandemic levels, so read claims of "historically low unemployment" with a discount.
6. The "Time-Lag" Awareness of Data
Nearly all macro data passes through a initial → revised → final revision chain:
| Stage | Traits | Trading implication |
|---|---|---|
| Initial (preliminary) | Incomplete sample, released fastest | Biggest market reaction, least reliable |
| Revised | Amended after supplementary samples | Often ignored but closer to truth |
| Final | Fully revised | Only affects historical series, not current trading |
⚠️ Don't Build Long-Term Conclusions on Initial Prints
GDP, NFP, and CPI all undergo systematic revision. Don't build long-term conclusions on initial prints; even in short-term data trades remember — you're trading "market sentiment about the initial print", not "economic truth".
7. The Complete Macro Data Interpretation Template
For any macro data release, walk six steps (also fixable as your own notes template):
① What is the data → name, definition, coverage period (CPI YoY? core? seasonally adjusted?)
② What was expected → what was consensus? what was the market betting on?
③ What was actual → how far off the forecast? how many standard deviations?
④ What's the structure → who drove the gap? break down by component/region/industry
⑤ First market reaction → which assets moved at release? direction and magnitude? consistent with intuition?
⑥ Sustainability → single-month noise or trend turning point? which follow-ups (PCE/PMI/minutes) will verify?Template quick-fill (CPI example):
| Step | Entry |
|---|---|
| ① Data | US CPI YoY (headline + core) |
| ② Expected | Core CPI forecast 3.2%; market pricing a 70% chance of a September cut |
| ③ Actual | Core 3.0%, 0.2pp below forecast |
| ④ Structure | Slowing shelter was the main driver; energy dragged |
| ⑤ First reaction | Treasury yields fell, gold jumped, Nasdaq gapped up |
| ⑥ Sustainability | Wait for PCE in two weeks; if PCE falls in the same direction, the cutting logic is confirmed |
Risk Warning
⚠️ Risk Warning
Macro data releases are often accompanied by violent gaps, widened bid-ask spreads, and vanishing liquidity; heavy positions or market orders can fill at terrible prices. The "market expectations" underlying expectation-gap judgments shift dynamically, and initial prints are frequently revised substantially — decisions based on initial data carry extra uncertainty. All indicator definitions, release arrangements, and numeric examples here are teaching references only; defer to the latest official definitions and latest market conditions. This article is not investment advice.