Analyzing Financial Performances of the Artificial Intelligence Firms by Using the AHP-TOPSIS Method - BURAK, Mehmet Fatih (PEARSON JOURNAL, 2023)
This study was carried out to evaluate the financial performance of artificial intelligence companies, which could not be found to be examined in the literature, by using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). For this purpose, the weights of the liquidity, financial structure, profitability and operating ratios criteria and the weights of the sub-criteria (current ratio, cash ratio, acid test ratio, long-term debt to capital ratio, debt to assets ratio, debt to equity ratio, net profit margin, return on equity (ROE), return on assets (ROA), return on investment (ROI), inventory turnover, asset turnover, receivable turnover) were determined by using the AHP method. As a result of the AHP analysis performed for the criteria, it was concluded that the criterion with the highest weight (n=0.533) was the profitability ratio (C3). In the analysis of all sub-criteria, it was understood that the sub-criterion with the highest global weight (n=0,186) was ROE (C3c). As a result of the TOPSIS analysis of the financial performance of the artificial intelligence companies selected between 2022-2019, it was understood that the artificial intelligence company that achieved the highest score in 2022, 2020 and 2019 was Firm 3. In 2021, it was concluded that Firm 2 achieved the highest score. This study is thought to provide helpful information to researchers and practitioners.