Knowledge Economy Studies

When Sustainability Meets Machine Learning: Reinforcement and Neural Evidence from an Emerging Market

Document Type : Original Article

Authors

Setareh Azadvar 1
Alireza Azarberahman 2

1 Department of accounting, Shandiz Institute of Higher Education, Mashhad, Iran E-mail: setareh.azadvar@gmail.com

2 Department of Accounting, Shandiz Institute of Higher Education, Mashhad, Iran Corresponding Author E-mail: a.azarberahman@shandiz.ac.ir

https://doi.org/10.22034/kes.2026.2077788.1087
Abstract
This study examined how firm-level environmental and social performance relates to stock price volatility in an emerging market characterized by limited transparency and weaker institutional frameworks. While prior research largely relied on linear models and focused on developed economies, this study adopted a dynamic, data-driven perspective to capture potentially nonlinear and time-dependent sustainability–risk patterns. Using a panel of non-financial firms listed on Tehran Stock Exchange (TSE) over the period 2011–2023, the firm-level environmental and social indicators were constructed based on a systematic analysis of sustainability disclosures. Empirical results from conventional linear regressions indicated weak and statistically insignificant average associations between sustainability performance and stock volatility. However, learning-based models, including reinforcement learning (RL) and LSTM neural networks, demonstrated superior ability to capture nonlinear and dynamic volatility patterns conditional on sustainability-related information. These findings suggested that the sustainability disclosures contain predictive information for volatility dynamics, even when linear risk-reduction effects are not evident. The study highlighted the importance of flexible modeling frameworks when assessing the financial implications of environmental and social performance in emerging markets.

Keywords

ESG performance
Stock volatility
Reinforcement learning (RL)
LSTM neural networks
Sustainable finance
Nonlinear dynamics
Subjects
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Knowledge Economy Studies
Volume 3, Issue 1
April 2026
Pages 47-71

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