The volume profile method and its theoretical connection with microeconomic theory as the main premise of its application

Research background: This article was conceived as a very valuable basis and the result of theoretical research in the field of microeconomics with a specific application. Specifically, in the article we tried to accurately describe the logic and predictive weight of the Volume profile method with reference to the exact descriptions of the functioning of the market mechanism. Purpose of the article: The aim of our paper is to describe in detail and identify the microeconomic foundations of the Volume Profile prediction tool. Methods: In the first chapter of the article, we described in detail the investigated method of volume profile. Subsequently, we described its logic, functions and detailed calculation as well as its use. We continued with a description of the basic interactions between supply and demand, as well as a description of the market mechanism. Findings & Value added: The result was an accurate identification and description of the connection between the operation of the investigated prediction method and its microeconomic basis

Normality and Its Verification as a Basic Prerequisite for the Application of VaR

If we start to deal with the topics of investing or trading in the financial markets, sooner or later we will encounter the topic of risk. Risk is one of the basic input variables in assessing the suitability and profitability of an investment and therefore there are a number of procedures and methods for its quantification. In our article, we address the issue of portfolio risk quantification through the VaR method and verification of its basic assumption, namely the normal distribution of values. The aim of the article is to compile an overview of procedures and methods for verifying the normal distribution and compare their specifics. In the first chapter of our paper, we focused on the definition of risk, its types. The second chapter focuses on methods of risk quantification and description of individual methods. In the third chapter, we will describe in detail the possibilities of verifying the normality of the distribution of values. In the last chapter, we will briefly interpret the information obtained and identified the advantages, disadvantages and other specifics of individual methods of verifying the normal distribution. We consider the aim of the article to be fulfilled and we believe that it will be a valuable contribution in this area of research

Company bankruptcy and its prediction in conditions of globalization

The internationalisation and globalisation of today’s world, especially in business, brings whole new range of opportunities, challenges and also many kinds of risks. Today´s global market in process of globalization offers many different ways of doing business and also whole new ranges of methods how to analyse optimize and also minimize it´s risks. The issue of bankruptcy models is still relevant given by the high competition in the markets and the increasingly frequent crises. Not only in the world, but also in our country, we can see a huge number of bankruptcies of businesses. If the company wants to thrive and successfully compete in the market environment, it should conduct a regular financial analysis of its activities, evaluate successes and failures, and use the results obtained to make strategic decisions about future business development. The aim of the article is to examine the possibilities of predicting the bankruptcy of companies and describe their individual procedures. In the first part of the paper we defined the terms such as insolvency, decline of company and bankruptcy. We continue with a brief overview of the development of bankruptcy models from the first attempts to modern practices. We have described and defined each model in detail, described its specifics, and described the calculation procedure. The biggest added value of this paper is a comprehensive elaboration of an overview of the possibilities of predicting company manrots through bankruptcy models. We can say that the goal of the post has been fulfilled

Usage of P/E earning models as a tool for valuation of shares in condition of global market

With the rapid development of computational technology, non-traditional mathematical and statistical methods have also parallely developed to help simplify and accelerate the computation of certain tasks, or even to solve problems that are usually unsolvable. The aim of this paper is to get closer to the P/E earning models and briefly summarize their calculation and usage options. In the first part of our paper we briefly worked out the theoretical basis of these models. Furthermore, we focused on a detailed description of their calculation and use in calculating the value of shares. In the second part of the work we focused on the application of the calculation of the selected P / E model to Apple inc. in the course of 2018 and compared the data obtained with another instrument to identify the intrinsic value of the action. In the last part we focused on the interpretation and summary of the results of the application. We consider the greatest added value of our contribution to be a theoretical comparison of different types of calculation and deeper application of the selected model to real market prices of Apple inc. with the interpretation of the results obtained. We can conclude that the aim we have set is met and we believe that our article will be a valuable addition to the issue in this area

Global financial market and tools for its prediction

Research background: In today’s modern world, we can constantly observe turbulent changes in every aspect of human life. These changes have also affected the area of financial markets, where we can identify a gigantic shift in the last few decades. In our article, we focused on the issue of trading in financial markets, the history of trading in contrast to the current situation, but especially the tool for predicting future price movements of financial instruments. In our article, we dealt with the issue of financial markets, their development and prediction tools. Purpose of the article: The aim of our article was to provide a brief overview of the path that the financial markets area has taken in the recent past to the present day. Methods: We used formal methods such as analysis and synthesis of theoretical findings and others. Findings & Value added: Based on the above-mentioned methods, we developed a clear framework for the development of financial markets, forecasting tools and specified the volume profile method and identified its strong relationship to the functioning of financial markets and the auction process itself. We consider the goal of the paper to be fulfilled and we believe it will bring a certain benefit of research in the given area

Detecting earnings manipulation and fraudulent financial reporting in Slovakia

Research background: Misleading financial reporting has a negative impact on all stakeholders since financial records are the primary source of information on financial stability, economic activity, and financial health of any company. The handling of them is primarily the responsibility of managers or owners and reasons for doing so may differ. Their common denominator is the artificial creation of information asymmetry to get different types of benefits. It is, therefore, logical that the issue of detecting opportunistic earnings management comes to the fore. Purpose of the article: The purpose of the study is to create a discriminant model of the detection of earnings manipulators in the conditions of the Slovak economy.  Methods: We used the discriminant analysis to create a model to identify fraudulent companies, based on the real data on companies that were convicted from misleading financial reporting in connection with tax fraud in the years 2009-2018. The model is inspired by the Beneish model, which is one of the most applied fraud detection methods at all. Findings & Value added: In order to achieve more accurate detection results, we extended the original model by taking into account the values of indicators from three consecutive years, i.e. by taking into account the development of the potential tendency of companies to be involved in opportunistic earnings management. Our model correctly identified 86.4% of fraudulent companies and overall reaches 84.1% classification ability. Both models were applied on empirical data on 1,900 Slovak companies from the years 2016-2018, while their overlap was 32.7% for fraudulent companies and 38.4% for non-fraud companies. This is a very useful result, as the application of both models rein-forces the results obtained and the identical classification of the company into fraudulent indicates that the manipulation of earnings occurs with a high probability