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In master's thesis the theoretical aspect of bankruptcy prediction models of companies is examined and evaluated; bankruptcy prediction models are analyzed; the characteristics of the companies providing travel organization services are examined; bankruptcy prediction calculations of fifteen selected Lithuanian companies that provide (provided) travel organization services and outbound tourism services were carried out, the adequacy of bankruptcy prediction models is determined for bankruptcy prediction of examined companies, the results obtained during the study were summarized. The master's thesis consists of three parts for the achievement of desired objective. The first part covers the analysis of the concept of bankruptcy, causes for bankruptcy, analysis of the importance of bankruptcy prediction and methods of diagnosis of bankruptcy. The second part presents the analysis of Lithuanian companies that provide travel organization services, the study of the impact of legislation of the Republic of Lithuania on tourism sector in Lithuania, the analysis of bankruptcy processes of the period of 1993-2015 of bankrupted companies that provided travel organization services. The third part includes bankruptcy prediction calculations for particular companies as well as the determination of adequacy of bankruptcy prediction models for bankruptcy prediction of examined companies. The study conducted in the period of the years 2013 - 2014.

In master's thesis the theoretical aspect of bankruptcy prediction models of companies is examined and evaluated; bankruptcy prediction models are analyzed; the characteristics of the companies providing travel organization services are examined; bankruptcy prediction calculations of fifteen selected Lithuanian companies that provide (provided) travel organization services and outbound tourism services were carried out, the adequacy of bankruptcy prediction models is determined for bankruptcy prediction of examined companies, the results obtained during the study were summarized. The master's thesis consists of three parts for the achievement of desired objective. The first part covers the analysis of the concept of bankruptcy, causes for bankruptcy, analysis of the importance of bankruptcy prediction and methods of diagnosis of bankruptcy. The second part presents the analysis of Lithuanian companies that provide travel organization services, the study of the impact of legislation of the Republic of Lithuania on tourism sector in Lithuania, the analysis of bankruptcy processes of the period of 1993-2015 of bankrupted companies that provided travel organization services. The third part includes bankruptcy prediction calculations for particular companies as well as the determination of adequacy of bankruptcy prediction models for bankruptcy prediction of examined companies. The study conducted in the period of the years 2013 - 2014.

The beginning of XXI century was marked by few events that had influence on international relations in the whole world. Terror attacks in the United States of America in September 11, 2001, were the first such high a big range attacks against the USA. It had shown that the USA – the most powerful state of the world – is vulnerable to more danger, than nuclear weapons alone. After September 11 the USA started war against terrorism in Afghanistan, and many states supported it. However, in spring 2003 the USA unilaterally, without international support, decided to use military power in Iraq, arguing that Iraq possessed potential threat – weapons of mass destruction. Unilateralism, and military and technological differences between the USA and its allies have brought to light a presumption that in the international system there is a single pole, a superpower (hyperpower), the United States of America. The existence of one pole only can have different consequences on international system and international security. The aim of this article was to determine specific features of the unipolar world, influence of polarity of international system upon international security, and specific threats to security that emerge only in unipolar international systems. Another aim was to try to predict the end of the unipolar system: what could be the next poles and when could it come to the end. After analysing a number of articles concerning this topic, few conclusions were drawn. Contemporary international system is unipolar and the single pole is the USA; the USA is superior in military, economic, geopolitical, and political spheres at the same time. Increasing military power and unilateralism of the USA may be treated as a threat to international security. Great powers are trying to balance unipolar power, however, the creation of balance to unipolar power is complicated for several reasons.[.].

The beginning of XXI century was marked by few events that had influence on international relations in the whole world. Terror attacks in the United States of America in September 11, 2001, were the first such high a big range attacks against the USA. It had shown that the USA – the most powerful state of the world – is vulnerable to more danger, than nuclear weapons alone. After September 11 the USA started war against terrorism in Afghanistan, and many states supported it. However, in spring 2003 the USA unilaterally, without international support, decided to use military power in Iraq, arguing that Iraq possessed potential threat – weapons of mass destruction. Unilateralism, and military and technological differences between the USA and its allies have brought to light a presumption that in the international system there is a single pole, a superpower (hyperpower), the United States of America. The existence of one pole only can have different consequences on international system and international security. The aim of this article was to determine specific features of the unipolar world, influence of polarity of international system upon international security, and specific threats to security that emerge only in unipolar international systems. Another aim was to try to predict the end of the unipolar system: what could be the next poles and when could it come to the end. After analysing a number of articles concerning this topic, few conclusions were drawn. Contemporary international system is unipolar and the single pole is the USA; the USA is superior in military, economic, geopolitical, and political spheres at the same time. Increasing military power and unilateralism of the USA may be treated as a threat to international security. Great powers are trying to balance unipolar power, however, the creation of balance to unipolar power is complicated for several reasons.[.].

The beginning of XXI century was marked by few events that had influence on international relations in the whole world. Terror attacks in the United States of America in September 11, 2001, were the first such high a big range attacks against the USA. It had shown that the USA – the most powerful state of the world – is vulnerable to more danger, than nuclear weapons alone. After September 11 the USA started war against terrorism in Afghanistan, and many states supported it. However, in spring 2003 the USA unilaterally, without international support, decided to use military power in Iraq, arguing that Iraq possessed potential threat – weapons of mass destruction. Unilateralism, and military and technological differences between the USA and its allies have brought to light a presumption that in the international system there is a single pole, a superpower (hyperpower), the United States of America. The existence of one pole only can have different consequences on international system and international security. The aim of this article was to determine specific features of the unipolar world, influence of polarity of international system upon international security, and specific threats to security that emerge only in unipolar international systems. Another aim was to try to predict the end of the unipolar system: what could be the next poles and when could it come to the end. After analysing a number of articles concerning this topic, few conclusions were drawn. Contemporary international system is unipolar and the single pole is the USA; the USA is superior in military, economic, geopolitical, and political spheres at the same time. Increasing military power and unilateralism of the USA may be treated as a threat to international security. Great powers are trying to balance unipolar power, however, the creation of balance to unipolar power is complicated for several reasons.[.].

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Bankruptcy may be at any business, especially during the economic crisis, and cause a lot of negative consequences not only for the companies but also the state and the society. Therefore, the probability of bankruptcy can be calculated using bankruptcy prediction models, most of which are based on financial reports. However, in case of inflation, financial reports and financial analysis results are distorted, so it is important to determine if the inflation doesn't distort the probability of bankruptcy prediction models, because these models are made integrating financial ratios, which are used in financial analysis. The object of MBA graduation paper is bankruptcy prediction models, and the purpose is to explore the impact of inflation in accuracy of the bankruptcy prediction models. The main tasks are, inspected the features, causes and consequences of the bankruptcy, to disclose the concept of bankruptcy, to determine the importance of the bankruptcy prediction models in the solvency assessment, examined the corporate bankruptcy and inflation statistics, to put the relevance and importance of this study and to investigate the inflation impact on the accuracy of the bankruptcy prediction models. The work consists of an introduction, 3 parts and conclusions. Bankruptcy – is a situation when it is proceeded against insolvent company for its financial default. The growth of the bankruptcy probability is driven by the increasing financial leverage, i.e., the growing volume of loan capital across the capital structure of corporate. The Bankruptcy of the company can be caused by macro-economical and micro-economical factors that are affected differently: can cause sudden bankruptcy of enterprises or increase decline of company and slowly anticipate to the bankruptcy. Corporate bankruptcies around the world are defined as the usual economic phenomena, which has recently increased significantly in the most countries of European union and that causes a lot of negative economic, financial and social consequences for the employees, the state, creditors, suppliers, investors, customers, only for the competitors they are useful. Bankruptcy prediction models help companies to continuously monitor the situation. There is a very wide classification of models in literature, but generally all models are divided into the classical statistical and intelligence techniques, in which different authors see both advantages and disadvantages, and therefore the one opinion of the specific bankruptcy prediction model for the appropriateness of today's bankruptcy has not yet been initiated. In the literature we can found the development of new models, which try to eliminate disadvantages of the old models. However, the questionnaire results showed that in practice the most popular are old classic bankruptcy prediction models, which are based on companies' financial reports. But, in case of inflation, which is observed in every country, despite of its level of development, the probability of these bankruptcy prediction models is distorted – the maximum error calculated in Springate and Altman models, while the lowest – in Ohlson. For each company the impact of inflation is different because of differences in amounts of corporate balance sheets and income (loss) reports items and their formation date. Therefore, investing in companies of different countries, doing the competitors' analysis of different foreign companies or simply making management decisions, it is important to consider the inflation.

Bankruptcy may be at any business, especially during the economic crisis, and cause a lot of negative consequences not only for the companies but also the state and the society. Therefore, the probability of bankruptcy can be calculated using bankruptcy prediction models, most of which are based on financial reports. However, in case of inflation, financial reports and financial analysis results are distorted, so it is important to determine if the inflation doesn't distort the probability of bankruptcy prediction models, because these models are made integrating financial ratios, which are used in financial analysis. The object of MBA graduation paper is bankruptcy prediction models, and the purpose is to explore the impact of inflation in accuracy of the bankruptcy prediction models. The main tasks are, inspected the features, causes and consequences of the bankruptcy, to disclose the concept of bankruptcy, to determine the importance of the bankruptcy prediction models in the solvency assessment, examined the corporate bankruptcy and inflation statistics, to put the relevance and importance of this study and to investigate the inflation impact on the accuracy of the bankruptcy prediction models. The work consists of an introduction, 3 parts and conclusions. Bankruptcy – is a situation when it is proceeded against insolvent company for its financial default. The growth of the bankruptcy probability is driven by the increasing financial leverage, i.e., the growing volume of loan capital across the capital structure of corporate. The Bankruptcy of the company can be caused by macro-economical and micro-economical factors that are affected differently: can cause sudden bankruptcy of enterprises or increase decline of company and slowly anticipate to the bankruptcy. Corporate bankruptcies around the world are defined as the usual economic phenomena, which has recently increased significantly in the most countries of European union and that causes a lot of negative economic, financial and social consequences for the employees, the state, creditors, suppliers, investors, customers, only for the competitors they are useful. Bankruptcy prediction models help companies to continuously monitor the situation. There is a very wide classification of models in literature, but generally all models are divided into the classical statistical and intelligence techniques, in which different authors see both advantages and disadvantages, and therefore the one opinion of the specific bankruptcy prediction model for the appropriateness of today's bankruptcy has not yet been initiated. In the literature we can found the development of new models, which try to eliminate disadvantages of the old models. However, the questionnaire results showed that in practice the most popular are old classic bankruptcy prediction models, which are based on companies' financial reports. But, in case of inflation, which is observed in every country, despite of its level of development, the probability of these bankruptcy prediction models is distorted – the maximum error calculated in Springate and Altman models, while the lowest – in Ohlson. For each company the impact of inflation is different because of differences in amounts of corporate balance sheets and income (loss) reports items and their formation date. Therefore, investing in companies of different countries, doing the competitors' analysis of different foreign companies or simply making management decisions, it is important to consider the inflation.

In one of the strategic European Union (EU) aims to reduce carbon dioxide emissions, Member States committed themselves since 2020 to build only zero-energy buildings. Constantly growing the number of more and more energy-efficient buildings, Lithuania and other EU member states began to encounter with the problem that the actual energy consumption for space heating and ventilation does not meet the designed values. There is evidences of "performance gap", reaching 2 or more times. In the study parametric sensitivity analysis is performed along with the determining input sensitivity index. Monte-Carlo method provides probabilistic energy consumption forecast. Calculated data are compared with the actual normalized energy consumption of the building. The calculation procedures are performed under LST EN 13790 standard methodology basis. Calculations allow to state the actual energy consumption is up to 3 times higher than the targeted value. The greatest impact on energy performance gap makes inaccurate prediction of ventilation heat recovery coefficient, internal temperature change from the design values and inaccurate external envelope heat transfer coefficient assessment. Structure: introduction, literature analysis, practical research, conclusions and suggestions, references. Thesis consist of: 49 p. text without appendixes, 23 pictures, 8 tables, 32 bibliographical entries. Appendixes included.

In one of the strategic European Union (EU) aims to reduce carbon dioxide emissions, Member States committed themselves since 2020 to build only zero-energy buildings. Constantly growing the number of more and more energy-efficient buildings, Lithuania and other EU member states began to encounter with the problem that the actual energy consumption for space heating and ventilation does not meet the designed values. There is evidences of "performance gap", reaching 2 or more times. In the study parametric sensitivity analysis is performed along with the determining input sensitivity index. Monte-Carlo method provides probabilistic energy consumption forecast. Calculated data are compared with the actual normalized energy consumption of the building. The calculation procedures are performed under LST EN 13790 standard methodology basis. Calculations allow to state the actual energy consumption is up to 3 times higher than the targeted value. The greatest impact on energy performance gap makes inaccurate prediction of ventilation heat recovery coefficient, internal temperature change from the design values and inaccurate external envelope heat transfer coefficient assessment. Structure: introduction, literature analysis, practical research, conclusions and suggestions, references. Thesis consist of: 49 p. text without appendixes, 23 pictures, 8 tables, 32 bibliographical entries. Appendixes included.