The Three-Step Macro Diagnosis: Empirical Regularity, Logic, Data, Pricing

The complete process of conducting a macro diagnosis on an economy can be collapsed into a three-step action chain: Step 1: Empirical regularity signals the position → Step 2: Logical reasoning illuminates the process → Step 3: Data verification, then transmitting the reasoned conclusions along the chain “economic pressure → policy pressure → policy turns from breaking to building → asset pricing” to asset prices; empirical regularity takes precedence over logic, and the order of the three steps cannot be reversed. This framework is an orchestrator rather than a sub-mechanism: it strings together empirical regularities, cycles, indicators, policy, and pricing into a fixed sequence for diagnosing a real economy; the mechanics of each sub-capability are not elaborated here.

The Framework As It Stands

This section is organized from the compiled research draft: the structure, terminology, and key formulations of the original framework are preserved, including editorial bridges and supplementary factual annotations; diagrams are drawn by the compiler following the structure of the source text.

Strong time-point notice: The course was recorded in 2019-01; Sections 7.1/7.2 are specific judgments and forecasts about “the Chinese economic picture at that time” (two rounds of deceleration = 2018Q4 + 2019H1, export growth, Canton Fair data, the timing of the policy pivot from breaking to building, etc.), all given as demonstrations at the time of the course in early 2019 and do not represent the current situation. What this entry extracts is the three-step framework itself; diagnosing the current economy requires re-running all three steps with current data — strictly avoid treating the then-current demonstrations as a description of present conditions.

I. Step 1: Empirical Regularity Signals the Position

The framework emphasizes: when judging economic position, first form a basic framework of empirical regularity in one’s mind, rather than beginning with logic — empirical regularity takes precedence over logic.

Operation: find the three drivers of economic growth: exports, real estate, and infrastructure investment, each one’s position determines the overall position.

Positioning technique: find a coordinate that has mean-reverting characteristics and regularity as a reference system. If a given indicator does not itself display cyclicality, there is always another indicator correlated with it that does; extract that one as the reference.

Using the course demonstration from late 2018 as an example:

Exports: use the current level of the U.S. inventory cycle as a coordinate (short-cycle exports depend on the inventory cycle); at that time the U.S. inventory cycle was at a high and rolling over, implying that export growth of 12%–13% was at an elevated level and likely to slow.
Real estate: new starts themselves do not show clear cyclicality; substitute the year-on-year change in housing prices in the top ten large cities as the reference indicator (the housing price cycle is stable, and at that time it was at the top), backing out that new starts would also mean-revert downward.

Step 1 conclusion: whether for exports or real estate, this round of economic growth was at an elevated position in the current cycle; future growth would in all probability move down from those highs.

II. Step 2: Logical Reasoning Illuminates the Process

Understand where the drivers of this cycle came from and how they will change; build a complete logical chain.

Using the course demonstration from late 2018 as an example:

Strong exports = global export expansion cycle; from April 2018 onward, the trade war materialized, U.S. tariffs on China escalated, the USD 200 billion tariff round on 2018-09-24 landed, compounded by the US and European economies peaking and slowing.
Strong real estate = prior destocking + subsequent push from shantytown monetary monetization (especially in tier-3/4/5 cities); policy became wary of the price bubble that an excessively high ratio of monetary shantytown compensation in tier-3/4 cities was generating.
Deeper reasoning: in this cycle, new starts abnormally ran ahead of sales, creating a scissors gap between new starts and sales; sales slow → receipts slow → new starts lose funding support → decline in new starts becomes inevitable.
Key signal: in Q3 2018, real estate prices began to soften (especially second-hand homes in tier-1 cities); price expectations are an important driver of sales, and softening → sales slow → new starts pulled down in the next period.

Logical chain: global trade recovery + shantytown monetization acceleration → high boom in export/real estate supply chains → economic resilience exceeded expectations in the first three quarters → exports peak, new starts peak → economic slowdown.

III. Step 3: Data Verification

Having formed empirical hypotheses and logical reasoning, the next step is data verification. Using the course demonstration from late 2018 as an example:

Export high-frequency verification:

From April 2018, Guangdong PMI data began to fall below the national average (Guangdong is upstream nationally, with orders outsourced to inland provinces), and Guangdong’s decline outpaced the national pace → confirming that export orders were indeed slowing.
Autumn 2018 Canton Fair (October): total order growth was roughly unchanged, but orders to the US fell approximately 30.3% (negative 30.3%).
Order lead-time rule: short orders within three months, medium orders three to six months, long orders six months or more; Q3 resilience was sustained by previously placed orders; what changed was new orders → effects show up in Q4 and the first half of the following year.

Real estate high-frequency verification: the second-hand home price index showed a clear softening in Q3, verifying the chain “prices slow → wait-and-see → sales slow → cash receipts affect new starts.”

Two-round deceleration conclusion: in Q4, export volumes/residential sales area/new starts began to decelerate modestly; in H1 2019, deceleration would further intensify (three reasons: high base, expectations transmit further and reinforce, the prior-order effect shows up in actual data); first round of deceleration in Q4 2018, second round in H1 2019.

IV. Transmission: Economic Pressure → Policy Pressure → Policy Turns from Breaking to Building

Employment pressure: the export supply chain is a labor-intensive sector; deteriorating conditions may generate employment problems in certain regions (especially after the spring return-to-work season), which led the Politburo meeting to put forward “six stabilities.”
Fiscal pressure: fiscal policy is pro-cyclical (pressure increases when the economy weakens), and expenditure is rigid, making it the second channel of pressure transmission.
Under the dual pressures of employment and fiscal, policy turns from “breaking” to “building,” shifting toward stabilizing growth (fiscal/infrastructure/financial/monetary all addressing growth).

V. Mapping to Asset Pricing: Risk-Appetite Trough vs. Fundamentals Trough

Policy turns from typical deleveraging → stabilizing growth (fiscal/infrastructure/financial/monetary)
        │
        ▼
Markets are efficient: current prices price in all observable factors
The only uncertainty is policy direction
        │
        ▼
Marginal policy reversal → market confirms the bottom determined by risk appetite
        │
        ├─ Risk-appetite trough: high-risk assets re-rate first
        │   Reason: during deleveraging, high-leverage/high-risk/low-credit assets bear the highest risk
        │           once policy stabilizes, risk on this segment declines most rapidly, changes most
        │           → re-rates first (but not excessively, or it becomes a bubble)
        │
        └─ Fundamentals trough (course demonstration from late 2018: approx. 2019Q2–Q3)
            Revenue and earnings still slowing; fundamentals trough has not yet appeared
            Later phase: transition from risk-appetite correction → fundamentals correction
 
Framework-external disturbances (e.g., U.S.-China trade friction bilateral negotiations) = pricing logic outside the main framework
  Better-than/worse-than-expected each trigger upward/downward revisions; must be handled separately from the main framework

Methodological convergence: The complete process from macro research to asset pricing = first form an empirical framework in mind → verify against data → reason out basic conclusions → map to asset prices (equities or bonds).

Compiler’s Perspective

Coordinates: Category = Cognitive Algorithm / axis_h = Fa / axis_v = Its Place in the Whole / soul_anchor = Cognitive differential and information differential: Can AI replace economists?

Connecting Level

The core claim of the three-step framework has one counter-intuitive feature in its step ordering: empirical regularity takes precedence over logic — the prior framework comes before the data, not the other way around (“look at the data first, then model”). This directly touches the essence of cognitive differential and information differential — a systematic empirical framework (mean-reverting reference indicators) generates a cognitive advantage not because it possesses more data, but because it knows which indicator to use as the coordinate. In the late-2018 course demonstration, new starts themselves showed no cyclicality; switching to the year-on-year housing price change in the top ten cities as the reference was the core contribution of the empirical framework; logic and data only became usable tools after that.

The Canton Fair U.S.-bound order figure of approximately -30.3% (October 2018 autumn Canton Fair) and the three-segment order lead-time rule (short orders within three months / medium orders three to six months / long orders six months or more) reveal the concrete operational difficulty of Step 3 data verification: the lag structure of high-frequency data is non-uniform, and using the wrong lag produces the error of “Q3 exports still stable → misjudged as no problem,” when in fact what changed was new orders, whose effect would not show up in the data until Q4. Equating “current-quarter data is stable” with “judgment: stable” is the most common specific error in data tracking under the old approach.

The risk-appetite trough preceding the fundamentals trough has a mechanism internal to this entry: during the deleveraging phase, high-risk assets bear risk premiums compressed to an extremely high level; once policy stabilizes, the assets on which the risk premium declines the most are precisely these — this is not the vague statement “market sentiment improves,” but the speed effect of the third yardstick (risk premium) reverting from an abnormally high level toward the mean. Revenue and earnings (the numerator) require much more time to improve, which is why the fundamentals trough lags the risk-appetite trough — the gap between them is a structural time difference, not a random one.

Source

Compiled draft z-0094 · collected 2026-07

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