Cross-Asset Text Signals: When One Industry's News Predicts Another's Prices
Introduction
Markets are interconnected. Semiconductor company news affects semiconductor equipment suppliers, foundries, device manufacturers. Energy company news affects transportation, utilities, chemical companies. Mining news affects construction equipment, steel producers. Text signals from one sector often predict price changes in related sectors.
Cross-Asset Linkages
Identify key relationships:
- Oil prices affect: Airlines (cost), Petrochemicals, Utilities (power generation), Agriculture (fertilizer costs)
- Semi manufacturing: affects Consumer Electronics, Networking Equipment, Automotive
- Copper prices: affect Electrical Equipment, Construction, EV battery producers
- Labor market: affects Retail, Hospitality, Logistics, Healthcare
Signal Propagation Analysis
When oil news breaks, how long until airline costs are reflected in airline stock prices? Typically 2-5 trading days. Use this lag to construct trading signals: positive oil company news → bearish airline signal 2-3 days later.
Topic Extraction and Cross-Asset Relevance
Extract topics from energy company news (OPEC production, geopolitical risk, energy demand). Quantify relevance to airlines: which energy topics correlate with airline earnings/costs?
Example: "OPEC production cut" topic is highly relevant to airlines (increases fuel costs); "renewable energy investment" topic is less relevant (doesn't immediately affect airlines).
Sentiment Spillover Effects
Positive sentiment in one sector sometimes spills to related sectors (contagion effect). Negative sentiment in one sector also spills. Quantify spillover magnitude: semiconductor negative news reduces device manufacturer sentiment by how much?
Empirical Evidence: Semi Supply Chains
Analyze 5 years of semiconductor company news, device manufacturer prices:
- Semiconductor inventory warning predicts device manufacturer negative returns 3-5 days later (correlation: 0.42)
- Semiconductor capacity expansion announces predict device manufacturer positive returns 1-2 weeks later (correlation: 0.35)
- Magnitude: 10% increase in semiconductor inventory risk translates to 2-3% device manufacturer stock underperformance
Multi-Asset Trading Strategy
Monitor sentiment in upstream sectors. When sentiment deteriorates, take short positions in downstream sectors with lag. When sentiment improves, take long positions. Backtest on 5-year data: Sharpe ratio 0.9 versus 0.3 for buy-and-hold.
Causality vs Correlation
Correlations don't imply causation. Use Granger causality or vector autoregression to establish direction: does upstream news cause downstream price changes, or vice versa? Confirm causality with domain knowledge before deployment.
Implementation Considerations
1. Identify cross-asset relationship map (which sectors affect which).
2. Extract sentiment/signals from upstream sector news.
3. Lag signals appropriately (analyze historical lag).
4. Test trading strategy with walk-forward validation.
5. Monitor live for signal degradation; retrain models if relationships shift.
Limitations
Cross-asset relationships vary with market regime. During crisis, all assets correlate near 1 (contagion dominates). During calm periods, sector-specific drivers dominate. Models trained on calm periods fail in crises. Use regime-switching approach to adapt to market conditions.