The application of data mining techniques in manipulated financial statement classification: The case of turkey

Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order to create knowledge to develop a decision support system to classify firms. Auditors may annotate the firms’ statements as “correct” or “incorrect” to add their experience, and then these annotations with related indicators can be used for the learning process to generate a model. Once the model is learned and tested for validation, it can be used for new firms to predict their class values. In this research, we attempted to reveal this benefit in the framework of Turkish firms. In this regard, the study aims at classifying financially correct and false statements of Turkish firms listed on Borsa İstanbul, using their particular financial ratios as indicators of a success or a manipulation. The dataset was selected from a particular period after the crisis (2009 to 2013). Commonly used three classification methods in data mining were employed for the classification: decision tree, logistic regression, and artificial neural network, respectively. According to the results, although all three methods are performed well, the latter had the best performance, and it outperforms other two classical methods. The common ground of the selected methods is that they pointed out the Z-score as the first distinctive indicator for classifying financial statements under consideration

A framework for the selection of the right nuclear power plant

Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35–45 MWe up to 1600–1700 MWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called “external factors” like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40–60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700 MWe

Career decision making in the maritime industry: research of merchant marine officers using Fuzzy AHP and Fuzzy TOPSIS methods

Individual career planning plays a key role in achieving success, goals, and ideals in professional life. However, managing to accomplish such favorable results depends on the correct decisions of graduates to choose suitable job opportunities. Oceangoing watchkeeping officers, who are responsible for the management and administration of vessels at sea, have several job options which are differentiated by vessel type, such as; bulk carriers, chemical tankers, general cargo ships, and container ships, etc. This study aims to discuss the criteria that Turkish oceangoing watchkeeping officers take into consideration and the values they attribute to such criteria regarding their vessel type preference. The aim is to provide instructions to oceangoing watchkeeping officer candidates and academicians who are interested in these issues and related parties of maritime industry. Attribution values of the criteria are determined by means of Fuzzy Analytic Hierarchy Process (AHP) and the most preferred alternative vessel type is revealed through Fuzzy TOPSIS methodology. According to the study results, the most important factors are; revenue, perception of occupational health and safety, and labor work density. The most preferred ship type among alternatives is the oil tanker