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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| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
| Published: |
Ayandegan Institute of Higher Education, Tonekabon,
2021-08-01
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| Series: | مدیریت نوآوری و راهبردهای عملیاتی |
| Subjects: | |
| Online Access: | http://www.journal-imos.ir/article_133336_0f47f2760b170c6f056db42544ece2c0.pdf |
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| Summary: | 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. |
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| ISSN: | 2783-1345 2717-4581 |