EKOIST Journal of Econometrics and Statistics

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DOI :10.26650/ekoist.2024.41.1487849   IUP :10.26650/ekoist.2024.41.1487849    Full Text (PDF)

Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis

Damla Yalçıner Çal, Meltem Karaatlı

In this study, the co-movements of the stocks of the companies in the BIST 100 index are analysed by Cluster Analysis and Association Rules Analysis. For the clustering analysis, yield, trading volume, price volatility, market value, beta, market value/book value, equity/paid capital, and market value/net sales (revenue) variables are used; for the association rules analysis, the closing price is taken as a variable. The period 06.12.2012 to - 30.12.2022 was analysed in the study. Cluster Analysis was conducted for this period, and the associations of all stocks and stocks for each cluster were also analysed. The CLARA algorithm was used for the cluster analysis, and the FP-Growth Algorithm was used for the association rules analysis. The R programming language was preferred for the cluster analysis, and the WEKA programme was preferred for the association rules analysis. Because of the study, Cluster Analysis was used to determine the interconnectedness of stocks and Association Rules Analysis was used to determine which stocks move together. This will help both individual and institutional portfolio managers in determining which stocks to focus on in the portfolio diversification process. In addition, identifying stocks that move in tandem with each other during upward or downward price changes in stock markets, which have a very dynamic structure, will provide investors with the opportunity to share in potential profits. The possibility that the upward or downward movement in the price of a stock whose co-movement is detected may be accompanied by other stocks constitutes potential profits. According to the main findings of the research, there is a very intense co-movement among the companies operating in the banking sector. In addition, there is a commonality among pharmaceutical, white goods, iron and steel, retail, energy, petrochemical and manufacturing companies operating in the same sector. There is also an association between family group companies and companies operating in real estate investment trusts. 

Keywords: Data Mining, CLARA Clustering Analysis, FP-Growth Association Rules Analysis, Portfolio Management, Stock Selection
DOI :10.26650/ekoist.2024.41.1487849   IUP :10.26650/ekoist.2024.41.1487849    Full Text (PDF)

Kümeleme ve Birliktelik Kuralları Analizi İle Borsa İstanbul 100 Endeksinde Yer Alan Hisse Senetlerinin İncelenmesi

Damla Yalçıner Çal, Meltem Karaatlı

Bu çalışmada BIST 100 endeks içinde yer alan şirketlere ait hisse senetlerinin birlikte hareketleri Kümeleme Analizi ve Birliktelik Kuralları Analizi ile incelenmiştir. Kümeleme Analizi için getiri, işlem hacmi (volume), fiyat oynaklığı, piyasa değeri, beta, piyasa değeri/defter değeri, özkaynaklar/ödenmiş sermaye, piyasa değeri/net satışlar (hasılat); birliktelik kuralları analizinde kapanış fiyatı değişken olarak kullanılmıştır. Çalışmada 06.12.2012-30.12.2022 dönemi incelemeye alınmıştır. Bu dönem için Kümeleme Analizi yapılmış daha sonra tüm hisse senetlerinin ve Kümeleme Analizi ile oluşan grupların kendi içerisinde birlikteliklerine bakılmıştır. Kümeleme Analizi için CLARA Algoritması kullanılmış, Birliktelik Kuralları Analizi için de FP-Growth Algoritmasından yararlanılmıştır. Kümeleme Analizi için R programlama dili, Birliktelik Kuralları Analizi içinde WEKA programı tercih edilmiştir. Çalışmanın sonucunda, Kümeleme Analizi ile hisse senetlerinin birbirleriyle olan bağlantıları, ardından Birliktelik Kuralları Analizi ile hisse senetlerinin hangilerinin birlikte hareket ettiği görülmüştür. Bu durum hem bireysel hem de kurumsal portföy yöneticilerine, portföy çeşitlendirme sürecinde hangi hisse senetlerine yönelebilecekleri konusunda yardımcı olacaktır. Ayrıca çok dinamik bir yapıya sahip olan hisse senedi piyasalarında yukarı veya aşağı yönlü fiyat değişimlerinde birbirine eşlik eden hisse senetlerinin belirlenmesi, yatırımcılara potansiyel kârlardan pay alma imkânı sağlayacaktır. Birlikte hareketi tespit edilen hisse senetlerinden bir tanesinin fiyatında görülen yukarı veya aşağı yönlü harekete diğer hisselerin de eşlik edebileceği olasılığı potansiyel kârı oluşturmaktadır. Araştırmadan elde edilen temel bulgulara göre bankacılık alanında faaliyet gösteren şirketler arasında çok yoğun bir birliktelik tespit edilmiştir. Ayrıca sektörel anlamda aynı sektör içerisinde faaliyet gösteren ilaç, beyaz eşya, demir çelik, perakende, enerji, petrokimya ve üretim şirketleri arasında birliktelik görülmektedir. Aile grup şirketleri ve gayrimenkul yatırım ortaklığı içerisinde yer alan şirketler arasında da birliktelik olduğu belirlenmiştir. 

Keywords: Veri Madenciliği, CLARA Kümeleme Algoritması, FP-Growth Birliktelik Kuralları Analizi, Portföy Yönetimi, Hisse Seçimi

EXTENDED ABSTRACT


In this study, the joint movements of the stocks of the companies included in the BIST 100 index were examined. Clustering Analysis and Association Rules Analysis were chosen as the method in the study. For the cluster analysis, return, volume, price volatility, market value, beta, market value/book value, equity/paid capital, market value/net sales (revenue) variables; In the association rules analysis, the closing price is taken as a variable and covers the period 06.12.2012 to -30.12.2022. The co-movements of stocks in the specified period were examined. In addition, a cluster analysis was conducted for this period and the associations of stocks for each cluster were examined. Here, information is shared about investors making profits by investing in stocks that have been identified to move together. The CLARA Algorithm was applied for the Clustering Analysis and the FP-Growth Algorithm was applied for the Association Rules Analysis.

First, the clusterability of the dataset was tested by using Hopkins Statistics in the R Programme. After determining that the dataset was musterable, the number of clusters was determined using the Gap Statistics. The number of clusters was determined as 9, and the CLARA Clustering Algorithm was applied. As a result of the CLARA Clustering Algorithm: In the 1st Cluster, AEFES, AKSEN, ALBRK, BAGFS, CCOLA, DOHOL, GLYHO, GSDHO, IPEKE, ISGYO, KOZAA, NTHOL, SKBNK, SNGYO, TAVHL, TKFEN, TRGYO, TSKB, TTRAK, TURSG, and ULKER stocks are listed together. In this cluster, there are stocks of companies of different sizes and from different sectors... In Cluster 2, AGHOL, ALGYO, BRYAT, EGEEN, and KARTN stocks are listed together. Stocks like BRYAT, EGEEN, KARTN and AGHOL that in general have a high potential for free capital increase are in this group. In Cluster 3, AKBNK, GARAN, HALKB, ISCTR, VAKBN, and YKBNK stocks are listed together. It can be said that this cluster differs from other clusters because there are only banks in it. In Cluster 4, AKFGY, AKSA, ALARK, ALKIM, BUCIM, CEMTS, CIMSA, DEVA, DOAS, ECILC, ERBOS, GUBRF, ISFIN, KARSN, KORDS, OTKAR, SELEC, TMSN, and YATAS stocks are listed together. It has been determined that this cluster includes organisations related to the pharmaceutical (DEVA, ECILC, SELEC), cement (BUCIM, ÇIMSA) and automotive (DOAS, KARSN, OTKAR, TMSN) sectors. In Cluster 5, stocks of ARCLK, BIMAS, ENKAI, FROTO, KCHOL, SAHOL, TCELL, TOASO, and TTKOM are listed together. The BIST 30 index includes important and large companies. There are also large holdings (KCHOL, SAHOL) in this cluster. In Cluster 6, ASELS, EKGYO, EREGL, KOZAL, KRDMD, PETKM, SISE, THYAO, and TUPRS stocks are listed together. Generally, the largest production enterprises of the BIST 30 index (except THYAO) are seen in this cluster. In Cluster 7, BERA, HEKTS, ISMEN, JANTS, LOGO, PRKAB, SASA, TUKAS, VESBE, and VESTL stocks are listed together. The SASA, HEKTS, and VESTL groups lead in this cluster. In Cluster 8, the GOZDE stock stands out alone. In Cluster 9, the MGROS, NUGYO, and OYAKC stocks are listed together. Here, three dissimilar companies have come together: a retail company, an investment trust and a cement company. However, the stocks of these companies are in similar clusters according to the determined variables. 

Then, by the WEKA programme, the joint movement of all stocks was first determined by FP-Growth Association Rules Analysis. A confidence level of 0.90 was considered for all stocks, and as a result of the analysis, no association rule other than of the bank was found in the first 54 of 74 rules.

The associations within each cluster identified by CLARA Clustering Analysis were further determined using FP-Growth Association Rules Analysis. According to this; 

• In Cluster 1, 1270 rules were found at the 0.90 confidence level.

• In Cluster 2, 5 rules were found at the 0.80 confidence level.

• In Cluster 3, 68 rules were found at the 0.90 confidence level.

• In Cluster 4, 126 association rules were found at a confidence level of 0.90.

• In Cluster 5, 14 rules were found at the 0.90 confidence level.

• In cluster 6, 20 association rules were found at a confidence level of 0.90.

• In Cluster 7, 24 association rules were found at a confidence level of 0.90.

• In Cluster 8, no rule has been formed.

• In cluster 9, 2 association rules were found at a confidence level of 70.

Because of the study; With Cluster Analysis, the connections between stocks were seen, and then with Association Rules Analysis, it was seen which stocks moved together. This will help both individual and corporate portfolio managers in determining which stocks they can focus on in the portfolio diversification process. According to the basic findings of the research, a very intense unity was detected among the companies operating in the banking field. In addition, sectoral unity wasis observed between pharmaceutical, household appliances, iron and steel, retail, energy, petrochemical and production companies operating in the same sector. It has also been determined that there is unity between family group companies and companies within the real estate investment trust. 


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APA

Yalçıner Çal, D., & Karaatlı, M. (2024). Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis. EKOIST Journal of Econometrics and Statistics, 0(41), 34-53. https://doi.org/10.26650/ekoist.2024.41.1487849


AMA

Yalçıner Çal D, Karaatlı M. Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis. EKOIST Journal of Econometrics and Statistics. 2024;0(41):34-53. https://doi.org/10.26650/ekoist.2024.41.1487849


ABNT

Yalçıner Çal, D.; Karaatlı, M. Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis. EKOIST Journal of Econometrics and Statistics, [Publisher Location], v. 0, n. 41, p. 34-53, 2024.


Chicago: Author-Date Style

Yalçıner Çal, Damla, and Meltem Karaatlı. 2024. “Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis.” EKOIST Journal of Econometrics and Statistics 0, no. 41: 34-53. https://doi.org/10.26650/ekoist.2024.41.1487849


Chicago: Humanities Style

Yalçıner Çal, Damla, and Meltem Karaatlı. “Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis.” EKOIST Journal of Econometrics and Statistics 0, no. 41 (Mar. 2025): 34-53. https://doi.org/10.26650/ekoist.2024.41.1487849


Harvard: Australian Style

Yalçıner Çal, D & Karaatlı, M 2024, 'Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis', EKOIST Journal of Econometrics and Statistics, vol. 0, no. 41, pp. 34-53, viewed 10 Mar. 2025, https://doi.org/10.26650/ekoist.2024.41.1487849


Harvard: Author-Date Style

Yalçıner Çal, D. and Karaatlı, M. (2024) ‘Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis’, EKOIST Journal of Econometrics and Statistics, 0(41), pp. 34-53. https://doi.org/10.26650/ekoist.2024.41.1487849 (10 Mar. 2025).


MLA

Yalçıner Çal, Damla, and Meltem Karaatlı. “Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis.” EKOIST Journal of Econometrics and Statistics, vol. 0, no. 41, 2024, pp. 34-53. [Database Container], https://doi.org/10.26650/ekoist.2024.41.1487849


Vancouver

Yalçıner Çal D, Karaatlı M. Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis. EKOIST Journal of Econometrics and Statistics [Internet]. 10 Mar. 2025 [cited 10 Mar. 2025];0(41):34-53. Available from: https://doi.org/10.26650/ekoist.2024.41.1487849 doi: 10.26650/ekoist.2024.41.1487849


ISNAD

Yalçıner Çal, Damla - Karaatlı, Meltem. “Examination of Stocks in the Istanbul Stock Exchange 100 Index With Clustering and Association Rules Analysis”. EKOIST Journal of Econometrics and Statistics 0/41 (Mar. 2025): 34-53. https://doi.org/10.26650/ekoist.2024.41.1487849



TIMELINE


Submitted21.05.2024
Accepted05.10.2024
Published Online26.12.2024

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