arXiv:2502.15411v4 Announce Type: replace Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Business Reporting Language (iXBRL) is mandated for public financial filings. Yet, its complex, fine-grained taxonomy limits the cross-company transferability of tagged Key Performance Indicators (KPIs). To address this, we introduce the Hierarchical Financial Key Performance Indicator (HiFi-KPI) dataset, a large-scale corpus of 1.65M paragraphs and 198k unique, hierarchically organized labels linked to iXBRL
The proliferation of AI in financial analysis and the mandate for iXBRL in public filings have created a critical demand for structured, machine-readable KPI data.
This dataset significantly enhances AI's ability to extract and analyze financial performance indicators across companies, improving investment decisions and regulatory oversight.
The HiFi-KPI dataset offers a standardized, hierarchical approach to KPI extraction, reducing the complexity of iXBRL and enabling more reliable cross-company financial comparisons.
- · Quantitative hedge funds
- · Financial AI/ML developers
- · Investment analysts
- · Regulatory bodies
- · Manual financial data extractors
- · Companies with opaque reporting
- · Static financial data providers
Improved efficiency and accuracy in financial statement analysis through AI-driven KPI extraction.
Increased transparency and comparability of financial performance across different companies, potentially leading to more efficient capital allocation.
The development of new financial products and services predicated on granular, real-time access to standardized KPI data, possibly accelerating market shifts.
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Read at arXiv cs.CL