A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm)
Purpose: One of the issues that significantly impact how people invest is the behavioural characteristics of investors. Given the importance of this issue, investors should be able to categorize investors into different classes and recommend investments appropriate to the personality type of the sam...
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| Format: | Article |
| Language: | fas |
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Ayandegan Institute of Higher Education, Tonekabon,
2021-08-01
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| Series: | مدیریت نوآوری و راهبردهای عملیاتی |
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| Online Access: | http://www.journal-imos.ir/article_133336_0f47f2760b170c6f056db42544ece2c0.pdf |
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| author | Marziyeh Nourahmadi Hojjatollah Sadeqi |
| author_facet | Marziyeh Nourahmadi Hojjatollah Sadeqi |
| author_sort | Marziyeh Nourahmadi |
| collection | DOAJ |
| description | Purpose: One of the issues that significantly impact how people invest is the behavioural characteristics of investors. Given the importance of this issue, investors should be able to categorize investors into different classes and recommend investments appropriate to the personality type of the same class for each class. One of the solutions that can be used for this purpose is clustering. Clustering is one of the unsupervised learning methods and has a descriptive nature. In this method, the data are allocated based on a similarity criterion so that the data in each cluster are most similar and the least comparable to the data in other clusters.Methodology: This study identifies a group of investors with similar ability and willingness to accept risk using K-means clustering and Affinity propagation clustering. We also show how to allocate assets effectively using investor characteristics and clustering techniques.Findings: Use silhouette coefficient to evaluate two clustering methods to select the best method for data clustering. The k-means coefficient was equal to 0.17, and the Affinity propagation clustering was equal to 0.097. Therefore, we choose the k-means method as the optimal clustering method. Using the K-means clustering method, we cluster investors based on financial, behavioural, and demographic characteristics, and according to the clustering results, we divide individuals into seven categories with low to high-risk acceptance.Originality/Value: All calculations in this study were performed by Python 3.8. Investment managers and stock advisors can use the results of this study. |
| format | Article |
| id | doaj-art-f1169835e9d346b79ee66ead2c506e45 |
| institution | Kabale University |
| issn | 2783-1345 2717-4581 |
| language | fas |
| publishDate | 2021-08-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | مدیریت نوآوری و راهبردهای عملیاتی |
| spelling | doaj-art-f1169835e9d346b79ee66ead2c506e452025-01-30T14:59:31ZfasAyandegan Institute of Higher Education, Tonekabon,مدیریت نوآوری و راهبردهای عملیاتی2783-13452717-45812021-08-012218019410.22105/imos.2021.289811.1111133336A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm)Marziyeh Nourahmadi0Hojjatollah Sadeqi1Department of Financial Engineering, Faculty of Economics, Management and Accounting, Yazd University, Yazd, IranDepartment of Finance and Accounting, School of Management and Economics, Yazd University, Yazd, Iran.Purpose: One of the issues that significantly impact how people invest is the behavioural characteristics of investors. Given the importance of this issue, investors should be able to categorize investors into different classes and recommend investments appropriate to the personality type of the same class for each class. One of the solutions that can be used for this purpose is clustering. Clustering is one of the unsupervised learning methods and has a descriptive nature. In this method, the data are allocated based on a similarity criterion so that the data in each cluster are most similar and the least comparable to the data in other clusters.Methodology: This study identifies a group of investors with similar ability and willingness to accept risk using K-means clustering and Affinity propagation clustering. We also show how to allocate assets effectively using investor characteristics and clustering techniques.Findings: Use silhouette coefficient to evaluate two clustering methods to select the best method for data clustering. The k-means coefficient was equal to 0.17, and the Affinity propagation clustering was equal to 0.097. Therefore, we choose the k-means method as the optimal clustering method. Using the K-means clustering method, we cluster investors based on financial, behavioural, and demographic characteristics, and according to the clustering results, we divide individuals into seven categories with low to high-risk acceptance.Originality/Value: All calculations in this study were performed by Python 3.8. Investment managers and stock advisors can use the results of this study.http://www.journal-imos.ir/article_133336_0f47f2760b170c6f056db42544ece2c0.pdfclusteringinvestment consultingrisk toleranceoptimizationinvestor behaviorrecommender system |
| spellingShingle | Marziyeh Nourahmadi Hojjatollah Sadeqi A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) مدیریت نوآوری و راهبردهای عملیاتی clustering investment consulting risk tolerance optimization investor behavior recommender system |
| title | A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) |
| title_full | A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) |
| title_fullStr | A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) |
| title_full_unstemmed | A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) |
| title_short | A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) |
| title_sort | clustering of investors behavior according to their financial behavioral and demographic characteristics an application of k means algorithm |
| topic | clustering investment consulting risk tolerance optimization investor behavior recommender system |
| url | http://www.journal-imos.ir/article_133336_0f47f2760b170c6f056db42544ece2c0.pdf |
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