README.md

metadata

pretty_name: Plan B — Contrarian Deep-Value US Equities & AI 10-K Analysis
license: cc-by-nc-4.0
language:
  - en
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
  - text-generation
  - time-series-forecasting
tags:
  - finance
  - equities
  - stocks
  - sec-edgar
  - xbrl
  - value-investing
  - fundamental-analysis
  - 10-k
  - form-4
  - insider-trading
  - financial-nlp
configs:
  - config_name: daily_rankings
    default: true
    data_files:
      - split: train
        path: daily_rankings.jsonl
  - config_name: ai_10k_memos
    data_files:
      - split: train
        path: ai_10k_memos.jsonl
  - config_name: insider_trades
    data_files:
      - split: train
        path: insider_trades.jsonl
  - config_name: scan_stats
    data_files:
      - split: train
        path: scan_stats.jsonl

Plan B — Contrarian Deep-Value US Equities & AI 10-K Analysis

Plan B is a daily quantitative and fundamental research dataset covering deeply discounted, financially resilient, and heavily beaten-down US operating companies across the NYSE and Nasdaq.

Every trading day, Plan B screens the entire US equity universe (~6,000 SEC XBRL filers) using primary SEC EDGAR XBRL frames, 3-year weekly price series, SEC Form 4 open-market insider transactions, news sentiment, and LLM-synthesized 10-K/20-F deep-value investment memos.

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Dataset Snapshot

Metric	Value
Coverage Window	2026-11-02 → 2026-11-06 (5 daily scans)
Unique Shortlisted Companies	54 NYSE/Nasdaq tickers
Daily Ranking Observations	200 rows (68 features per row)
Structured AI 10-K/20-F Memos	16 filings analyzed
Open-Market Form 4 Insider Trades	305 verified transactions
Update Frequency	Daily (automated post-market scan)
License	CC-BY-NC-4.0 (Research & Non-Commercial)

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Dataset Structure & Files

Each table is provided in both CSV (ideal for Kaggle, Pandas, R, and DuckDB) and JSON Lines (.jsonl) (ideal for Hugging Face datasets, Spark, and LLM pipelines).

Table / Config	Files	Primary Key	Description
daily_rankings (default)	daily_rankings.csv daily_rankings.jsonl	(date, ticker)	Daily ranked contrarian shortlist (~40 companies/day) with composite scores, valuation ratios, SEC XBRL balance-sheet & cash-flow fundamentals, insider totals, and AI 10-K verdict summary.
ai_10k_memos	ai_10k_memos.csv ai_10k_memos.jsonl	(ticker, accession)	Skeptical value-investor memos generated from Item 1 (Business), Item 1A (Risk Factors), and Item 7 (MD&A) of each company's latest SEC Form 10-K or 20-F.
insider_trades	insider_trades.csv insider_trades.jsonl	(ticker, owner, kind, date, shares, form4_url)	Open-market insider purchases (P) and sales (S) parsed directly from SEC Form 4 XML filings over the trailing 180 days (excluding option grants/awards).
scan_stats	scan_stats.csv scan_stats.jsonl	date	Daily universe screening funnel: total filers, usable XBRL fundamentals, priced equities, and shortlisted count.

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Screening & Ranking Methodology

Plan B follows a strict Graham–Klarman–Buffett deep-value discipline. Financial institutions (SIC 6000–6799: banks, insurers, REITs, SPACs) are excluded so balance-sheet ratios remain comparable across operating businesses.

1. Hard Quantitative Screen (Cheap + Safe + Hated)

A company must pass all three pillars simultaneously:

• Cheap: P/E ≤ 12, or EV/EBIT ≤ 8, or trading below Net Current Asset Value (NCAV / Market Cap ≥ 1.0, a classic Benjamin Graham net-net).
• Safe: Positive stockholders' equity, current ratio ≥ 1.2, total liabilities ≤ 2.0× equity (or net cash position), 3-year share dilution ≤ 25%, and positive operating cash flow if net income is negative.
• Hated: Share price down at least 40% from its 3-year weekly high (dd_3y ≤ -0.40), with market cap ≥ $50M and share price ≥ $1.00.

2. Composite Score (0–100)

• Base Weighting: 40% Cheap (cheap_score) + 30% Safe (safe_score) + 30% Hated (hated_score, blending price drawdown and headline negativity news_neg).
• Bonuses: Net open-market Form 4 insider buying (up to +12 pts) and 3-year share count reduction via buybacks (+4 pts).
• Penalties: Share dilution, negative free cash flow, accrual divergence (net income significantly exceeding operating cash flow or operating income), one-off profits following consecutive loss years, and high goodwill/M&A intensity.
• AI 10-K Adjustment: After reading the 10-K filing, the composite score is adjusted by the AI analyst's verdict (CONTRARIAN BUY CANDIDATE, WATCH, or AVOID) and conviction (0–100).

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Quickstart Usage

Python (datasets — Hugging Face)

from datasets import load_dataset

# Load default config (daily_rankings)
rankings = load_dataset("elsiddik/planb-deep-value", "daily_rankings", split="train")
print(rankings.to_pandas()[["date", "rank", "ticker", "score", "pe", "ev_ebit", "dd_3y", "ai_verdict"]].head())

# Load structured 10-K AI memos
memos = load_dataset("elsiddik/planb-deep-value", "ai_10k_memos", split="train")

# Load Form 4 open-market insider trades
insiders = load_dataset("elsiddik/planb-deep-value", "insider_trades", split="train")

Python (pandas — Kaggle / Local CSV)

import pandas as pd

df = pd.read_csv("daily_rankings.csv")
memos = pd.read_csv("ai_10k_memos.csv")
insiders = pd.read_csv("insider_trades.csv")

# Filter highest-conviction ideas with net cash and insider buying
top_ideas = df[(df["net_cash"] > 0) & (df["insider_buy_value"] > 0)].sort_values(["date", "rank"])
print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_pct", "ai_verdict"]].head(10))

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Data Dictionary

1. daily_rankings (68 Columns)

Column	Type	Description
date	string	Scan date (YYYY-MM-DD).
rank	int	Daily rank (1 = highest composite score).
ticker, name, cik, exchange, sector	string/int	Ticker symbol, SEC registrant name, SEC CIK identifier, exchange (NYSE/Nasdaq), and SIC industry description.
score	float	Final Plan B composite score (0–100).
cheap_score, safe_score, hated_price_score, hated_score	float	Normalized sub-scores (0.0–1.0) for valuation, balance-sheet safety, price drawdown, and combined hated score.
news_neg, hate_why	float/string	Headline negativity score (0.0 loved → 1.0 hated) and short explanation of market pessimism.
first_seen, return_since_first	string/float	Date the ticker first entered the shortlist and cumulative price return since first_seen.
bonus, flags	list / string	Score bonuses (e.g., insider buying, shrinking shares) and accounting/risk flags.
price, mcap, ev	float	Latest share price (USD), market capitalization (USD), and enterprise value (Market Cap + Debt - Cash, USD).
dd_3y, ret_1y, high_3y, high_52w, low_52w, off_low	float	Drawdown from 3-year high (-0.75 = down 75%), 1-year return, 3-year/52-week highs & lows, and percentage above 52-week low.
pe, ev_ebit, pb	float	Price-to-Earnings, Enterprise Value-to-EBIT (capped at 0 when EV is negative), and Price-to-Book ratio.
earnings_yield, ebit_ev, fcf_yield, book_yield	float	Inverse valuation yields: NI / MCap, EBIT / EV, FCF / MCap, and Equity / MCap.
ncav, netnet, net_cash, net_cash_pct	float	Net Current Asset Value (CA - Total Liabilities), NCAV / MCap, Net Cash (Cash - Debt), and Net Cash / MCap.
current_ratio, liab_eq, fcf, profit_years	float/int	Current assets / current liabilities, total liabilities / equity, free cash flow (CFO - CapEx), and profitable years out of the last 3 FYs (0–3).
share_chg_3y, rev_growth, buyback_yield, div_yield, goodwill_pct, acq_pct	float	3-year share count change, YoY revenue growth, buybacks/MCap, dividends/MCap, goodwill/assets, and acquisitions/assets.
period, fy, assets, ca, cl, cash, equity, goodwill, debt, debt_known, liab	mixed	Raw SEC XBRL balance-sheet values (USD) and reporting period (CY2026Q3I, etc.).
shares, shares_old, shares_old_period	float/string	Latest common shares outstanding and shares outstanding 3 years prior.
ni, ni_1, ni_2, oi, cfo, capex, buyback, dividends, acquisitions, rev, rev_1	float	Raw SEC XBRL annual income statement and cash-flow values (USD) for the latest and prior fiscal years.
insider_buy_value, insider_sell_value, insider_buyers_count	float/int	Trailing 180-day open-market Form 4 purchase USD total, sale USD total, and distinct buyer count.
tenk_date, tenk_accession, tenk_form, tenk_url	string	Filing date, SEC accession number, form type (10-K / 20-F), and direct SEC EDGAR archive URL.
ai_verdict, ai_conviction, ai_balance_sheet, ai_value_trap_risk, ai_one_liner	mixed	AI 10-K analyst summary fields (CONTRARIAN BUY CANDIDATE / WATCH / AVOID, conviction 0–100, balance sheet rating, value-trap risk, and one-line thesis).

2. ai_10k_memos (21 Columns)

Column	Type	Description
ticker, name, cik, exchange, sector	string/int	Company identifiers and SIC industry classification.
accession, filing_date, read_on, filing_url	string	SEC accession number, filing date, scan date analyzed, and direct EDGAR filing URL.
verdict, conviction, balance_sheet, value_trap_risk	string/int	Overall verdict, conviction (0–100), balance-sheet rating (fortress/solid/stretched/weak), and value-trap risk (low/medium/high).
one_liner, business, why_hated, capital_allocation, management	string	Concise investment synthesis covering the business model, market pessimism, buybacks/dividends/M&A, and leadership.
bull, bear, red_flags	list / string	Bullet lists of key bull points, bear points, and accounting/governance/footnote red flags from the 10-K.

3. insider_trades (11 Columns)

Column	Type	Description
date	string	Transaction date (YYYY-MM-DD).
ticker, company_name, cik	string/int	Issuer ticker, company name, and SEC CIK.
owner, title	string	Reporting insider name and relationship/officer title (CEO, CFO, Director, 10% owner).
kind	string	Open-market transaction code: P (Purchase) or S (Sale).
shares, price, value	float	Number of shares traded, execution price per share (USD), and total dollar value (USD).
form4_url	string	Direct URL to the primary SEC Form 4 XML filing on EDGAR.

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Data Integrity, Privacy & Caveats

• Zero Credentials / Zero PII Guarantee: Every export passes an automated security gate (assertNoCredentials) that enforces an explicit column allowlist, scrubs email addresses, and scans against secret/token patterns before writing or uploading.
• XBRL Tagging Nuances: SEC fundamentals reflect company-reported US-GAAP XBRL tags. Unusual tagging or dual-class share structures can occasionally distort market cap or debt ratios; always cross-check the linked SEC filing (tenk_url).
• Research Only: This dataset is for quantitative, academic, and machine-learning research only and never constitutes financial or investment advice.