This work in the first chapter describes reasons for the formation of nonprofit sector and the necessity of its existence. Further, individual sources of funding nonprofit organizations in the Czech Republic are described and also explained using examples from common life. I mainly focus on specificities of individual financial sources and also compare sources of funding among NPO. I also submit some conclusions of conducted research. It gives possibility to study diversity of financing different types of NPO. Last chapter is based on regression analysis. I examine certain conclusions from theoretical part of work.

Aim of this thesis was to give practical example than the perspective of internal and external economic analyst for analysis. The work deals not only with different angles analyst arising from different levels of available information, but also highlights some weaknesses in the assessment of economic analysis as such. The practical part is then evaluated on real data selected indicators of economic analysis. The final part deals with factors that affect the quality of treatment and evaluation of economic analysis as such.

The thesis is composed of three main parts. The first part is theoretical and I deal here with economic relationships between macroeconomic magnitudes. Second part dedicated to the econometric theory of prognosis follows, in which I deal with different types of prognoses and prediction methods used at present. In the third, practical, part my intended aim is to create the best possible models of relations between fundamental macroeconomic magnitudes, using real Czech economy data, and to make simulation predictions of these magnitudes based on acquired models while utilising scenario analysis. First, I deal with choice of MSE and VAR models. Then follows the estimate of particular models and validation of prognostic capabilities of particular models for static and dynamic simulation. I conclude with elaboration of macroeconomic magnitudes prognosis while using scenario analysis.

The main goal of this Master's Thesis is to determine the value of Poděbradka, a.s. as at 31 March 2015. Free cash flow to the firm model has been used for the valuation purposes. The work includes strategic and financial analyses which are used as a basis for financial plan. Consequently, discount rate is calculated and used further in the thesis. The main valuation method is supported by minor methods that are based on market comparison. Comparative analysis based on comparable traded companies and comparable transactions was performed. The thesis also include sensitivity analysis that explores influence of key factors on the enterprise value.

The master´s thesis analyzes the performance of company Řetězárna Ltd. by using the Model START, which is based on the EFQM Excellence Model principle. The first part is devoted to the theoretical description of performance measurement. The second part of the master´s thesis deals with the application of these theoretical findings in practice, in the form of an analysis according to the Model START. In the last part of the master´s thesis, according to the results of the analytical part, are proposed new possible improvements to increase the performance of Řetězárna Ltd.

The content of this master’s thesis is proposal of business plan for setting up company 123fotky s.r.o., which will run microphotobank. This name is used for photobank which sells photographs with non-exclusive rights in other words with royalty free license. Features specific for successful business plan are included in theoretical part. Analytical part provides view on environment in which will the new company operate. The main proposal of business plan is based on theoretical knowledge of this environment and on market analysis.

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The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.