Informational Complexity and Market Structure: A Nonlinear Panel Analysis of Stock Returns Using Entropy and Kullback–Leibler Divergence - Zia Ul Rehman Zafar; Ahad Sultan; Kashif Ali Abdul Wahid Alias; Ahmad Hassan (Universitas Syiah Kuala, 2026)
This study examines how informational complexity and market structure jointly influence stock returns in an emerging market context. Using panel data from 40 publicly listed firms across eight sectors during 2005–2026, the study measures informational complexity through Shannon entropy and Kullback–Leibler (KL) divergence, while market concentration is captured using the Herfindahl–Hirschman Index (HHI). Fixed effects panel regression models are employed to evaluate the direct and interaction effects of informational and structural variables, with additional macroeconomic controls including inflation, interest rates, and exchange rates. The results show that informational complexity is positively associated with stock returns in baseline models, indicating that uncertainty and distributional irregularities contain economically relevant information. However, the significance of these effects declines after incorporating macroeconomic controls and time effects, suggesting that informational dynamics are partly shaped by broader economic conditions. The interaction between entropy and market concentration remains positive and robust, demonstrating that the effect of informational complexity depends on market structure and becomes stronger in more concentrated sectors. Exchange rates also exhibit a significant negative relationship with stock returns, highlighting the importance of macroeconomic stability in emerging markets. The study is limited by its focus on a single emerging market and the use of annual aggregated data, which may not fully capture higher-frequency dynamics. Nevertheless, the findings provide practical implications for investors and policymakers by emphasizing the conditional nature of informational effects in financial markets. The study’s originality lies in integrating information-theoretic measures and market structure within a unified empirical framework to explain nonlinear stock return dynamics.