Financial performance research with multi-criteria decision making (MCDM) methods, is a common subject of study not only for researchers in the finance literature but also in the applied sciences. Financial performance manifests itself in an internal universe that a firm can directly control, while the share return of the same firm is shaped synchronically in an external universe that cannot be controlled directly. On the other hand, preferring the most suitable MCDM and weighting method to use in measuring financial performance is often regarded as a source of uncertainty. In this study, the share price is used as an external proxy and a tool for comparing MCDM methods, completely different from the previously proposed approaches based on the superiority of internal features. This study was conducted on 131 manufacturing companies in Borsa Istanbul, covering entire 20-quarter period between 2014 and 2018. The experimental findings of the study provide valid solutions for the MCDM and weighting selection problem, that can be proposed as a practical and indirect solution. The results show that preference ranking organization method for enrichment of evaluations (PROMETHEE) method used with hybrid weighting technique produced by far the best performance rankings in 19 out of 20 quarterly periods when compared to the technique for order preference by similarity to ideal solution (TOPSIS) and weighted sum approach (WSA).
Abstract-Forecasting sales quantity and sales revenue is very vital for a company to take action for the next period for sustainable competition. It is especially important for growing industries like grocery retailing industry. Turkey's grocery retailing industry is evolving rapidly. Due to increasing importance; the aim of this study is to forecast the sales revenue of grocery retailing industry in Turkey with the help of grocery retailers marketing costs, gross profit, and its competitors' gross profit by using artificial neural network. Artificial neural networks are models which are used for forecasting because of their capabilities of pattern recognition and machine learning. ANN method is used to forecast the sales revenue of upcoming period. According to results there are high similarities between forecasted and actual data. Forecasted results of this study are bigger or smaller than the actual data for only 10%. Because of this high accuracy, companies at grocery retailing industry in Turkey can use ANN as a forecasting tool.Index Terms-Sales revenue, forecasting sales revenue, grocery retailing industry, artificial neural network.
With the volatility crisis brought by the pandemic, technology and health sector companies have entered the agenda of more investors in the global capital markets. COVID-19 has increased the uncertainty in financial markets as well as paralyzing the health systems of countries. Making the optimum decision among multiple alternatives is of vital importance for investors. For this purpose, multiple criteria decision-making applications (MCDA) have been used with increasing frequency in the recent years. In this study, the financial performance of healthcare companies traded in Borsa Istanbul was examined by MOORA and TOPSIS methods for four periods during the pandemic process. Interestingly, the same companies came out first in terms of financial performance for both methods in three of the four periods analyzed. For this purpose, these two methods have been recommended to the financial decision makers who are on the verge of analyzing the companies in financial markets during uncertainty.
The aim of this study is to help financial decision makers by making long-term performance analysis of initial public offerings with a different comparative analysis perspective. Multi-criteria decision analysis (MCDA) methods are used in problems with complex answers. The pandemic, which spread throughout the world in the first quarter of 2020, created a short-term shock effect in the capital markets, but capital markets survived this shock with investors. While the number of shareholders in Borsa Istanbul was 1.3 million before the pandemic, this number exceeded 3.3 million. An increase in this number also means an increase in the number of financial decision-makers. At this research, the long-term performance of 49 initial public offerings, which took place in Borsa Istanbul before the pandemic is analyzed with a comparative MCDA analysis perspective. To that end, the study, in which CRITIC weighting technique and ARAS, MOORA, TOPSIS, COPRAS and ELECTRE III methods were used, examined 10 quarter periods during the pandemic process. As a result of the research, which is the most comprehensive MCDA study in the field of IPOs, the MOORA method has been recommended to financial decision-makers because it produced superior results compared to other 4 methods analyzed.
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