{"slug": "show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source", "title": "Show HN: Ford's $1.3B USMCA risk, priced from prediction markets (open source)", "summary": "An open-source tool called Election Risk Desk, built on the Agent Skills standard and usable in Claude Code, Codex, Cursor and Gemini, prices how the Nov 3, 2026 US elections could affect individual companies' profits using Kalshi and Polymarket prediction markets. In its Ford example, run on Oct 28 prices, the tool rates election risk LOW and policy risk MEDIUM, with the largest exposure a −$1.30B annual hit (−19.2% of profit) if the US gives notice to leave USMCA before 2027, an outcome Kalshi prices at 6%. Across eight companies run end to end, only PG&E drew a High election risk rating, and the tool's stated surprise is that which party wins on Nov 3 moves these odds less than headlines suggest.", "body_md": "**An AI risk desk for what the Nov 3, 2026 US elections could do to a company's profit.**\n\nName a company. Your coding agent staffs a desk: analysts map how the company makes its profit, what each\ncandidate would do, and every Kalshi and Polymarket market that can move it. A Risk Officer sizes each risk\nwith a small engine, a Red Team flags the weak numbers, and every run ends in two verdicts, with sources.\nWorks in Claude Code, Codex, Cursor, Gemini and any agent that reads [Agent Skills](https://agentskills.io).\n\n**See it without installing:** live reports for [Ford](https://juwonyee.github.io/election-risk/examples/F/report.html), [PG&E](https://juwonyee.github.io/election-risk/examples/PCG/report.html),\n[Coinbase](https://juwonyee.github.io/election-risk/examples/COIN/report.html) and [DraftKings](https://juwonyee.github.io/election-risk/examples/DKNG/report.html).\n\nInstall it (one line, [below](#install)), then ask your agent:\n\n```\nWhat does Nov 3 mean for Ford?\n```\n\nThe desk board, in the chat and at the top of a local web report ([Ford's](https://juwonyee.github.io/election-risk/examples/F/report.html), prices of Oct 28):\n\n| Analyst | Signal | Why | \n|---|---|---|\n| Exposure | EXPOSED | biggest if it happens: −$1.30B a year (−19.2% of profit) | \n| Policy | WEAK LINK | for results of 1% of profit or more, who wins moves the chance by 5 points at most | \n| Market | PRICED | 3 markets on Kalshi and Polymarket | \n| Red Team | CAUTION | 8 flags to read | \n\n**Risk Officer:** Election risk **LOW** · Policy risk **MEDIUM**\n\n- **Election risk LOW:** nothing passes the bar. The biggest is +$1.00B a year if Congress repeals the\nSection 232 auto and metals tariffs, but only 5% even if Democrats win.\n- **Policy risk MEDIUM:** Kalshi puts 6% on the US giving notice to leave USMCA before 2027. For Ford:\n−$1.30B a year (−19.2% of profit).\n- **Red Team:** the USMCA impact is an estimate, and at the high end of its range (−$2.50B) policy risk would\nbe HIGH.\n\nAnd a card to share (the report saves it as PNG):\n\nEight household names, run end to end (Ford on Oct 28 prices, the rest on Oct 27). Each links to its full summary, profit structure and sources.\n\n| Company | Election risk | Policy risk | Biggest risk | Market | Profit, per year | \n|---|---|---|---|---|---|\n| [PG&E](https://github.com/juwonyee/election-risk/blob/main/examples/PCG/summary.md) | **High** | none | Becerra wins (96%) → 30% chance of a rate freeze, vs 0% under Hilton | Kalshi | **−$600M** (−12.6%) | \n| [DraftKings](https://github.com/juwonyee/election-risk/blob/main/examples/DKNG/summary.md) | Medium | **High** | Congress bans sports contracts on prediction markets in 2026 | Polymarket 18% | **+$150M** (+24.2%) | \n| [Coinbase](https://github.com/juwonyee/election-risk/blob/main/examples/COIN/summary.md) | Low | **High** | Clarity Act (crypto market structure) becomes law before 2028 | Kalshi 21% | **+$370M** (+25.8%) | \n| [Ford](https://github.com/juwonyee/election-risk/blob/main/examples/F/summary.md) | Low | Medium | US gives notice to leave USMCA before 2027 | Kalshi 6% | **−$1.3B** (−19.2%) | \n| [GM](https://github.com/juwonyee/election-risk/blob/main/examples/GM/summary.md) | Low | Medium | Same USMCA notice | Kalshi 8% | **−$1.7B** (−13.3%) | \n| [Disney](https://github.com/juwonyee/election-risk/blob/main/examples/DIS/summary.md) | none | Medium | Federal film and TV production tax credit becomes law | Kalshi 26% | **+$400M** (+2.3%) | \n| [Uber](https://github.com/juwonyee/election-risk/blob/main/examples/UBER/summary.md) | Low | none | California AG suit ends in a Lyft-sized settlement: 70%, and who wins moves it at most 11 points | Kalshi | **−$170M** (−3.1%) | \n| [Starbucks](https://github.com/juwonyee/election-risk/blob/main/examples/SBUX/summary.md) | Low | Low | New York City enacts a $30 minimum wage by 2030 | Kalshi 8% | **−$60M** (−2.0%) | \n\n\"none\" means no market was linked to the company on that side.\n\n**The surprise: who wins on Nov 3 moves these odds less than the headlines suggest.** Only one of the eight\ngets a high election risk. The bigger risks come from markets that price the policy directly and barely\ndepend on the vote: the Clarity Act and sports contracts (high), USMCA for Ford and GM (medium). Where the\nelection does matter, the Red Team keeps it honest: research finds a new utility commission moves allowed\nreturns by about 0.15 points, and attorneys general of both parties sue companies, so PG&E and Uber carry\nthose flags.\n\n**Not investment advice.** Analyst drafts for human review. No recommendation to buy, sell or hold any\nsecurity or to trade any contract. Markets are shown as information only.\n\nFour skills, one per desk role. Each works on its own; `election-risk` calls the other three by name.\n\n| Role | Skill | Ask it on its own | \n|---|---|---|\n| Exposure analyst | **profit-structure** | \"Which of Ford's profit lines can a government move?\" | \n| Policy analyst | **candidate-positions** | \"What would California's next governor do on electricity rates?\" | \n| Market analyst | **prediction-markets** | \"What are the odds on a data-center moratorium?\" | \n| Risk Officer + Red Team | **election-risk** | \"What does Nov 3 mean for Uber?\" | \n\n- **The agent researches, the engine calculates.** Every number comes from a small Python engine (standard\nlibrary only), not from the model's arithmetic.\n- **Two kinds of market.*** Direct* : the market resolving Yes is the policy, so its price is the chance.*Race* : the market prices who wins, times the chance the policy follows each winner.\n- **Two verdicts.*** Election risk* = share of profit × how much who wins moves the chance.*Policy risk* =\nshare of profit × the market's chance. High at 2% of profit or more, Medium at 0.5%. Only results that pass\nthe bar count (at least 1% of profit, volume 10,000, chance 5%); when none does, the board says what came\nclosest and why it falls short.\n- **Every impact shows how it was built** : the formula with each number and its source, and a low-high range.\n- **Red Team.** It checks whether each verdict survives its own range: \"Policy risk HIGH ... would be LOW at\nthe low end\" means the verdict rests on one estimate. It also flags positions with no source link (counted\nas 0), thin markets and estimated impacts.\n\n**Ask your agent** (recommended):\n\n```\nInstall election-risk from https://github.com/juwonyee/election-risk\n```\n\n## Or install it yourself\n\n```\n# Claude Code\nclaude plugin marketplace add juwonyee/election-risk\nclaude plugin install election-risk@election-risk\n\n# Codex\ncodex plugin marketplace add juwonyee/election-risk\ncodex plugin add election-risk@election-risk\n\n# Gemini\ngemini extensions install https://github.com/juwonyee/election-risk\n\n# Cursor\ngit clone --depth 1 https://github.com/juwonyee/election-risk ~/.cursor/plugins/local/election-risk\n\n# Any other agent, via the skills CLI\nnpx skills add juwonyee/election-risk\n```\n\nInstall the plugin or the skills, not both (you would see every skill twice).\n\nNeeds Python 3.10+. No packages, no API keys.\n\nRace results need to know what each candidate would actually do. That lives in a **policy book**: one file\nper Nov 3 contest, every position with a source link, and the chance it happens if they win, from a\n[research-based probability table](https://github.com/juwonyee/election-risk/blob/main/skills/candidate-positions/references/probability-table.md)\n(veto override rates, governors' proposal success, ballot measure pass rates and more).\n\nThe book is not in this repo, on purpose: candidate positions in a public repo could be edited by anyone. On the first run your agent asks you to pick:\n\n- **Build it yourself** : your agent researches every contest with sources. It takes a while, and it's reused\nfor every company afterwards.\n- **Download ours** : ready in a minute, built by us on the date shown, possibly out of date. Your agent tells\nyou that and asks before running`erisk book download --accept-risk` .\n\nEvery report prints which book it used (built or downloaded, date, fingerprint) and when prices were fetched.\n\n- **Chances are upper bounds.** The probability table comes from historical rates over a full term, used here\nfor about two years.\n- **Impacts are often estimates.** Each one is labeled verified or estimated and shows its formula, so you can\ncheck it.\n- **The book can be wrong or out of date.** Positions with no source count as 0, and the agent lists rows it\ndoubts as weak links.\n- **Only priced risks.** A risk with no Kalshi or Polymarket market is left out. Unreachable sources are\nlisted, never worked around.\n- **Prices move.** The examples use Oct 27 prices. Raw price snapshots are not kept in this repo (venue terms).\n\nIt won't recommend trades, size positions, help open market accounts, or work around a venue's regional restrictions. It names no small businesses (franchised chains are discussed as \"a typical store\").\n\n```\npython3 engine/erisk.py markets pull                         # every open Nov 3 + policy market\npython3 engine/erisk.py book status                          # policy book coverage\npython3 engine/erisk.py book find --tag utilities --state CA\npython3 engine/erisk.py markets show GOVPARTYGA-26 --venue kalshi\nERISK_OUT=examples python3 engine/erisk.py compute --company SO --no-open   # test fixture, synthetic prices\n```\n\nSee [AGENTS.md](https://github.com/juwonyee/election-risk/blob/main/AGENTS.md). Edit `engine/`, then `python3 scripts/sync_engine.py`; check with\n`python3 scripts/validate_skills.py` and `python3 -m unittest discover tests`.\n\nMIT.", "url": "https://wpnews.pro/news/show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source", "canonical_source": "https://github.com/juwonyee/election-risk", "published_at": "2026-10-29 12:38:02+00:00", "updated_at": "2026-10-29 12:47:23.562410+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "ai-products"], "entities": ["Ford", "PG&E", "Coinbase", "DraftKings", "General Motors", "Disney", "Uber", "Starbucks"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source", "markdown": "https://wpnews.pro/news/show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source.md", "text": "https://wpnews.pro/news/show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source.txt", "jsonld": "https://wpnews.pro/news/show-hn-ford-s-1-3b-usmca-risk-priced-from-prediction-markets-open-source.jsonld"}}