canonical/modules/M065-post-mortems-and-error-taxonomy.md

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<!-- Module: 065 | Title: Post-Mortems and Error Taxonomy -->

## PART XIII - RISK, PORTFOLIO CONTEXT, AND DECISION MAKING | MODULE 065

# Post-Mortems and Error Taxonomy

> Mission. Separate thesis errors, sizing errors, timing errors, data errors, process errors, and luck.

## Decision output

Objective: Separate thesis errors, sizing errors, timing errors, data errors, process errors, and luck. The completed work product must be reproducible from evidence, show the downstream financial or decision effect when material, state the strongest contrary case, and define a dated update rule.

## Explicit operating procedure

1. Compare actual results with the dated forecast and decision journal, separating business outcome, stock outcome, and information available at the time.

1. Attribute error to data/source, accounting/definition, model mechanics, forecast assumption, industry/competitive thesis, valuation, event timing, risk interaction, communication, or process discipline.

1. Separate bad process with a good outcome from good process with a bad outcome and from pure luck.

1. Measure forecast error by driver, not only EPS/revenue, to identify systematic optimism, conservatism, or missed nonlinear relationships.

1. Write the process change required to prevent a repeat: new source, check, scenario, review gate, cadence, or training drill.

1. Feed recurring errors into analyst training, model QA, and research-priority rules rather than merely documenting them.

## Required evidence and model bridge

- Primary-source set: risk register, stress model, correlation/liquidity inputs, decision journal, forecast-error history. Preserve exact document/version, date, period, and source location for every material factual input used in post-mortems and error taxonomy.

- For each key concept - original record, data errors, accounting errors, model errors, forecast errors, thesis errors - state whether it is a reported fact, analyst calculation, management claim, external estimate, or judgment. Quantitative concepts must retain raw components and units; qualitative concepts must retain the specific evidence and counterevidence.

- Map only economically relevant findings into the model or decision record. Process-control modules such as post-mortems and error taxonomy may have no direct valuation line; in that case document the downstream error or governance risk the control prevents.

## Metrics and calculation controls

| Metric / concept | Construction | Required validation |
| --- | --- | --- |
| error contribution | Absolute or signed forecast/valuation error attributable to a specific error category divided by total error across the decision. | error contribution: Reperform the count from the defined population, inspect every material exception, and confirm the denominator/universe did not change between periods. |
| repeat-error rate | Previously identified error types recurring in later analyses divided by total subsequent analyses or relevant decisions. | repeat-error rate: Recalculate from same-scope numerator and denominator; confirm period, units, cohort/geography, and issuer definition; reconcile material differences to filings or operating data. |
| forecast calibration | Actual outcome relative to the analyst's stated forecast distribution, range, or scenario probabilities; track coverage, bias, and dispersion over time. | forecast calibration: Recalculate from same-scope numerator and denominator; confirm period, units, cohort/geography, and issuer definition; reconcile material differences to filings or operating data. |



## Worked application

> Case: macro rates hurt the stock while company operations meet assumptions.

- Reconstruct the relevant reported fact from primary evidence before interpreting the case. For post-mortems and error taxonomy, show the raw components rather than only the resulting ratio or narrative.

- Build the causal chain through original record, data errors, accounting errors, model errors, then identify which link is directly observed and which link remains an assumption.

- Calculate error contribution, repeat-error rate, forecast calibration from sourced components under the reported/base interpretation and at least one skeptical alternative interpretation.

- Translate the difference between cases into the variable that matters for post-mortems and error taxonomy: evidence quality, revenue, operating profit/NOPAT, free cash flow, invested capital, financing/dilution, risk, or valuation. Mark non-applicable links instead of inventing them.

- Expert consistency test: separate bad outcome from bad process and make each lesson change a concrete control.

- Precommit the specific future filing, KPI, customer/supplier observation, regulator action, or market input that would materially invalidate the post-mortems and error taxonomy conclusion.

## Failure tests

- FAIL if original record cannot be defined and reproduced from the source pack.

- FAIL if outcome quality is confused with process quality or if errors are not classified into source, model, assumption, timing, behavior, risk, and luck with a corrective action.

- FAIL if the post-mortems and error taxonomy conclusion depends on an unstated assumption, unreconciled definition, or evidence that cannot be traced to its source/version.

- FAIL if evidence materially inconsistent with the post-mortems and error taxonomy conclusion is omitted, reclassified, or dismissed without a documented definition, materiality, causal, timing, and source-quality analysis.

## Completion test

A senior reviewer must be able to reproduce the post-mortems and error taxonomy conclusion, vary the most sensitive assumption independently, trace the change through the model, understand the strongest opposing case, and identify the next evidence that would force an update. If any link is missing, the module remains open.