SELECTION OF DATA CONVERSION TECHNIQUE VIA SENSITIVITY-PERFORMANCE MATCHING: RANKING OF SMALL E-VANS WITH PROBID METHOD

Sensitivity analyses are frequently performed to determine the robustness of MCDM methods, of which there are more than 200 types. In the past, rankings were compared to each other rather than to an external ranking. Thus, the direction and meaning of sensitivity can become unclear and complex. In addition, sensitivity analysis is usually performed only based on weight coefficients, but the effect of the normalization type is neglected. In this study, the most appropriate data conversion technique was investigated through an innovative sensitivity procedure to select the e-Small Van, which is an environmentally friendly logistics and transportation vehicle. Seven different normalization types based on the PROBID method (and two additional alternative MCDM methods) were used as parameters, resulting in 105 different MCDM rankings. According to the findings, MCDM rankings, which have low sensitivity, were also the performing methods that produced the highest correlation with price. What is striking is that careless choice of normalization type can be so effective as to manipulate the results. Although the most appropriate technique may vary depending on the data type, the fixed gold standard we recommend offers a flexible solution for all applications. A suitable data converter will result in the choice of a reliable electric vehicle

Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data

A major difficulty in comparing and even choosing MCDM methods is the uncertainty of information about the consistent and unique characteristics of the results produced. The objective information content of the final scores produced by MCDM methods and their relevance to real life can give us an important idea about them. In this study, first of all, seven MCDM methods with different methodologies were applied to evaluate companies’ financial performance. Then, the obtained MCDM scores were compared using two different objective verification mechanisms. The first validation criterion is the relationship of a MCDM method to real-life rankings (share price). The second criterion is the standard deviation (SD) technique used to discover the objective information content of MCDM final scores. According to the results of this study, PROMETHEE and FUCA definitely outperform other methods in terms of both SD values and strength of correlation with reference real-life rankings. Also, FUCA is methodologically simpler than other methods. However, it produced nearly identical results as the sophisticated PROMETHEE method

Analysis Of The Relationship Between Financial Performance And Stock Return: A comparison On Borsa Istanbul Manufacturing Firms

Doktora TeziFirmaların günümüz piyasa koşullarında rekabet edebilmeleri ve varlıklarını sürdürebilmeleri gösterecekleri performans düzeyine bağlıdır. Ölçülemeyen performansın yönetilemeyeceği açıktır. Doğru ölçülemeyen performans ise, yanlış yönetilmeye açık hale gelir. Soyut ve somut yönleriyle beraber düşünüldüğünde varlığı kabul edilmekle beraber performans çerçeve olarak üzerinde tam olarak uzlaşmaya varılamamış bir kavramdır. Firmaların finansal performansının ölçümü dikkat çeken ve araştırılan konuların başında gelmiştir. Finansal performansın (FP) ölçümü için yaygın kullanılan yöntemlerden biri Çok Kriterli Karar Verme (ÇKKV) yöntemidir. En iyi çözümün (optimal) bilinemediği durumlarda, baskın çözüm (pareto optimal) olarak ÇKKV yöntemleri, karar destek sisteminin bir unsuru olarak kararları iyileştirmek için sahne alır. Bu yöntemler birden çok rasyoyu ağırlık önemini de göz önüne alarak matematiksel bir süreçten geçirir ve firma için tek bir puana dönüştürür. Böylece firmaları bu puanları baz alarak başarısına göre sıralamak mümkün hale gelir. Bir ÇKKV çözüm senaryosunun gerçek yaşamı iyi modelleyebilmesi istenen bir durumdur. ÇKKV sıralamalarının gerçek yaşam sıralamaları ile ilişkili olması bu anlamda büyük kolaylık sağlar. Bu doğrultuda çalışmada Borsa İstanbul’a kayıtlı imalat sektöründen 151 firma için 2013-2019 dönemlerini kapsayan bir performans ölçümü ve değerlendirmesi yapılmıştır. TOPSIS yöntemiyle ölçülen FP modelinin inşasında genel performans türü olarak değer ve muhasebeye dayalı oranların kullanımı, uygun oranın seçimi için yaygın benimsenen vekil oranların kullanımı ve diğer çalışmalardan farklı olarak rasyo formu için Carton ve Hofer (2006)’in önerdiği statik ve değişim odaklı rasyo kullanımı genel yaklaşım olarak benimsenmiştir. Elde edilen TOPSIS-FP sıralama sonuçları ile hisse senedi getiri sıralamaları arasındaki ilişki Spearman sıra korelasyon testiyle araştırılmıştır. Sonuçlara göre finansal performans ile hisse senedi getirisi sıralamaları arasında hemen hemen tüm baz dönemler için istatistiksel olarak orta veya güçlü şiddette anlamlı ilişkiler bulunmuştur. Diğer taraftan çalışma sonuçları başka pratik bir sonuç doğurmuştur. ÇKKV-FP uygulamalarında ağırlıklandırma ve ÇKKV yöntemi seçimi bir problemdir ve kullanıcılar için bir endişe kaynağı olabilmektedir. Bu çalışma, bunun çözümü için kullanışlı olan nicel bir duyarlılık analizi kriteri önermiştir. TOPSIS ve WSA yöntemleri bu kriter üzerinden karşılaştırılmış ve bir değerlendirme yapılmıştır.The ability of companies to compete and survive in today’s market conditions depends on their performance level. It is clear that unmeasured performance cannot be managed. Performance that cannot be measured correctly becomes vulnerable to mismanagement. When the performance is considered together with its abstract and concrete aspects, although its existence is accepted, it is a phenomenon that cannot be fully agreed on as a framework. Measurement of the financial performance of firms has been one of the most notable and researched issues. One of the widely used methods for measuring financial performance (FP) is Multi Criteria Decision Making (MCDM) method. In cases where the best (optimal) solution is not known, MCDM methods as the dominant solution (pareto optimal) take the stage to improve the decisions as an element of the decision support system. These methods pass multiple ratios through a mathematical process taking into account the weight importancei, and convert them into a single score for the firm. Thus, it becomes possible to rank companies according to their success based on these scores. It is desirable that a MCDM solution scenario can model real life well. The fact that MCDM rankings are related to real life rankings provides great convenience in this sense. In this direction, a performance measurement and evaluation for 151 companies from the manufacturing sector registered in Borsa Istanbul by covering the 2013- 2019 period was made in this study. In the construction of FP model measured by TOPSIS method; the use of value and accounting ratios as generic performance types; the use of the widely accepted surrogate rates to select the appropriate rate; and unlike other studies static and change-oriented ratio usage suggested by Carton and Hofer (2006) for ratio form has been adopted as a general approach. The relationship between acquired TOPSIS-FP ranking results and stock return rankings was investigated by Spearman rank correlation test. According to the results, statistically moderate or strong significant relationships were found between financial performance and stock return rankings for almost all base periods. On the other hand, the results of the study led to another practical result. Weighting and MCDM method selection is a problem in MCDM-FP applications, and this can be a concern for users. This study suggested a useful quantitative sensitivity analysis criterion as a solution for this. TOPSIS and WSA methods were compared based on this criterion, and an evaluation was made

An Alternative Sensitivity Analysis for the Evaluation of MCDA Applications: The Significance of Brand Value in the Comparative Financial Performance Analysis of BIST High-End Companies

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made

Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria

Comparison of fuzzy and crisp decision matrices: An evaluation on PROBID and sPROBID multi-criteria decision-making methods

The use of multi-criteria decision-making (MCDM) methods to select the most appropriate one from a range of alternatives considering multiple criteria is a suitable methodology for making informed decisions. When constructing a decision or objective matrix (DOM) for MCDM procedure, either crisp numerical values or fuzzy linguistic terms can be used. A review of relevant literature indicates that decision experts often prefer to give linguistic terms (instead of crisp numerical values) based on their domain knowledge, to establish a fuzzy DOM. However, previous research articles have not adequately studied the selection between fuzzy and crisp DOM in MCDM, especially under the context of assessing the financial performance (FP) of listed firms – a notably complex decision-making problem. As such, the primary motivation of this study is to bridge this research gap through comparative analyses of fuzzy and crisp DOM in MCDM. Along this path, and in order to handle fuzzy DOM, this work also proposes two new fuzzy MCDM methods: fuzzy preference ranking on the basis of ideal-average distance (PROBID) and fuzzy sPROBID (simpler PROBID), extending the applicability of the original crisp PROBID and sPROBID methods. Moreover, for the first time in the literature, this work compares the FP rankings obtained using fuzzy MCDM methods with an objective benchmark we have identified, i.e., the real-life stock return (SR)-based ranking. The case study of ranking the FP of 32 listed firms demonstrates that the fuzzy MCDM methods produce higher correlation results with the SR-based ranking. The results also suggest that the proposed fuzzy sPROBID method with triangular fuzzy DOM performs the best for assessing the FP of firms in terms of Spearman’s rank correlation coefficient with the SR-based ranking. Overall, the contributions of this work are three-fold: first, it proposes two new fuzzy MCDM methods (i.e., fuzzy PROBID and fuzzy sPROBID); second, it advances the application of fuzzy MCDM methods in assessing and ranking the FP of listed firms to make rational investment decisions in the financial market; third, it studies the selection between fuzzy and crisp DOM through comparisons with an objective benchmark