Information-Theoretic ESG Index Direction Forecasting: A Complexity-Aware Framework.

ABSTRACT: Sustainable finance exhibits non-linear dynamics, regime shifts, and distributional drift that challenge conventional forecasting, particularly in volatile emerging markets. Conventional models, which often overlook this structural complexity, can struggle to produce stable or reliable probabilistic forecasts. To address this challenge, this study introduces a complexity-aware forecasting framework that operationalizes information-theoretic meta features, Shannon entropy (SE), permutation entropy (PE) and Kullback-Leibler (KL) divergence to make Environmental, Social, and Governance (ESG) index forecasting more stable, probabilistically accurate, and operationally reliable. Applied in an emerging-market setting using Türkiye's ESG index as a natural stress test, the framework was benchmark

SUBMITTER: Ozturk KN 

PROVIDER: S-EPMC12651845 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature