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Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm

Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm

Kirti Aggarwal, Anuja Arora
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 21
ISSN: 1938-0232|EISSN: 1938-0240|EISBN13: 9781683180500|DOI: 10.4018/IJITPM.313421
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MLA

Aggarwal, Kirti, and Anuja Arora. "Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm." IJITPM vol.13, no.3 2022: pp.1-21. http://doi.org/10.4018/IJITPM.313421

APA

Aggarwal, K. & Arora, A. (2022). Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm. International Journal of Information Technology Project Management (IJITPM), 13(3), 1-21. http://doi.org/10.4018/IJITPM.313421

Chicago

Aggarwal, Kirti, and Anuja Arora. "Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm," International Journal of Information Technology Project Management (IJITPM) 13, no.3: 1-21. http://doi.org/10.4018/IJITPM.313421

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Abstract

A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in comparison to standard network community detection algorithms – greedy and label propagation.

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