ESG Quantitative Scoring System and ML-Driven Investment Research

Sun, 01 Jan 2023 00:00:00 +0000

Problem

Existing ESG ratings from major providers suffer from low cross-provider agreement, limited transparency, and methodological subjectivity. A more systematic, data-driven approach is needed that integrates structured financial data with unstructured news and disclosure content, updates at high frequency, and supports both index construction and active strategy development.

Method

AQUMON developed a four-layer evaluation framework — Pillar, Category, Topic, and Metric — covering environmental, social, and governance dimensions for a universe of listed companies. The system integrates structured financial and regulatory data with unstructured news, corporate disclosures, and sentiment signals using multimodal ML pipelines. Dynamic weight updating and cross-industry comparability were explicit design requirements. The scoring methodology was designed to satisfy IFRS S1 and IFRS S2 disclosure frameworks and supports both institutional investment mandates and corporate sustainability reporting.

Outcome

The system provides daily ESG score updates covering 3,000+ listed companies. Backtested long-only strategies combining ESG scores with fundamental signals demonstrated improved risk-adjusted performance relative to market benchmarks. The framework has been adopted by the HKSAR Government and multiple listed companies for sustainability reporting and investment mandate compliance.

Quantitative Research |

ESG Quantitative Scoring System and ML-Driven Investment Research

Problem

Method

Outcome