Developing a hybrid model for comparative analysis of financial data clustering algorithms
Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algo...
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| Format: | Article |
| Language: | fas |
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Ayandegan Institute of Higher Education, Tonekabon,
2023-09-01
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| Series: | تصمیم گیری و تحقیق در عملیات |
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| Online Access: | https://www.journal-dmor.ir/article_145108_1c40bf9c90cdae35d219eaa1c7ede3d7.pdf |
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| author | Mojtaba Movahedi Mahdi Homayounfar Mehdi Fadaei Mansour Soufi |
| author_facet | Mojtaba Movahedi Mahdi Homayounfar Mehdi Fadaei Mansour Soufi |
| author_sort | Mojtaba Movahedi |
| collection | DOAJ |
| description | Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.Methodology: This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.Findings: After clustering the surveyed companies by five clustering algorithms, namely K-means, EM, COBWEB, density-based algorithm and ward method, seven indicators RS, DB, Dun, SD, Purity, Entropy and Time were used to evaluate the algorithms. Finally, the total performance of the algorithms was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, K-means has a better performance in clustering based on the financial data sets.Originality/Value: Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio. |
| format | Article |
| id | doaj-art-7aa6ddbbe1ae47469112aa8e5d73dc37 |
| institution | Kabale University |
| issn | 2538-5097 2676-6159 |
| language | fas |
| publishDate | 2023-09-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | تصمیم گیری و تحقیق در عملیات |
| spelling | doaj-art-7aa6ddbbe1ae47469112aa8e5d73dc372025-01-30T15:03:21ZfasAyandegan Institute of Higher Education, Tonekabon,تصمیم گیری و تحقیق در عملیات2538-50972676-61592023-09-018250752610.22105/dmor.2022.296982.1453145108Developing a hybrid model for comparative analysis of financial data clustering algorithmsMojtaba Movahedi0Mahdi Homayounfar1Mehdi Fadaei2Mansour Soufi3Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.Methodology: This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.Findings: After clustering the surveyed companies by five clustering algorithms, namely K-means, EM, COBWEB, density-based algorithm and ward method, seven indicators RS, DB, Dun, SD, Purity, Entropy and Time were used to evaluate the algorithms. Finally, the total performance of the algorithms was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, K-means has a better performance in clustering based on the financial data sets.Originality/Value: Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio.https://www.journal-dmor.ir/article_145108_1c40bf9c90cdae35d219eaa1c7ede3d7.pdfclusteringmulti-criteria decision makingfinancial performance evaluation |
| spellingShingle | Mojtaba Movahedi Mahdi Homayounfar Mehdi Fadaei Mansour Soufi Developing a hybrid model for comparative analysis of financial data clustering algorithms تصمیم گیری و تحقیق در عملیات clustering multi-criteria decision making financial performance evaluation |
| title | Developing a hybrid model for comparative analysis of financial data clustering algorithms |
| title_full | Developing a hybrid model for comparative analysis of financial data clustering algorithms |
| title_fullStr | Developing a hybrid model for comparative analysis of financial data clustering algorithms |
| title_full_unstemmed | Developing a hybrid model for comparative analysis of financial data clustering algorithms |
| title_short | Developing a hybrid model for comparative analysis of financial data clustering algorithms |
| title_sort | developing a hybrid model for comparative analysis of financial data clustering algorithms |
| topic | clustering multi-criteria decision making financial performance evaluation |
| url | https://www.journal-dmor.ir/article_145108_1c40bf9c90cdae35d219eaa1c7ede3d7.pdf |
| work_keys_str_mv | AT mojtabamovahedi developingahybridmodelforcomparativeanalysisoffinancialdataclusteringalgorithms AT mahdihomayounfar developingahybridmodelforcomparativeanalysisoffinancialdataclusteringalgorithms AT mehdifadaei developingahybridmodelforcomparativeanalysisoffinancialdataclusteringalgorithms AT mansoursoufi developingahybridmodelforcomparativeanalysisoffinancialdataclusteringalgorithms |