Cracking the Code: Enhancing Financial Forecasts with Cross-Company Attention

Financial forecasting models have long been criticized for their narrow focus on individual tickers, missing out on the interconnected nature of today's global economy. A factory hiccup in Taiwan shouldn't just affect local stocks. It sends ripples across global markets, impacting giants like Apple before individual models even blink. Enter a new solution: a cross-company attention model that promises to align financial forecasting with the real-time, interconnected world.

Introducing a Hybrid Architecture

This new system isn't just a splash of machine learning lingo. It's a sophisticated Rust-Python streaming architecture designed for speed and precision. On the data ingestion front, a zero-copy Rust edge processes news records in around 100 nanoseconds, scanning through the target equity universe in just 1.2 microseconds. That kind of speed isn't just impressive. It's necessary in a high-frequency trading environment where milliseconds can mean millions.

Real-Time Inference with Neural Hawkes Process

The system's heart is its multivariate Neural Hawkes Process, which utilizes per-node continuous-time LSTM states and bilinear latent projections. This enables it to propagate financial signals across companies. Think of it as a dynamic web, constantly updating and adapting to the latest news. An adaptive pruning rule ensures that the system doesn't get bogged down, keeping computational costs in check even as the market data floods in.

Here's where it gets interesting. The complete architecture achieves an end-to-end latency of about 13 milliseconds per incoming news record on a standard CPU. In a world where speed dictates success, that's a big deal. Evaluated on a month-long holdout of the FNSPID corpus, covering 638 articles across 47 tickers, it delivered a precision lift of 1.70 times over random predictions at the 90th-percentile next-day return threshold. And, it outperformed a same-sector baseline by 3.36 times.

The Importance of Dynamic Attention

Why does this matter? Because the model's precision collapses to zero when the graph topology is removed, confirming that its dynamic attention network is the only driver of cross-company signals. This isn't just a flashy feature. It's a necessity for accurate forecasting in today's interconnected markets. Can any single-asset model claim to do the same?

In the end, it's not about throwing more models into a GPU cluster. It's about bridging the gap between isolated ticker predictions and the real-world complexities they exist within. If the AI can hold a wallet, who writes the risk model? This new architecture might just be the start of a solution, showing that the intersection is real. Ninety percent of the projects aren't.