/forensic-accounting

description: Earnings-quality forensics — Beneish M-Score, Altman Z, Sloan accruals ratio, cash-vs-earnings divergence, SBC-adjusted FCF, revenue-recognition and capitalisation checks, auditor and related-party flags, scored as a Manipulation Risk Score 0–10

Forensic Accounting — Are the Reported Earnings Real?

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest market data for the ticker:

1. **Fetch current price** — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data. (Market cap feeds Altman's X4; everything else in this skill comes from the filings.) 2. **Pull the filings, not a data feed** — this skill needs **at least two, preferably three to five, consecutive fiscal years** of as-filed statements, plus their notes:

• **10-K Item 8**: income statement, balance sheet, cash-flow statement (for every index below you need year *t* and year *t−1*).
• **Revenue note** (ASC 606): disaggregation, contract assets / **unbilled receivables**, deferred revenue, remaining performance obligations, any bill-and-hold or principal-vs-agent judgment.
• **Receivables / allowance note**, **inventory note** (reserves, LIFO/FIFO changes), **PP&E and intangibles notes** (capitalised software, useful-life changes), **capitalised contract costs** (ASC 340-40), **related-party note**, **stock-compensation note**, **commitments and contingencies**.
• **Auditor's report** (opinion, **critical audit matters**, going-concern paragraph, tenure), **Item 9A** (internal control over financial reporting — material weaknesses), and **8-K Item 4.01** (auditor change) / **Item 4.02** (non-reliance — restatement) for the last five years.
• **Earnings releases** for the non-GAAP reconciliation (8-K Exhibit 99.1), and **Form 4s** for insider sales around results.

Use as-filed figures; when a later filing restated a prior year, note both and compute the indices on the restated basis (the restatement itself is a finding). 3. **State your data source** — fill in the `Data & Sources` header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. 4. **Flag stale data explicitly** — if live data is unavailable, display this warning before proceeding:

> ⚠️ **Live data unavailable.** The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.

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📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the [Data & Accuracy](https://yennanliu.github.io/InvestSkill/data-and-accuracy.html) page and the first thing `result-validator` looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>

• `Retrieval: model memory` must be paired with `Confidence: LOW` — memory is a placeholder until confirmed against a primary source.
• Mixed sources: list each with its own as-of date rather than blending them.
• Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
• Data the user pasted is reported as `pasted by user`; do not upgrade its confidence beyond what the user's own source supports.

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🌐 Output Language — `--lang zh-TW`

English is the default. With `--lang zh-TW` — or when the user writes in Traditional Chinese and asks for a Chinese answer — write the whole analysis in Traditional Chinese (繁體中文, Taiwan usage), keeping it machine-checkable:

• **Translate** every heading, narrative paragraph, table header and bullet. Give each metric's English name in brackets the first time it appears — 自由現金流（Free Cash Flow, FCF） — so the reader can match it to the glossary and to English analyses.
• **Keep in English** tickers, figures and units, SEC form and item names (10-K Item 7, Form 4), the four `Data & Sources` field labels (`As of`, `Source`, `Retrieval`, `Confidence` — the Retrieval value may be written in Chinese, e.g. 使用者提供), and the Investment Signal box and JSON footer exactly as specified — field names and the BULLISH / NEUTRAL / BEARISH, HIGH / MEDIUM / LOW, BUY / HOLD / SELL, STRONG / MODERATE / WEAK values. Tools parse them.
• Title the invalidation section **論點失效條件（Thesis Invalidation）**, and close with the disclaimer in Chinese: **免責聲明：** 僅供教育用途，非投資建議。
• Use Taiwan financial terms: 殖利率、本益比、營業利益率、自由現金流、聯準會、選擇權、買權／賣權、除息日、預扣稅.

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Overview

Every valuation in this catalog takes reported earnings as an input. This skill asks the question that comes first: **how much of that earnings number is cash, how much is judgment, and do the patterns in the statements look like the patterns that preceded past manipulations and failures?** It runs the academic screens that have a track record (Beneish's M-Score, Altman's Z, Sloan's accruals anomaly), then the practitioner checks that catch what the models miss — revenue-recognition stretch, cost capitalisation, the widening gap between "adjusted" and GAAP earnings, stock compensation treated as free, and the governance tells (auditor changes, material weaknesses, restatements, related parties).

**This is a screen for risk, not an accusation.** A high reading means "the statements have the shape of trouble and deserve a skeptical read," not "this company commit