▲Top Tier Newswire

Methodology

Top Tier Newswire is a server-polled, AI-augmented financial newswire built for traders. This page documents exactly how we produce every signal that appears on the site and in the live terminal — so you can evaluate the data with the same rigour you'd apply to any institutional research feed.

1. News ingestion

We poll a fixed set of 40+ institutional sources continuously. The list spans wire-service primaries (Reuters, AP, Bloomberg Newswire derivatives via syndication), exchange filings (SEC EDGAR Form 4, US House Clerk PTR), broker desk research distilled by financial-press outlets (CNBC, WSJ, MarketWatch, Yahoo Finance, Seeking Alpha, Barron's, TipRanks, StockTitan, Morningstar), and curated alternative feeds (Benzinga, Reuters Markets, BBC Business, NPR Business). The list is fixed and tuned — we do not chase every new outlet that appears online. Every source is one a real desk monitors.

Each headline lands in our database with a normalised shape: headline text, full summary, source attribution, publish timestamp (verbatim from the publisher), our own fetched-at timestamp (which is monotonic and ours, so a publisher cannot game it), URL, and any tickers mentioned. We deduplicate on the URL path when available, otherwise on a slugified headline key, so a single story syndicated across multiple wires shows up once.

2. AI sentiment scoring (0-100)

Every headline is scored 0-100 by a fine-tuned language model — Newswire Tagger v14 at time of writing. 50 is neutral; values above 50 indicate bullish lean, below 50 bearish lean. The model was fine-tuned on a curated set of finance-text-to-sentiment pairs we maintain internally. We score on the full headline-plus-summary because headlines alone often miss the qualifier ("X reports record revenue" reads differently with the body line "...but warns of slowing demand into Q4").

The score is a research signal, not a directional prediction. Two stories rated 80 might both be bullish but have very different implications for the underlying name — sentiment doesn't replace reading the story. It exists so you can FILTER (show me only items rated ≥75 today) and AGGREGATE (what's the 24-hour average across NVDA mentions?).

3. AI ticker tagging

The same model identifies which US-listed tickers each headline materially references. A headline saying "Microsoft launches new Copilot tier" gets MSFT; one saying "tech sector roundup" without naming a specific stock gets nothing. We deliberately do NOT tag broad-market ETFs (SPY, QQQ) on generic "stocks" / "the market" coverage — only when an article is specifically about an ETF.

The tagging step also includes a "grounding gate" that drops any ticker the model emitted that doesn't have textual evidence in the headline (no symbol mention, no company-name mention, no canonical alias). This catches a known failure mode of fine-tuned taggers where they hallucinate mega-cap names onto loosely-related stories.

4. SEC insider transactions (Form 4)

We poll the SEC EDGAR system every 15 minutes for new Form 4 filings via the openinsider aggregator. Every filing is parsed into: insider name, role at the company, transaction type (Purchase / Sale / Award / Gift / etc.), share count, price per share, total value USD, transaction date, filing date, and a direct link to the original Form 4 PDF on SEC.gov. We retain 180 days of history (180 days is enough to capture multi-month accumulation patterns; older filings are preserved for any record referenced by the AI Top Trades model).

Form 4 must be filed within two business days of a transaction by US law. Latency from real-world trade → filing on EDGAR → our database is typically under 30 minutes.

5. US Congressional trades (House PTR)

The US STOCK Act of 2012 requires Members of Congress and senior staff to disclose personal trades via Periodic Transaction Reports (PTRs) within 45 days. We parse the daily ZIP feed published by the Clerk of the House plus each member's individual PTR PDF. Every disclosure is parsed into: member name, party, chamber, transaction type (P/S coded), amount range (PTRs disclose ranges, not exact values — e.g. $1,001-$15,000), transaction date, filing date, ticker, asset description, and a link to the source PDF.

The 45-day window means congressional trades are inherently a lagging signal — but they're a uniquely transparent one, and our parser handles the full taxonomy of asset types (common stock, options, ETFs) with explicit-non-stock filtering so "real estate fund" disclosures don't pollute equity tags.

6. AI Top Trades — long & short ranking

Our flagship signal. Every ~7 minutes the model rates a bounded universe of candidates (typically 25-30 finalists pre-selected by signal strength across news, X / social, insider Form 4 flow, and congressional trades) and returns:

  • Direction: long, short, or avoid (no edge — signals too thin or conflicting)
  • Conviction: 0-100, where 0-30 is weak / speculative, 31-60 moderate, 61-85 strong multi-signal alignment, 86-100 reserved for overwhelming corroboration
  • Thesis: 1-2 sentences naming the specific drivers
  • Drivers: enum-tagged signal types (e.g. insider_buy_cluster, news_bullish, price_divergence)

The board served to users is the top 10 longs and top 10 shorts by conviction. Avoids drop off — they're "the model saying no edge", not actionable.

Evidence packs

Each rating is grounded in a structured evidence pack the model sees per ticker — never the raw news / X firehose. The pack includes top headlines (~5), top tweets (~3), 90-day SEC insider net buying / selling, 90-day congressional buys / sales, live price + day move, multi-window price history (5-day, 1-month, 3-month, 6-month, 1-year returns plus 52-week range position and volume vs 30-day average), sector-relative move vs the appropriate SPDR / SMH / IBB sector ETF, and short interest / ownership stats from Finviz. The pack is bounded text — the model never sees the unbounded news DB.

Learning loop

We capture every rating with the exact evidence pack and entry price (price_at_call), then mature forward returns at 1d / 3d / 7d / 14d horizons against the entry. A nightly recompute aggregates outcomes per driver code and per 10-point conviction bucket, producing realised win rates that calibrate future ratings: if 70-bucket long calls historically hit 7-day 55%, the multiplier scales subsequent 70-bucket long convictions accordingly (bounded so a single noisy bucket can't swing scores wildly).

The model itself isn't being retrained — its weights are frozen — but a wrapper layer translates the model's raw conviction into an empirically calibrated one based on how its past calls actually played out. The mathematical adjustments are bounded; bootstrap-safe; and visible in the data (we keep both conviction_raw and conviction_adj on every row).

Failure modes (loud)

If the AI tagger is unreachable or returns malformed output, the run logs an error and keeps the last good board served. We do NOT fabricate ratings or fall back to a regex / heuristic substitute — silent fallbacks would mean users see the same board but worse signal underneath. Our policy is loud failures: a Top Trades run that can't talk to the model produces nothing this cycle and tries again next cycle.

7. Data freshness

The live terminal at app.toptiernewswire.com is real-time: news arrives via WebSocket push the moment our scraper inserts; the AI Top Trades board updates within seconds of a fresh scoring run; insider and congressional filings appear with sub-minute latency once they hit EDGAR / the Clerk feed.

This public site (toptiernewswire.com) shows data delayed by 4 hours across all news / X / sentiment / insider / congressional sources. The AI Top Trades archive page is restricted further: only picks at least 7 days old, where realised forward-return outcomes have matured. The current live board is never exposed on the public site.

8. What this is NOT

This is research. It is not investment advice. The sentiment scores, the Top Trades board, the insider and congressional flow — none of it is a recommendation to buy or sell. We do not custody assets, execute trades, or charge based on performance. We are a financial information service; you make your own decisions.

If you want to evaluate our work, the AI Top Trades archive at /ai-top-trades publishes every historical pick with its realised outcomes — you can see exactly how the model has done over time, including the failures.