Abstract
Assessment of companies is vital for an accurate investment decision. Financial ratios are essential performance indicators. However, there is no consensus in their comparison among all financial ratios. Expert opinions are an indispensable resource for such assessment. This study uses an integrated approach to benefit from expert opinions. Clustering is an important area of unsupervised learning. Clustering, when assigned to classes, can also be used for classification. It is vital to classify data to apply for decision-making. This study applies the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy C-Means (FCM) for clustering and classification as a part of machine learning. Financial ratios are widely used to compare different companies. This study focuses on companies under the "Textile Leather Index" registered in the Istanbul Stock Exchange (BIST) for applying the proposed model.
The study employed current financial results and the positive and negative trends of the last year for classification. The results allow the decision-maker to choose the right company to invest in. Among 17 companies, 2 are classified as A class.
To the best of our research, using trend values and integrating FAHP and FCM for classification is new in the literature.
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Yiğit, F. (2023). Classification of XTEKS Companies During COVID-19 Pandemic using Fuzzy-Analytic Hierarchy Process and Fuzzy-C-Means. In: Kahraman, C., Sari, I.U., Oztaysi, B., Cebi, S., Cevik Onar, S., Tolga, A.Ç. (eds) Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 759. Springer, Cham. https://doi.org/10.1007/978-3-031-39777-6_71
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DOI: https://doi.org/10.1007/978-3-031-39777-6_71
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