Corporate Sustainability Disclosure in the Tehran Stock Exchange Using Grey Theory and Markov Models

Objective: Corporate sustainability disclosure (CSD) has become a key mechanism for improving transparency beyond traditional financial reporting by providing stakeholders with a more comprehensive picture of firms’ economic, governance, social, and environmental performance. Such disclosure was expected to reduce information asymmetry, strengthen investor confidence, and enhance decision quality in capital markets. Given the growing relevance of sustainability reporting and the limited availability of direct sustainability disclosure metrics in Iran, this study aimed to construct a Corporate Sustainability Disclosure Index (CSDI) for firms listed on the Tehran Stock Exchange (TSE) and to forecast its time trend. The study further aimed to enhance forecasting accuracy by combining Grey forecasting with Markov chain correction, thereby offering an approach capable of capturing both trend dynamics and state-to-state transitions in disclosure levels.
Methods: A composite CSDI was developed because no single direct indicator of sustainability disclosure was available in Iranian corporate datasets. The index was constructed by integrating several dimensions that collectively reflected sustainability-related disclosure and performance: (1) economic/financial variables, (2) governance-related indicators, (3) reporting quality measures, and (4) environmental proxies. After data collection and index calculation, the time path of CSDI was forecast using the Grey Model GM(1,1), which was suitable for systems with limited or incomplete information. To improve the baseline Grey forecasts, a Markov chain framework was then employed. Specifically, firms’ disclosure levels were classified into discrete states (levels), a transition probability matrix was estimated, and Grey forecasts were adjusted using Markov transition probabilities to account for the likelihood of moving between disclosure states over time.
Results: The results indicated that the constructed CSDI exhibited an overall increasing trend across the examined period, suggesting gradual improvement in sustainability-related disclosure among TSE-listed firms. The Grey GM(1,1) model captured the upward temporal trend, while the Markov correction revealed that disclosure levels did not evolve deterministically; rather, they shifted between adjacent (and in some cases non-adjacent) states with identifiable transition dynamics. The transition probability structure suggested that firms tended to maintain their disclosure category with a measurable persistence, yet meaningful upward mobility between disclosure states occurred, consistent with a progressive disclosure trajectory. The combined Grey–Markov approach therefore provided both a smooth trend forecast and a probabilistic depiction of disclosure-level mobility.
Conclusion: The study concluded that corporate sustainability disclosure among firms listed on the TSE was improving and that disclosure evolution could be better understood through a hybrid modelling approach. Combining GM(1,1) with Markov chains supported more realistic forecasting by incorporating uncertainty and state transition behavior. This methodological integration was useful for monitoring disclosure development, identifying potential risks associated with stagnation or downward transitions, and informing managerial or regulatory strategies aimed at strengthening sustainability disclosure practices.
Innovation: This study contributed by (i) operationalizing a composite CSDI tailored to the Iranian context in the absence of a direct disclosure metric, and (ii) applying a Grey GM(1,1) model enhanced with Markov transition correction to jointly model disclosure trends and probabilistic shifts between disclosure levels in the TSE setting.