Design of a Network-Oriented Framework for Detecting Key Stocks in the Tehran Stock Exchange via Time Series Analysis and Nonlinear Techniques

This study develops a network-based analytical framework for identifying key stocks and portfolio strategies in the Tehran Stock Exchange. The objective is to capture nonlinear and directional dependencies among stocks that traditional linear methods often overlook. Daily prices of ۲۰ highly traded stocks from March ۲۰۱۶ to March ۲۰۲۴ were analyzed. Nonlinear relationships were quantified using Dynamic Time Warping (DTW) for dynamic similarity and Transfer Entropy (TE) for information flow, combined into a weighted directed network. Key stocks were identified through multiple centrality measures and the Louvain community detection algorithm. Two portfolio strategies—equal-weighted and centrality-weighted—were evaluated using a rolling backtest. Results show that the equal-weighted portfolio achieved higher risk-adjusted returns, while the network-based approach provided structural insights into hidden market dependencies.

کلیدواژه ها:

Network Analysis ، Transfer Entropy ، Dynamic Time Warping ، Tehran Stock Exchange ، Centrality Measures