Research Paper
Other
Mostafa Baratpour; Ghodratullah Emamvardi; Mahmoud Mahmoudzadeh; Parvaneh Salatin
Abstract
Identifying leading stocks is critical for investors, particularly in markets lacking comprehensive analytical tools. Effective stock selection necessitates an integrated approach that combines financial network analysis, performance evaluation, and predictive modeling. This study examines firm-level ...
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Identifying leading stocks is critical for investors, particularly in markets lacking comprehensive analytical tools. Effective stock selection necessitates an integrated approach that combines financial network analysis, performance evaluation, and predictive modeling. This study examines firm-level interconnections within the Tehran Stock Exchange, focusing on the implications of exchange rate shocks. A dual-phase analytical framework is applied: first, Minimum Spanning Tree network analysis identifies leading stocks and quantifies the effects of exchange rate fluctuations; second, VAR-GARCH models assess volatility dynamics of leading stocks, while the iterated cumulative sum of squares method detects structural breaks in market behavior. The dataset includes daily returns of 50 top-performing stocks and the free-market USD exchange rate across two periods: pre-shock (March 24, 2016–April 3, 2018) and post-shock (April 4, 2018–July 21, 2020). Pre-shock, Pars Khodro, Foulad, and Kegol dominated the market. Post-shock, export-driven sectors such as metals retained leadership due to competitive advantages, while import-dependent industries like automotive declined significantly. Later, stocks including Veghadir, Foulad, Sharak, and Tipico emerged as new leaders, reflecting structural realignments driven by currency volatility. The findings highlight the efficacy of network-driven methodologies in portfolio optimization and risk management, offering empirical clarity on sectoral dependencies and exchange rate sensitivities in emerging economies.
