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Multi-Criteria Decision-Making Techniques for Ranking NSE Sectorial Indices: A Comparative Analysis with Feature Weighting

Priyanka Kanwar and Dr. S. M. Ghosh

Data analysis plays a crucial role in financial and managerial applications, impacting a range of IT and business operations. This research focuses on the use of Multi-Criteria Decision Making (MCDM) techniques to rank Sectorial Indices of the National Stock Exchange (NSE) based on various criteria. Traditional approaches often involve high computational complexity, prompting the exploration of more efficient alternatives. In this study, we apply MCDM techniques, including Simple Additive Weighting Method (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment (COPRAS), Additive Ratio Assessment (ARAS), and Evaluation based on Distance from Average Solution (EDAS), to rank NSE Sectorial Indices. To enhance the performance of these techniques, feature weighting is incorporated, demonstrating superior accuracy and scalability compared to state-of-the-art approaches.