Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs
arXiv:2606.05733v1 Announce Type: new Abstract: Per-ticker forecasting models dominate financial time-series work yet remain blind to cross-company propagation: a foundry disruption in Taiwan does not register in a single-asset model until Apple's own price has already moved. To address this limitation, we introduce a heterogeneous Rust-Python streaming architecture that maps cross-company attention as a continuous-time graph driven directly from text. We show that on the ingestion side, a zero-copy Rust edge parses news records in $\sim$100 ns and scans the target equity universe in $\sim$1.2
The increasing volume and velocity of unstructured data, particularly news, necessitates more sophisticated, real-time methods for financial market analysis to capture rapid propagation events.
Sophisticated forecasting models are moving beyond single-asset analysis to incorporate cross-asset contagion, enabling more accurate and proactive risk management and investment strategies.
Financial models can now integrate immediate, text-driven cross-company interactions on a continuous-time basis, moving beyond per-ticker limitations.
- · Quantitative hedge funds
- · High-frequency trading firms
- · Financial data analytics companies
- · Risk management platforms
- · Traditional algorithmic trading strategies
- · Financial news aggregators relying on batch processing
- · Retail investors without advanced tools
Financial market participants gain real-time insight into ripple effects across interconnected companies.
Investment strategies become more resilient to sudden supply chain or geopolitical shocks by anticipating cross-asset price movements.
The development of similar real-time 'attention graph' analytics could extend to geopolitical forecasting or social sentiment analysis, impacting national security and public policy.
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Read at arXiv cs.LG