New Hermes Framework Enhances Stock Forecasting by Leveraging Industry Correlations

Global: New Hermes Framework Enhances Stock Forecasting by Leveraging Industry Correlations

Researchers have introduced the Hermes framework, a hypergraph‑based model designed to improve stock time‑series forecasting by more fully exploiting correlations among industry sectors. The preprint, posted on arXiv in September 2025, outlines how the system addresses two notable gaps in existing approaches: limited treatment of inter‑industry lead‑lag dynamics and insufficient modeling of multi‑scale information.

Background

Accurate forecasting of financial time series supports decision‑making for investors, regulators, and analysts. While multivariate forecasting is common across domains, stock data uniquely exhibit strong inter‑industry relationships that can enhance predictive performance when properly captured. Prior hypergraph methods have incorporated industry links but have been criticized for handling lead‑lag interactions superficially and for neglecting information at varying temporal scales.

Methodology

The Hermes architecture introduces a hyperedge‑based moving aggregation module that employs a sliding window to perform dynamic temporal aggregation across industries. This design enables the model to flexibly represent lead‑lag dependencies, allowing information from one sector to influence predictions for another with appropriate temporal offsets.

Complementing this, a multi‑scale fusion module conducts cross‑scale, edge‑to‑edge message passing. By integrating signals from coarse and fine temporal resolutions while preserving the integrity of each scale, the framework seeks to capture both short‑term fluctuations and longer‑term trends within and across industry groups.

Results

Experimental evaluation on several real‑world stock datasets demonstrates that Hermes consistently outperforms leading benchmark models, achieving higher forecasting accuracy across multiple metrics. The authors attribute these gains to the combined effect of refined lead‑lag modeling and multi‑scale information integration.

Implications

If validated in broader settings, the Hermes framework could provide analysts with more reliable forecasts, potentially informing portfolio allocation, risk assessment, and regulatory monitoring. Enhanced predictive capability may also reduce reliance on heuristic methods that overlook nuanced industry interactions.

Future Directions

The study acknowledges that further testing on diverse market conditions and integration with alternative data sources remain necessary to assess robustness. Researchers suggest that extending the hypergraph structure to incorporate macro‑economic indicators could further enrich the model’s contextual awareness.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.