QD Research Engine

QD Research Engine is the editorial AI system powering Quant Decoded. It was designed by James Park to systematically analyse peer-reviewed academic papers, institutional white papers, and factor research from sources including AQR Capital Management, Fama-French, the CFA Institute, and the Journal of Finance — and translate them into structured, accessible analysis for independent investors.

Every article produced by the QD Research Engine is reviewed against its source material for accuracy before publication. The engine does not generate opinion or speculation — it synthesises, structures, and contextualises published research.

How It Works

The QD Research Engine operates as an automated research pipeline. It draws from peer-reviewed journals in financial economics, publicly available factor return datasets, and institutional research published by leading systematic investment managers. For each article, the engine identifies the primary academic source, cross-references findings against related studies across different time periods and geographies, and structures the synthesis into a consistent, readable format.

The automated pipeline handles source identification, cross-reference analysis, structured output generation, and translation into seven languages. The human editorial layer reviews every article for accuracy against the cited sources, validates that claims are properly attributed, and approves publication. No article goes live without this review step.

For questions about our editorial methodology, see the About page.