Can Dinh Ngoc, Tam Phan Huy, Tu Ta Thi Cam, Tam Luong Thi My, Hien Nguyen Thi Thuy, Minh Ngo Hai
{"title":"基于机器学习的越南证券交易市场政治关联聚类研究","authors":"Can Dinh Ngoc, Tam Phan Huy, Tu Ta Thi Cam, Tam Luong Thi My, Hien Nguyen Thi Thuy, Minh Ngo Hai","doi":"10.1142/s1793993323500242","DOIUrl":null,"url":null,"abstract":"This paper aims to cluster politically affiliated groups using machine learning. The sample used in the study is enterprises listed on the stock exchanges of Ho Chi Minh City and Hanoi, research data during the period from 2015 to 2020. Data used in the study include state ownership ratio, the degree of political connection of business leaders and financial indicators in the listed financial statements of enterprises. The author’s study measures political connection by K-means algorithm and then compares the results of the K-means clustering with the traditional method of manual measurement of political connection including two values of 0 and 1, where 0 is no political affiliation and 1 is political affiliation. At the same time, the author runs three clusters to have in-depth insight. The authors conclude that machine learning clustering using the k-means model can replace the traditional method. Politically connected businesses listed on HOSE and HNX with political connections bring many benefits to businesses in investment activities, in accessing resources as well as capital; however, that businesses have a negative impact on business performance. The authors recommend that a moderate degree of political affiliation will help businesses achieve better performance.","PeriodicalId":44073,"journal":{"name":"Journal of International Commerce Economics and Policy","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Political Affiliate Clustering with Machine Learning in Vietnam Stock Exchange Market\",\"authors\":\"Can Dinh Ngoc, Tam Phan Huy, Tu Ta Thi Cam, Tam Luong Thi My, Hien Nguyen Thi Thuy, Minh Ngo Hai\",\"doi\":\"10.1142/s1793993323500242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to cluster politically affiliated groups using machine learning. The sample used in the study is enterprises listed on the stock exchanges of Ho Chi Minh City and Hanoi, research data during the period from 2015 to 2020. Data used in the study include state ownership ratio, the degree of political connection of business leaders and financial indicators in the listed financial statements of enterprises. The author’s study measures political connection by K-means algorithm and then compares the results of the K-means clustering with the traditional method of manual measurement of political connection including two values of 0 and 1, where 0 is no political affiliation and 1 is political affiliation. At the same time, the author runs three clusters to have in-depth insight. The authors conclude that machine learning clustering using the k-means model can replace the traditional method. Politically connected businesses listed on HOSE and HNX with political connections bring many benefits to businesses in investment activities, in accessing resources as well as capital; however, that businesses have a negative impact on business performance. The authors recommend that a moderate degree of political affiliation will help businesses achieve better performance.\",\"PeriodicalId\":44073,\"journal\":{\"name\":\"Journal of International Commerce Economics and Policy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of International Commerce Economics and Policy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s1793993323500242\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of International Commerce Economics and Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1793993323500242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
Political Affiliate Clustering with Machine Learning in Vietnam Stock Exchange Market
This paper aims to cluster politically affiliated groups using machine learning. The sample used in the study is enterprises listed on the stock exchanges of Ho Chi Minh City and Hanoi, research data during the period from 2015 to 2020. Data used in the study include state ownership ratio, the degree of political connection of business leaders and financial indicators in the listed financial statements of enterprises. The author’s study measures political connection by K-means algorithm and then compares the results of the K-means clustering with the traditional method of manual measurement of political connection including two values of 0 and 1, where 0 is no political affiliation and 1 is political affiliation. At the same time, the author runs three clusters to have in-depth insight. The authors conclude that machine learning clustering using the k-means model can replace the traditional method. Politically connected businesses listed on HOSE and HNX with political connections bring many benefits to businesses in investment activities, in accessing resources as well as capital; however, that businesses have a negative impact on business performance. The authors recommend that a moderate degree of political affiliation will help businesses achieve better performance.
期刊介绍:
Journal of International Commerce, Economics and Policy (JICEP) is a peer-reviewed journal that seeks to publish high-quality research papers that explore important dimensions of the global economic system (including trade, finance, investment and labor flows). JICEP is particularly interested in potentially influential research that is analytical or empirical but with heavy emphasis on international dimensions of economics, business and related public policy. Papers must aim to be thought-provoking and combine rigor with readability so as to be of interest to both researchers as well as policymakers. JICEP is not region-specific and especially welcomes research exploring the growing economic interdependence between countries and regions.