Our study is aimed to validate the results of MDA methods on companies’ key performance indicators (KPI) and confirm their strong utility in the decision making process within the investment portfolio management. We applied four methods of MDA in order to conclude if the results are convergent and conduct us to the same decisions to be taken regarding investment portfolios management within the integrated capital markets.
We evaluated the companies’ competitiveness and their financial performance by multivariate statistical methods of principal component analysis and factor analysis. Then the companies were ranked and classified by Cluster and Discriminant Analysis (using Fisher, Bayesian and Mahalanobis classifiers), our analysis taking into account the advantages and disadvantages of each method. Investors may choose stocks which are more competitive, in order to reduce risk and make profits.
The application of principal component analysis (PCA) in the original space allowed us to obtain a high quality synthesis of information related to the companies’ financial performance. Moreover, the use of MDA methods allowed us to save considerable time in our efforts to select the most important financial information needed in decision making process related to investment portfolios management. At the same time, the final obtained results represent useful prediction and pertinent forecasting tools in the integrated capital markets.
Key words: financial performance/indicators, portfolio management, capital markets, multidimentional statistical analysis