Research Article


DOI :10.26650/MED.1278850   IUP :10.26650/MED.1278850    Full Text (PDF)

Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method

Çiğdem ÖzarıEsin Nesrin Can

The main aim of this study is to cluster production companies in Borsa Istanbul based on financial ratios using the k-means clustering method and to determine the natural structure of these companies. The analysis focuses on 11 years of production companies based on 15 financial ratios and two financial indicators. The study determined the similarity among the financial performances of these companies using the k-means clustering analysis and evaluated the most appropriate cluster group and the most appropriate number of clusters that should be separated using the silhouette index and elbow method. The analysis was performed with different initial centers within the dataset for each k value by considering the choice of the initial center, which is a drawback of the k-means clustering method. In addition, because the number of clusters in which production companies should be grouped naturally is unknown, different k values were examined, with the most appropriate number of clusters to be separated from these values being determined using the silhouette index and elbow method. The results of the clustering analysis imply that splitting production companies into two clusters provides more accurate results.

JEL Classification : G17 , C38 , M00
DOI :10.26650/MED.1278850   IUP :10.26650/MED.1278850    Full Text (PDF)

Finansal Göstergeler Açısından Borsa İstanbul’da Faaliyet Gösteren Üretim Şirketlerinin Değerlendirilmesi: Silhouette İndeksine ve Elbow Yöntemine Göre K-Ortalamalar ile Kümelenmesi

Çiğdem ÖzarıEsin Nesrin Can

Bu çalışmanın ana amacı, k-ortalamalar kümeleme yöntemi kullanılmak suretiyle, Borsa İstanbul’da faaliyet gösteren üretim şirketlerini, finansal tablolarından hareketle hesaplanmış finansal göstergelerine göre gruplamak ve grup yapılarını değerlendirmektir. Çalışmada şirketler 11 yıllık finansal tablolardan yararlanılarak 15 finansal oran ve 2 gösterge ile gruplanmıştır. Şirketlerin finansal performans benzerliği k-ortalamalar kümeleme yöntemi ile değerlendirilirken, uygun küme grubu ve ayrılması gereken uygun küme sayısı Silhouette İndeks ve Elbow Yöntemi aracılığı ile değerlendirilmiştir. Çalışmada k-ortalamalar kümeleme yönteminin sorunsallarından biri olan başlangıç merkez seçimi göz önünde tutularak, her “k” değeri için veri seti içerisinden farklı başlangıç merkezleri itibarıyla analiz gerçekleştirilmiştir. Ayrıca üretim şirketlerinin doğal gruplanması gereken küme sayısı bilinmediği için farklı “k” değerleri incelenmiş ve bu değerlerden hareketle ayrışması uygun olan küme sayısı, Silhouette İndeks ve Elbow Yöntemi yardımıyla belirlenmiştir. Yapılan analizler sonucunda Borsa İstanbul’da faaliyet gösteren üretim şirketlerinin iki kümeye ayrılmasının daha fazla kümeye ayrılmasına göre daha uygun olduğu belirlenmiştir.

JEL Classification : G17 , C38 , M00

EXTENDED ABSTRACT


The main purpose of this study is to cluster production companies operating in Borsa Istanbul based on the most important financial indicators using the k-means clustering method and to find the natural and accurate structure of these companies. Some studies determine the performance of companies and even universities in different sectors by using financial ratios and indicators alongside the k-means clustering method in order to determine their place in the sector.

Unlike other studies, this study aims to determine the most appropriate grouping structure by running an algorithm for different initial centers. In other words, it aims to eliminate one of the drawbacks of the k-means clustering analysis, which is the selection of initial centers, by considering different initial centers from inside the dataset. In addition, the natural structure (i.e., grouping structure) of production companies is not well defined, which means that the accurate value of k is not determined. As such, the k-means clustering algorithm is applied for different values of k (e.g., 2, 3, 4, and 5) in order to determine which cluster group is more accurate using the silhouette index and elbow method. Determining the best valueof k provides information about how many clusters similar companies will be grouped into in terms of financial performance similarities of production companies.

Due to one of the study’s goals being to understand and determine the natural structure of production companies, having a long enough study period is desired. The study covers 11 years of data. Although Borsa Istanbul had142 different companies operating during this period of analysis, the 42 companies of these that did not operate throughout the entire 11-year period were not included in the study. To discover the similarities between these companies concerning their financial performance, this study needed to examine their financial ratios. Due to variation of the number of financial ratios in the literature, the researcher in this study has decided to select the financial ratios to be used in the analysis based on scientific reasons. This study has examined the literature and resultantly uses 15 financial ratios and two indicators. Four of these show the ability of short-term assets to meet the short-term liabilities of companies, five show the profitability of companies, and three show the ability of a company to effectively used their liabilities. One instance that should not be overlooked in this situation is the possibility of obtaining varied outcomes by grouping these producing organizations with various financial ratios.

During the study period, the EREGL and TUPRS listings remained in the same group and showed similarities in terms of financial performance. In Year 2, the ARCLK listing was added to this group and remained in the same group as EREGL and TUPRS for all subsequent years. In Year 3, AEFES was added to this group. Similar to ARCLK, AEFES continued to participate in the same group for all subsequent years after joining the group. In Year 4, the TOASA and VESTL listings were included in this group. In addition, the FROTO listing has been determined to be in the same group as these companies only for Years 6 and 10. ARCLK, ERGL, TUPRS, and AEFES were discovered to be part of the same cluster group for 8 years.

As a result of the findings obtained from the study, the financial performance of production companies can be said to have been preserved over time. However, the fact that 40 companies were not included in the study due to data losses may indicate that the resulting cluster groups do not fully reflect reality. Future studies may obtain more effective results by comparing

the results that were obtained using different clustering methods. In addition, which of the cluster groups is more suitablecan be determined using different statistical methods.


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Özarı, Ç., & Can, E.N. (2023). Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method. Journal of Accounting Institute, 0(69), 1-19. https://doi.org/10.26650/MED.1278850


AMA

Özarı Ç, Can E N. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method. Journal of Accounting Institute. 2023;0(69):1-19. https://doi.org/10.26650/MED.1278850


ABNT

Özarı, Ç.; Can, E.N. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method. Journal of Accounting Institute, [Publisher Location], v. 0, n. 69, p. 1-19, 2023.


Chicago: Author-Date Style

Özarı, Çiğdem, and Esin Nesrin Can. 2023. “Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method.” Journal of Accounting Institute 0, no. 69: 1-19. https://doi.org/10.26650/MED.1278850


Chicago: Humanities Style

Özarı, Çiğdem, and Esin Nesrin Can. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method.” Journal of Accounting Institute 0, no. 69 (Jul. 2024): 1-19. https://doi.org/10.26650/MED.1278850


Harvard: Australian Style

Özarı, Ç & Can, EN 2023, 'Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method', Journal of Accounting Institute, vol. 0, no. 69, pp. 1-19, viewed 17 Jul. 2024, https://doi.org/10.26650/MED.1278850


Harvard: Author-Date Style

Özarı, Ç. and Can, E.N. (2023) ‘Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method’, Journal of Accounting Institute, 0(69), pp. 1-19. https://doi.org/10.26650/MED.1278850 (17 Jul. 2024).


MLA

Özarı, Çiğdem, and Esin Nesrin Can. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method.” Journal of Accounting Institute, vol. 0, no. 69, 2023, pp. 1-19. [Database Container], https://doi.org/10.26650/MED.1278850


Vancouver

Özarı Ç, Can EN. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method. Journal of Accounting Institute [Internet]. 17 Jul. 2024 [cited 17 Jul. 2024];0(69):1-19. Available from: https://doi.org/10.26650/MED.1278850 doi: 10.26650/MED.1278850


ISNAD

Özarı, Çiğdem - Can, EsinNesrin. Evaluating Production Companies on Borsa Istanbul In Terms of Financial Indicators: Clustering Companies Using K-Means with the Silhouette Index and Elbow Method”. Journal of Accounting Institute 0/69 (Jul. 2024): 1-19. https://doi.org/10.26650/MED.1278850



TIMELINE


Submitted07.04.2023
Accepted13.06.2023
Published Online29.08.2023

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