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name: neo4j-news description: Query news articles from Neo4j with fulltext and vector search. Use when fetching news data or searching news content.

Neo4j News Queries

Queries for News nodes with fulltext and vector search capabilities.

Basic News Queries

News for company in date range

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start_date AND n.created <= $end_date
RETURN n.id, n.title, n.teaser, n.created, n.channels
ORDER BY n.created DESC

News around filing (with anomaly filter)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start_date AND n.created <= $end_date
  AND r.daily_stock IS NOT NULL AND NOT isNaN(r.daily_stock)
RETURN n.title, n.channels, n.created, r.daily_stock, r.daily_macro
ORDER BY n.created

News by channel

CopyMATCH (n:News)-[:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.channels CONTAINS $channel  // e.g., 'Guidance', 'Earnings', 'M&A'
RETURN n.title, n.created, n.channels
ORDER BY n.created DESC

News with highest impact

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start_date AND n.created <= $end_date
  AND r.daily_stock IS NOT NULL AND NOT isNaN(r.daily_stock)
RETURN n.title, n.created, r.daily_stock, r.daily_macro,
       abs(r.daily_stock - r.daily_macro) AS impact
ORDER BY impact DESC
LIMIT 10

Latest news for company

CopyMATCH (n:News)-[:INFLUENCES]->(c:Company {ticker: $ticker})
RETURN n.title, n.teaser, n.created, n.channels
ORDER BY n.created DESC
LIMIT 10

Fulltext Search

Search news by keyword

CopyCALL db.index.fulltext.queryNodes('news_ft', $query)
YIELD node, score
RETURN node.title, node.created, score
ORDER BY score DESC
LIMIT 20

Search news for company

CopyCALL db.index.fulltext.queryNodes('news_ft', $query)
YIELD node, score
MATCH (node)-[:INFLUENCES]->(c:Company {ticker: $ticker})
RETURN node.title, node.created, score
ORDER BY score DESC
LIMIT 20

Search news body

CopyCALL db.index.fulltext.queryNodes('news_ft', $query)
YIELD node, score
RETURN node.title, node.teaser, substring(node.body, 0, 500) AS body_preview, score
ORDER BY score DESC
LIMIT 10

Vector Search

Semantic search (requires embedding)

CopyCALL db.index.vector.queryNodes('news_vector_index', $k, $embedding)
YIELD node, score
RETURN node.title, node.created, score
ORDER BY score DESC

Semantic search for company

CopyCALL db.index.vector.queryNodes('news_vector_index', $k, $embedding)
YIELD node, score
MATCH (node)-[:INFLUENCES]->(c:Company {ticker: $ticker})
RETURN node.title, node.created, score
ORDER BY score DESC

News with Returns

News impact on stock

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start_date AND n.created <= $end_date
  AND r.daily_stock IS NOT NULL
RETURN n.title, n.created,
       r.daily_stock, r.daily_industry, r.daily_sector, r.daily_macro
ORDER BY n.created

Aggregate news impact

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start_date AND n.created <= $end_date
  AND r.daily_stock IS NOT NULL AND NOT isNaN(r.daily_stock)
RETURN count(n) AS news_count,
       avg(r.daily_stock) AS avg_stock_return,
       avg(r.daily_stock - r.daily_macro) AS avg_excess_return

Return Analysis

Hourly vs daily return divergence

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.hourly_stock IS NOT NULL AND r.daily_stock IS NOT NULL
  AND ((r.hourly_stock > 0 AND r.daily_stock < 0) OR (r.hourly_stock < 0 AND r.daily_stock > 0))
RETURN c.ticker, n.title, r.hourly_stock, r.daily_stock, n.created
ORDER BY abs(r.hourly_stock - r.daily_stock) DESC
LIMIT 20

Companies outperforming market

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND r.daily_macro IS NOT NULL
  AND r.daily_stock > r.daily_macro + 5.0
RETURN c.ticker, n.title, r.daily_stock, r.daily_macro,
       r.daily_stock - r.daily_macro AS excess_return
ORDER BY excess_return DESC
LIMIT 20

Multi-level return coverage check

CopyMATCH ()-[r:INFLUENCES]->()
WITH count(*) AS total,
     count(r.daily_stock) AS has_daily_stock,
     count(r.hourly_stock) AS has_hourly_stock,
     count(r.daily_industry) AS has_daily_industry,
     count(r.daily_sector) AS has_daily_sector,
     count(r.daily_macro) AS has_daily_macro
RETURN total,
       round(100.0 * has_daily_stock / total) AS daily_stock_pct,
       round(100.0 * has_hourly_stock / total) AS hourly_stock_pct,
       round(100.0 * has_daily_industry / total) AS daily_industry_pct,
       round(100.0 * has_daily_sector / total) AS daily_sector_pct,
       round(100.0 * has_daily_macro / total) AS daily_macro_pct

Data Analysis

Count all INFLUENCES relationships

CopyMATCH ()-[r:INFLUENCES]->() RETURN count(r)

Count news with embeddings

CopyMATCH (n:News) WHERE n.embedding IS NOT NULL RETURN COUNT(n) as embedded_news

News embedding coverage

CopyMATCH (n:News) WHERE n.embedding IS NOT NULL
WITH COUNT(n) as embedded_count
MATCH (n2:News)
WITH embedded_count, COUNT(n2) as total_count
RETURN embedded_count, total_count, ROUND(100.0 * embedded_count / total_count) as coverage_pct

Find relationships with null returns

CopyMATCH ()-[r:INFLUENCES]->()
WHERE r.daily_stock IS NULL OR r.hourly_stock IS NULL
RETURN COUNT(*) as null_count

News with maximum daily_stock values

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND r.daily_stock <> 'NaN'
WITH n, c, toFloat(r.daily_stock) as daily_return
WHERE NOT isNaN(daily_return)
RETURN n.title, c.ticker, daily_return ORDER BY daily_return DESC LIMIT 10

News causing extreme market movements (>10%)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND ABS(toFloat(r.daily_stock)) > 10.0
RETURN n.title, c.ticker, r.daily_stock, r.daily_industry, r.daily_sector, r.daily_macro,
       r.hourly_stock, r.session_stock, n.created
ORDER BY ABS(toFloat(r.daily_stock)) DESC LIMIT 20

News causing extreme positive movements (>8%)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND r.daily_stock <> 'NaN' AND toFloat(r.daily_stock) > 8.0
RETURN n.title, c.ticker, r.daily_stock, n.created
ORDER BY toFloat(r.daily_stock) DESC LIMIT 20

Count news with daily_stock > 10%

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND r.daily_stock <> 'NaN' AND toFloat(r.daily_stock) > 10.0
RETURN COUNT(n) AS news_count

News driving stocks below market (-3%)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock < r.daily_macro - 3.0
RETURN n.title, c.ticker, r.daily_stock, r.daily_macro
ORDER BY r.daily_stock LIMIT 20

Opposite hourly vs daily returns (with NaN handling)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.hourly_stock IS NOT NULL AND r.hourly_stock <> 'NaN'
  AND r.daily_stock IS NOT NULL AND r.daily_stock <> 'NaN'
WITH n, c, r, toFloat(r.hourly_stock) as hourly_return, toFloat(r.daily_stock) as daily_return
WHERE NOT isNaN(hourly_return) AND NOT isNaN(daily_return)
  AND ((hourly_return > 0 AND daily_return < 0) OR (hourly_return < 0 AND daily_return > 0))
RETURN n.title, c.ticker, hourly_return, daily_return, n.created
ORDER BY abs(hourly_return - daily_return) DESC LIMIT 20

Complete return path (all levels)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.daily_stock IS NOT NULL AND r.daily_industry IS NOT NULL
  AND r.daily_sector IS NOT NULL AND r.daily_macro IS NOT NULL
  AND ABS(toFloat(r.daily_stock)) > 3.0
RETURN n.title, c.ticker,
       r.daily_stock as stock_return, r.daily_industry as industry_return,
       r.daily_sector as sector_return, r.daily_macro as market_return
ORDER BY ABS(toFloat(r.daily_stock)) DESC LIMIT 20

Companies with same-day report and news impact

CopyMATCH (c:Company)<-[:PRIMARY_FILER]-(r:Report)
WITH c, r, date(datetime(r.created)) as report_date
MATCH (n:News)-[rel:INFLUENCES]->(c)
WHERE date(datetime(n.created)) = report_date AND rel.daily_stock IS NOT NULL
RETURN c.ticker, r.formType, n.title, rel.daily_stock
ORDER BY ABS(toFloat(rel.daily_stock)) DESC LIMIT 20

News impact on SPY market index

CopyMATCH (n:News)-[r:INFLUENCES]->(m:MarketIndex)
WHERE m.ticker = 'SPY' AND r.daily_macro IS NOT NULL AND ABS(toFloat(r.daily_macro)) > 1.0
RETURN n.title, r.daily_macro, n.created
ORDER BY ABS(toFloat(r.daily_macro)) DESC LIMIT 20

SPY daily returns from news

CopyMATCH (n:News)-[r:INFLUENCES]->(m:MarketIndex)
WHERE m.ticker = 'SPY' AND r.daily_macro IS NOT NULL
RETURN n.title, r.daily_macro, n.created
ORDER BY ABS(toFloat(r.daily_macro)) DESC LIMIT 20

Hourly sector returns for tech vs healthcare

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.hourly_sector IS NOT NULL AND r.hourly_sector <> 'NaN'
  AND datetime(n.created) > datetime() - duration('P30D')
  AND (c.sector = 'Technology' OR c.sector = 'Healthcare')
WITH n, c, r, toFloat(r.hourly_sector) as hourly_sector_return
WHERE NOT isNaN(hourly_sector_return)
RETURN c.sector, n.title, c.ticker, hourly_sector_return, n.created,
       CASE WHEN hourly_sector_return > 0 THEN 'Positive'
            WHEN hourly_sector_return < 0 THEN 'Negative' ELSE 'Neutral' END as return_direction
ORDER BY c.sector, hourly_sector_return DESC LIMIT 50

Companies with news outperforming macro in last 30 days

CopyMATCH (n:News)-[rel:INFLUENCES]->(c:Company)
WHERE datetime(n.created) > datetime() - duration('P30D')
  AND rel.daily_stock IS NOT NULL AND rel.daily_stock <> 'NaN'
  AND rel.daily_stock > rel.daily_macro AND rel.daily_macro > 0
RETURN DISTINCT c.ticker, n.title, rel.daily_stock, rel.daily_macro
ORDER BY rel.daily_stock DESC LIMIT 20

Recent news with populated data (last 7 days)

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE datetime(n.created) > datetime() - duration('P7D')
  AND n.title IS NOT NULL AND n.title <> ''
  AND c.ticker IS NOT NULL AND c.ticker <> ''
  AND r.daily_stock IS NOT NULL AND r.daily_stock <> 'NaN'
WITH n, c, toFloat(r.daily_stock) as daily_return
WHERE NOT isNaN(daily_return)
RETURN n.title, c.ticker, daily_return
ORDER BY datetime(n.created) DESC LIMIT 20

News events from past week with market impact

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE datetime(n.created) > datetime() - duration('P7D') AND r.daily_stock IS NOT NULL
RETURN n.title, c.ticker, r.daily_stock, n.created
ORDER BY ABS(toFloat(r.daily_stock)) DESC LIMIT 30

Industries with divergent company vs industry returns

CopyMATCH (n:News)-[r:INFLUENCES]->(c:Company)
WHERE r.hourly_industry IS NOT NULL AND r.hourly_industry <> 'NaN'
  AND r.hourly_stock IS NOT NULL AND r.hourly_stock <> 'NaN'
  AND datetime(n.created) > datetime() - duration('P7D')
WITH n, c, r, toFloat(r.hourly_industry) as industry_return, toFloat(r.hourly_stock) as stock_return
WHERE NOT isNaN(industry_return) AND NOT isNaN(stock_return)
  AND industry_return < 0 AND stock_return > 0
RETURN DISTINCT c.industry LIMIT 100

News by Market Session

Pre-Market News Impact

CopyMATCH (n:News)-[rel:INFLUENCES]->(c:Company)
WHERE n.market_session = 'pre_market'
  AND ABS(rel.session_stock) > 2.0
RETURN n.title, c.ticker, n.created,
       rel.session_stock as pre_market_impact,
       rel.daily_stock as full_day_impact
ORDER BY ABS(rel.session_stock) DESC
LIMIT 20

Post-Market News Impact

CopyMATCH (n:News)-[rel:INFLUENCES]->(c:Company)
WHERE n.market_session = 'post_market'
  AND ABS(rel.session_stock) > 2.0
RETURN n.title, c.ticker, n.created,
       rel.session_stock as post_market_impact,
       rel.daily_stock as full_day_impact
ORDER BY ABS(rel.session_stock) DESC
LIMIT 20

Industry-Wide News Events

CopyMATCH (n:News)-[rel:INFLUENCES]->(i:Industry)
WHERE ABS(rel.daily_industry) > 2.0
RETURN n.title, i.name as industry,
       rel.daily_industry as industry_impact,
       n.created
ORDER BY ABS(rel.daily_industry) DESC
LIMIT 20

Sector-Wide News Events

CopyMATCH (n:News)-[rel:INFLUENCES]->(s:Sector)
WHERE ABS(rel.daily_sector) > 1.0
RETURN n.title, s.name as sector,
       rel.daily_sector as sector_impact,
       n.created
ORDER BY ABS(rel.daily_sector) DESC
LIMIT 20

News Around Earnings Calls

CopyMATCH (c:Company {ticker: $ticker})-[:HAS_TRANSCRIPT]->(t:Transcript)
WITH c, t, datetime(t.conference_datetime) as call_date
ORDER BY call_date DESC
LIMIT 1
MATCH (n:News)-[:INFLUENCES]->(c)
WHERE datetime(n.created) > call_date - duration('P2D')
  AND datetime(n.created) < call_date + duration('P2D')
RETURN n.title, n.created,
       CASE
         WHEN datetime(n.created) < call_date THEN 'Before Call'
         ELSE 'After Call'
       END as timing
ORDER BY n.created
LIMIT 20

Notes

• News.channels is a JSON string. Use CONTAINS for filtering. Example: ["News", "Guidance"].
• News.tags and News.authors are also JSON strings.
• News.created and News.updated are ISO strings.
• News.market_session values: in_market, pre_market, post_market, market_closed.
• Data gap: 1,746 News→Company edges (0.9%) have daily_industry but daily_stock is NULL.
• Returns on INFLUENCES depend on target: Company edges have daily_stock; Sector/Industry/MarketIndex edges don't.
• Vector index: news_vector_index on News.embedding (float[]).
• Fulltext index: news_ft covers title, body, teaser.

Known Data Gaps

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Version 1.1 | 2026-01-11 | Added self-improvement protocol