Normalization Technique Selection for MCDM Methods: A Flexible and Conjunctural Solution that can Adapt to Changes in Financial Data Types

Abstract

It makes sense to use the MCDM methodology to select and rank alternatives for a multi-criteria problem. As it is known, since it is not possible to use criteria consisting of different units in a common calculation, it is necessary to convert them into a unitless dimension. Many alternative normalization techniques have been proposed in the past for this conversion process. On the other hand, normalization techniques that appear to be accurate fair transformers have the potential to affect the final ranking of any MCDM method, and this is a significant problem. As a matter of fact, these alternative techniques can change the best alternative and overall ranking for an MCDM. Therefore, an unconsciously chosen normalization technique may reduce the quality of the findings. However, it cannot be said that there is a consensus on the choice of normalization methods. Previous studies have unanimously stated that normalization methods can affect MCDM results. In this study, from an innovative perspective, the effect of normalization methods on the results is evaluated with a third party, an external constant factor. In other words, we focus on how the normalization technique affects the relationship of MCDM with an external factor. Thus, we want to achieve a fair assessment by choosing a reference point. According to the findings of the approach tested in different financial data sets, the most successful technique may change periodically. Therefore, the selection of the best normalization technique depending on the data structure should be evaluated from a dynamic rather than static perspective.

Keywords

• Normalization
• MCDM
• FINANCIAL DATA ANALYTICS

ÇKKV Yöntemleri İçin Normalizasyon Tekniği Seçimi: Finansal Veri Türlerindeki Değişikliklere Uyum Sağlayabilecek Esnek ve Konjonktürel Bir Çözüm

Öz

MCDM metodolojisini çok kriterli bir problemde alternatiflerden en uygun olanı seçmek ve sıralama yapmak amacıyla kullanmak mantıklıdır. İlk karar matrisindeki birbirinden farklı amaçlara sahip kriterler genelde farklı birimlerden oluşan bir yapıya sahip olduğundan bu verileri birimsiz bir boyutta normalize ederek homojenize etmek gerekir. Öte yandan masum bir dönüştürücü gibi görünen normalizasyon tekniklerinin herhangi bir MCDM yönteminin nihai sıralamasını etkileyebilme potansiyeli önemli bir sorundur. Nitekim bu teknikler genel sıralamayı ve en iyi alternatifin belirlenmesini etkileyebilmektedir. Aslında herhangi bir MCDM yönteminin amacının en iyi olan bir alternatifi önermek olduğu göz önüne alındığında rast gele seçilen bir normalizasyon tekniği karar verici için ciddi bir kalite maliyeti oluşturabilir. Ne var ki normalizasyon yöntemlerinin seçimi için henüz literatürde kesin bir mutabakat yoktur. Bu çalışmada yenilikçi bir bakış açısıyla normalizasyon yöntemlerinin gerçek yaşamı yakalama derecesi ya da üçüncü bir tarafla ilişkisi açısından değerlendirilmesi önerilmektedir. Bu çalışmada ÇKKV yönteminin (normalizasyon sonrası denklemi sabit tutularak) farklı normalizasyon yöntemlerinin üretilen sonuçları nasıl etkiledikleri incelenmiştir. Farklı finansal veri setlerinde test edilen yaklaşımın bulgularına göre genel itibariyle dönemsel olarak en başarılı olan teknik farklıdır. Dolayısıyla veri yapısına bağlı olarak en iyi normalizasyon tekniğinin seçimi, statik değil dinamik bir bakış açısıyla değerlendirilmelidir. Ayrıca bu çalışma, klasik normalizasyon yöntemlerinin yanı sıra sıralama (ranking) temelli dönüştürme fonksiyonunun da kullanılabileceğini net olarak göstermiştir.

Anahtar Kelimeler

• Normalizasyon
• ÇKKV
• Finansal Veri Analitiği

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