Political affiliate clustering with machine learning in Vietnam stock exchange
Authors:   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam, Phan Huy Tam ,   Vietnam National University, Ho Chi Minh City, Viet Nam, Dinh Can Ngoc ,   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam, Tu Ta Thi Cam ,   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam Google Scholar Pubmed, Luong Tam Thi My ,   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam, Nguyen Hien Thi Thuy ,   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam, Ngo Minh Hai ,   Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam
Published: October 2023

This paper focuses on employing machine learning techniques to cluster politically affiliated groups. The study utilizes a sample of enterprises listed on the stock exchanges of Ho Chi Minh City and Hanoi, analyzing research data spanning the period from 2015 to 2020. The dataset comprises various variables, including the state ownership ratio, the level of political connections among business leaders, and financial indicators derived from the listed financial statements of these enterprises. In this study, the author measures political connections using the K-means algorithm and compares the results of the K-means clustering with the traditional manual method of assessing political connections, which involves assigning values of either 0 or 1. A value of 0 indicates no political affiliation, while a value of 1 represents political affiliation. Additionally, the author conducts three cluster analyses to gain deeper insights into the data. The findings of this research conclude that machine learning clustering using the K-means model holds promise in effectively identifying firms with political connections compared to the traditional manual approach. It is observed that politically connected businesses listed on the stock exchanges of Ho Chi Minh City and Hanoi reap numerous benefits in terms of investment activities, resource accessibility, and capital. However, the study also acknowledges the negative impact that such political affiliations can have on firm performance. Based on the results, the authors recommend maintaining a moderate degree of political affiliation, as this approach may assist firms in achieving better overall performance. By striking the right balance, companies can leverage the advantages of political connections while minimizing any adverse effects on their operations. In conclusion, this study highlights the potential of machine learning clustering, specifically utilizing the K-means algorithm, to effectively identify politically affiliated groups among enterprises listed on the stock exchanges of Ho Chi Minh City and Hanoi. The research emphasizes the benefits of political connections for businesses but also underscores the need for careful management to optimize performance outcomes.