Methodology

Sourcing

Events are detected directly from SEC EDGAR — the authoritative US filing system — by form type: Form 10 / 10-12B (spin-offs), DEFM14A (mergers), S-1 / F-1 (IPOs), Schedule 13E-3 (going private), Schedule 13D (activist), Schedule TO-T (tender offers), and 8-K item 1.03 (bankruptcy). Company financials come from SEC XBRL company-facts; ownership from Schedule 13D/13G and Form 4; short interest from FINRA; clinical trials from ClinicalTrials.gov; drug approvals from openFDA. All are public-domain or open data, free to redistribute.

How the pipeline works

The catalog is built and kept current by an automated pipeline that runs against SEC EDGAR — no manual data entry, no third-party data feeds:

1. 1. Search. We query EDGAR full-text search for each relevant form type within a date window.
2. 2. Group.Filings are grouped by the subject company's CIK (its permanent SEC identifier), so each company's event is one record.
3. 3. Enrich.We pull the company's current name, ticker, exchange and SIC industry from the SEC submissions API.
4. 4. Parse, strictly. For spin-offs we read the Information Statement for the parent, record/distribution dates and the share ratio — using tight, anchored patterns that skip rather than risk a wrong value.
5. 5. Store. Each record is upserted keyed on (event type, company), newest filing wins, so re-scans and later amendments correct earlier data instead of duplicating it.
6. 6. Refresh daily. A scheduled job re-scans a rolling window every day, catching new filings and amendments that finalise terms.

Coverage & completeness

We are deliberately honest about gaps rather than papering over them with estimates:

• Blank dates & ratios are normal.Many 10-12B filings are preliminary, so the distribution date or ratio isn't set yet — we show it blank until a filing states it, never a guess.
• Some companies have no current ticker.A spin-off that hasn't listed yet, or a target since acquired/delisted, simply has no ticker in SEC's data — we leave it empty rather than infer one.
• History reaches back to 2001. EDGAR full-text search covers 2001 onward; earlier filings need bulk-index ingestion, a planned extension.
• Expected dates are labelled.Where an upcoming spin-off's date isn't yet in a filing, any web-sourced expected date is shown as “~ est.” and kept strictly separate from SEC-confirmed dates.

Our rules

• Never guess.When a figure isn't cleanly available, we leave it blank — we never estimate or infer it.
• Everything is source-linked. Each event and datum points to the filing it came from, so you can verify it.
• No licensed prices. Stock prices, returns and index values require a paid redistribution licence, so we show none. We rely on fundamentals, ownership and structure instead — which the spin-off research weighs heavily.
• Facts, not advice. Signals are factors the research literature studies, not recommendations.

Spin-off signals

On each spin-off we surface the factors the academic and practitioner literature associates with spin-off outcomes — Greenblatt (You Can Be a Stock Market Genius, 1997); Cusatis, Miles & Woolridge (1993); Desai & Jain (1999); McConnell & Ovtchinnikov (2004). The composite “Spinoff Score” — a tally of favorable factors — follows the binary scorecard approach of Bülow & Mjörnemark (CBS, 2017) and Lindeborg & Falck (Lund, 2019), itself modeled on Piotroski's F-Score (2000). We implement the factors computable from free SEC data and deliberately omit the ones that need licensed data (analyst coverage; EV/EBIT, which needs a market price). Each is computed only from the company's own SEC filings:

Focus-increasing

Whether the spin-off enters a different sector than the parent (a pure-play). Focus-increasing spin-offs have historically outperformed diversifying ones (Desai-Jain).

Size vs parent

The spin-off's revenue relative to the parent's. Small spin-offs draw more forced index/institutional selling — the classic temporary-mispricing edge (Greenblatt).

Leverage

Liabilities ÷ assets. Parents sometimes load debt onto the spin-off; a clean balance sheet is favorable.

Operating margin

Profitability of the standalone business, from its own filings.

Return on capital (ROCE)

EBIT ÷ capital employed — the quality measure the spin-off scorecard research weighs most (Greenblatt; Bülow & Mjörnemark).

Tax-basis report

A Form 8937 accompanies tax-free §355 distributions — favorable for taxable holders.

Insider buying

Open-market purchases by new management (Form 4) — one of the strongest positive signals in the literature.

Activist holder

A 5%+ activist (Schedule 13D) is a potential value-unlock catalyst.

Time since spin-off

The documented premium concentrates in roughly the first one to three years.

The spin-off return premium is debated and weaker in recent samples (McConnell-Ovtchinnikov); these factors are descriptive, not a forecast or a recommendation.

Financial-health & forensic scores

On every company hub we compute four textbook scores from the company's own SEC XBRL figures — no market price, nothing estimated. Each is blank unless every input is cleanly present (we never guess), and each is a descriptive factor, not advice:

Altman Z″ (distress)

Book-value bankruptcy-distress score: 6.56·X1 + 3.26·X2 + 6.72·X3 + 1.05·X4, where X1–X4 are working capital, retained earnings, EBIT, and equity ÷ liabilities, scaled by assets. Above 2.6 = safe · 1.1–2.6 = grey · below 1.1 = distress. Uses no market cap, so it needs no licensed price (Altman, 1968/2006).

Sloan accruals

(net income − operating cash flow) ÷ assets. High positive accruals mean earnings aren't backed by cash — a classic earnings-quality red flag and restatement precursor (Sloan, 1996).

Piotroski F-Score

Counts how many of nine fundamental-health checks pass (profitability, leverage, efficiency), shown as passed / applicable. We use operating margin and total liabilities as documented proxies where the exact input isn't XBRL-tagged (Piotroski, 2000).

Beneish M-Score

An eight-ratio earnings-manipulation screen comparing the latest fiscal year with the prior one: M = −4.84 + 0.92·DSRI + 0.528·GMI + 0.404·AQI + 0.892·SGI + 0.115·DEPI − 0.172·SGAI + 4.679·TATA − 0.327·LVGI (indices of receivables/sales, gross margin, asset quality, sales growth, depreciation, SG&A, accruals and leverage). M above −1.78 historically flagged a higher likelihood of manipulation. It is a screen, not proof — we compute it only when all eight variables' inputs exist in both adjacent years and gross margins are positive (Beneish, 1999).