📋 SEC Form N-PX — Mutual Fund Proxy Vote Disclosures

Every proxy vote, every fund, straight from the SEC. Pull machine-readable mutual fund and ETF proxy voting records from SEC EDGAR Form N-PX annual disclosures. Filter by fund CIK, by issuer ticker, or by recent filings. Each record carries the fund's vote, the management recommendation, a voted_with_management flag, the proposal category (governance, compensation, environmental, social, shareholder), shares voted, and shares on loan. Governance research and ESG voting analytics at a fraction of Glass Lewis, ISS, or Morningstar Voting Rights pricing.

This actor unlocks the same data set that powers $1,500-and-up-per-seat enterprise products. Every U.S. registered investment company is required to disclose its proxy votes annually. The filings are public. The XML schema is documented. Nobody has built a clean institutional-grade API around it — until now.

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Why this actor beats the alternatives

Feature	This Actor	ISS Voting Analytics	Glass Lewis Proxy Talk	Morningstar Voting Rights	ProxyMonitor.org
Coverage	ALL N-PX filings (~5,000 funds, ~5M votes/yr)	Enterprise client list only	Enterprise client list only	~30,000 funds	S&P 500 only
Refresh latency	Real-time (within hours of EDGAR filing)	Quarterly updates	Quarterly updates	Daily	Annual
Pricing	Pay-per-vote ($0.05 / record)	Enterprise contract ($25K+/yr)	Enterprise contract ($30K+/yr)	$1,500+/yr per seat	Free, but limited
Filter by fund CIK	✅ Direct	Manual through UI	Manual through UI	✅	❌
Filter by issuer (cross-fund)	✅ One query	✅	✅	✅	Partial
Filter by recent filings	✅ daysBack window	❌	❌	❌	❌
Voted-with-management flag	✅ Auto-computed	✅	✅	✅	Partial
ESG categorization	✅ (env / social / gov / compensation)	✅	✅	✅	❌
Output format	JSON / CSV / Excel	Excel only	PDF + Excel	CSV via portal	HTML scrape
API / scriptable	✅ REST + SDK	❌ Portal only	❌ Portal only	Limited	❌
Setup time	< 1 minute	2-6 weeks	4-8 weeks	1 week	None
Source	SEC EDGAR (primary)	SEC EDGAR + ISS overlay	SEC EDGAR + Glass Lewis overlay	SEC EDGAR	Hand-curated subset

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What you get

Every dataset item is a single proxy vote record with these fields:

• Fund identifiers: fund_cik, fund_name, fund_series_id, fund_series_name
• Filing context: filing_id (EDGAR accession), filing_date, period_of_report
• Issuer identifiers: issuer_name, issuer_cusip, issuer_isin, issuer_ticker (when joinable)
• Vote context: meeting_date, proposal_index, proposal_text (truncated to 500 chars), proposal_category (governance / compensation / environmental / social / shareholder / other), proposal_source (ISSUER / SHAREHOLDER)
• The vote itself: vote (For / Against / Abstain / NoVote / Mixed), vote_management_recommendation, voted_with_management (boolean)
• Stewardship signal: shares_voted, shares_on_loan
• Audit trail: _source_url — direct link to the EDGAR ProxyVotingTable.xml for that filing

Every field is normalized — no parsing free-text "AGAINST (Withhold)" vs "WITHHELD" vs "AGAINST" across thousands of funds.

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Use cases

• ESG / sustainability fund managers — verify your own fund family's vote record, audit competitors' say-on-climate votes, build year-over-year ESG voting scorecards
• Activist hedge fund desks — see which mutual funds voted against management at your target companies, build a vote-prediction model for the next AGM
• Governance researchers — quantify board-recommendation override rates by fund family, cross-correlate fund manager turnover with voting behavior shifts
• Proxy-advisory firms (Glass Lewis / ISS competitors) — backtest your recommendation alignment with actual fund votes, benchmark adoption of your guidelines
• Compensation consultants — track say-on-pay support rates by industry, by market cap, by fund family
• Academic finance / governance researchers — replicate published studies (Aggarwal, Erel, Ferreira, Matos), build longitudinal datasets, panel-data analyses
• Shareholder activist NGOs — identify the fund families most receptive to climate / DEI / human-rights proposals
• Corporate IR teams — preview which institutional holders are likely to support or oppose upcoming ballot items

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Quick start

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

run = client.actor("nexgendata/sec-form-npx-mutual-fund-proxy-votes").call(
    run_input={
        "mode": "by_cik",
        "cik": "1120925",         # T. Rowe Price International Index Fund
        "maxVotes": 500,
        "proxyConfiguration": {"useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"]},
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["issuer_name"], "|", item["proposal_text"][:80],
          "|", item["vote"], "vs mgmt:", item["vote_management_recommendation"])

Modes

• recent_filings — scan all N-PX filings filed in the last daysBack days (default 30). Best for monitoring the late-summer filing rush.
• by_cik — pull every vote for one fund / fund-family. Requires cik. Use this for compliance audits or peer-fund comparisons.
• by_issuer — cross-fund view: every vote from every fund on a single issuer. Accepts a ticker (AAPL) or a company-name substring (Exxon). Use this for stewardship signal analysis.

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Pricing — premium institutional tier

This actor uses Pay-Per-Event billing:

Event	Price
Actor start	$0.005 per run
Proxy vote record	$0.05 per record returned

Typical query costs:

• Single-fund full year (10,000–50,000 votes) — $500–$2,500
• Single-issuer across all funds, one year (50–200 votes) — $2.50–$10
• One-fund-family quarterly compliance audit (~5,000 votes) — $250
• Smoke-test query (maxVotes: 20) — $1.00

We recommend setting a daily account cap of $5,000 / 100,000 votes to bound spend on accidental large pulls.

Compared with ISS Voting Analytics ($25K/yr) or a Morningstar Voting Rights seat ($1,500/yr), a $250 single-audit query pays for itself the first time you avoid a contract renewal.

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Related actors from NexGenData

Cross-reference proxy vote behavior with insider trading, institutional holdings, and corporate events:

Actor	What it does
13F Holdings Delta Tracker	Quarter-over-quarter changes in 13F institutional holdings
SEC Form 4 Insider Tracker	CEO / CFO / Director / 10%-owner buys & sells
Corporate Actions Tracker	Dividends, splits, M&A, spinoffs
Earnings Calendar	Forward earnings dates with consensus estimates
Analyst Targets Scraper	Wall Street price targets and rating changes
Pre-IPO Secondary Market	Private market valuations for pre-IPO names

The whole NexGenData fleet — 100+ premium financial / governance / market actors at apify.com/nexgendata?fpr=2ayu9b.

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FAQ

Q: Is this real SEC data, or a third-party aggregator? A: 100% primary-source EDGAR. We pull directly from https://www.sec.gov/Archives/edgar/data/... — the same XML files the SEC publishes. There is no third-party scraper, no rewrite layer. Your _source_url field points at the exact XML attachment we parsed.

Q: How current is the data? A: As soon as a fund files Form N-PX, EDGAR indexes it (usually within minutes) and our recent_filings mode will surface it on the next run. Most N-PX filings are submitted July through September each year for the 12-month period ending June 30.

Q: What about pre-2024 filings? I heard the format changed. A: SEC Form N-PX Modernization (effective July 2024) introduced the structured XML schema we parse. Pre-modernization filings are unstructured HTML and are skipped with a log message — they're not extractable into the same flat schema reliably. If you need pre-2024 data, request a custom build.

Q: Why is issuer_ticker sometimes null? A: Form N-PX requires CUSIP and ISIN but not ticker. We populate CUSIP/ISIN faithfully — join on CUSIP for ticker resolution if you have an OpenFIGI / Refinitiv mapping. Future versions will integrate the SEC company-tickers map.

Q: How does voted_with_management get computed? A: It is true when the fund's normalized vote (For/Against) equals the issuer management's normalized recommendation. Mixed votes, abstains, and absent recommendations resolve to false. The raw values are also exposed so you can rebuild the boolean with your own logic.

Q: What's the largest filing you can process? A: We stream the ProxyVotingTable.xml in 64KB chunks and iterate the parse so memory stays bounded. The largest filings we've parsed are about 100MB and 600,000 vote records per fund family. Set maxVotes to bound output volume and cost.

Q: How does this compare with the Apify 13F Delta Tracker? A: 13F shows what funds own. N-PX shows what funds believe. The two together — holdings + voting record — are the canonical stewardship dataset. Run both as a sister-actor query for any fund family.

Q: Can I get this data exported to Snowflake / BigQuery / S3? A: Yes. Apify's run-on-schedule + dataset export to S3 / GCS / Snowflake is built in. Set up a daily scheduled task and the data lands in your warehouse with no glue code.

Q: Are votes deduplicated? A: Yes — per (fund_cik, filing_id, proposal_index) tuple. Re-running the same query will not produce duplicate dataset rows for the same proposal.

Q: Why is the per-record price $0.05? A: This is institutional-grade data with a buyer pool that pays $1,500–$25,000 per seat for the same content elsewhere. The $0.05 / record price keeps the actor affordable for one-off audits ($250 covers a full-fund-year pull) while preserving margin on cross-fund issuer queries that institutions pay premium prices for. Compared to a Morningstar Voting Rights seat, you'll break even on the first focused query.

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About NexGenData

NexGenData publishes 100+ premium financial-data and governance actors on Apify — every major U.S. and global financial filing, market data feed, fund holdings, insider activity, and corporate-events source, all with consistent pay-per-event pricing and full audit trails to the primary regulator.

Browse the full fleet: apify.com/nexgendata?fpr=2ayu9b

Contact: contact@thenextgennexus.com

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SEC Form N-PX is a public disclosure required of all U.S. registered investment companies under Rule 30b1-4 of the Investment Company Act of 1940. This actor reproduces the public record. No subscription is required to view N-PX filings at SEC.gov; this product exists to make the data machine-readable, queryable, and reliably structured at institutional scale.