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Assessment of Economic Situation of a Company and Proposals for Its Improvement
Trtková, Markéta ; Velecký, Lukáš (referee) ; Doubravský, Karel (advisor)
The diploma thesis evaluates the economic situation of the company Niveko s.r.o. in between years 2011 to 2018. The theoretical part describes financial indicators, time series, regression and correlation analysis. The analytical part contains calculations of financial indicators, some of which are selected for statistical analysis, which is used to determine the expected development of indicators in the next two years or to reveal the dependence between selected indicators. The last part contains suggestions for improving the current economic situation of the company.
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Risk Analysis for Important Factors of Firm Using Statistical Methods
Ochabová, Miroslava ; Žák, Libor (referee) ; Karpíšek, Zdeněk (advisor)
This diploma thesis deals with the analysis of financial indicators using time series, regression analysis and interval regression analysis of a selected company. The diploma thesis describes selected financial indicators, time series, regression analysis and interval regression analysis. Furthermore, the calculations of financial indicators for the selected company and the characteristics of the time series are performed. Individual financial indicators are subjected to regression analysis and interval regression analysis. Based on the performed analyzes, the company's risk factors are determined and recommendations for the improvement of the current situation in the company are proposed.
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Risk Management in Selected Subject by Means of Statistical Methods
Oralová, Ivana ; Žák, Libor (referee) ; Karpíšek, Zdeněk (advisor)
The diploma thesis is focusing on the review of financial indicators of a selected company through the use of statistical methods. Time series analysis, interval regression analysis, and regression analysis were used to analyse financial indicators, and based on that a prediction of the development in the next two years was created. The analysis also addresses potential risks for the company and possible ways to lower them. Based on the information obtained from the analysis a complete evaluation is created and recommendations are made to improve the company’s situation.
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Evolutionary Prediction of Time Series
Křivánek, Jan ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
This thesis summarizes knowledge in the field of time series theory, method for time series analysis and applications in financial modeling. It also resumes the area of evolutionary algorithms, their classification and applications. The core of this work combines these knowledges in order to build a system utilizing evolutionary algorithms for financial time series forecasting models optimization. Various software engineering techniques were used during the implementation phase (ACI - autonomous continual integration, autonomous quality control etc.) to ensure easy maintainability and extendibility of project by more developers.
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FORMAL MODEL OF DECISION MAKING PROCESS FOR HIGH-FREQUENCY DATA PROCESSING
Zámečníková, Eva ; Rábová, Ivana (referee) ; Šaloun, Petr (referee) ; Kreslíková, Jitka (advisor)
Tato disertační práce se zabývá problematikou zpracování vysokofrekvenčních časových řad. Zaměřuje se na návrh algoritmů a metod pro podporu predikce těchto dat. Výsledkem je model pro podporu řízení rozhodovacího procesu implementovaný do platformy pro komplexní zpracování dat. Model navrhuje způsob formalizace množiny podnikových pravidel, které popisují rozhodovací proces. Navržený model musí vyhovovat splnění požadavků na robustnost, rozšiřitelnost, zpracování v reálném čase a požadavkům ekonometriky. Práce shrnuje současné poznatky a metodologie pro zpracování vysokofrekvenčních finančních dat, jejichž zdrojem jsou nejčastěji burzy. První část práce se věnuje popisu základních principů a přístupů používaných pro zpracování vysokofrekvenčních časových dat v současné době. Další část se věnuje popisu podnikových pravidel, rozhodovacího procesu a komplexní platformy pro zpracování vysokofrekvenčních dat a samotnému zpracování dat pomocí zvolené komplexní platformy. Důraz je kladen na výběr a úpravu množiny pravidel, které řídí rozhodovací proces. Navržený model popisuje množinu pravidel pomocí maticové gramatiky. Tato gramatika spadá do oblasti gramatik s řízeným přepisováním a pomocí definovaných matic umožňuje ovlivnit zpracování dat.
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Algorithmic Trading Using Artificial Neural Networks
Šeda, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology.Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.
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Deep Neural Networks for Time Series Forecasting
Kayabasi, Yigit Mertol ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
Time series forecasting is a task of both academic and pragmatic interest. Although it has been long dominated by qualitative methods and simple quan- titative methods, machine learning and deep learning algorithms in modelling temporal data has become more common, but the progress is still far from the progress in typical machine learning tasks like computer vision or natural lan- guage processing. Recurrent neural networks are the most natural choice for modelling sequential data, but training them is tricky especially to learn from long sequences. Recently a divergence from back propagation Reservoir Comput- ing paradigm has started to draw attention with the performance of the models arising from it in this kind of tasks. They proved to be a good option partic- ularly for modelling rather more chaotic systems. In this thesis we will explore and compare these two families of neural networks regarding their performance and implementation. 1
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Three essays on empirical Bayesian econometrics
Adam, Tomáš ; Komárek, Luboš (advisor) ; Feldkircher, Martin (referee) ; Herrala, Risto (referee) ; Melecký, Martin (referee)
The dissertation consists of three papers which apply Bayesian econometric techniques to monitoring macroeconomic and macro-financial developments in the economy. Its aim is to illustrate how Bayesian methods can be employed in standard areas of economic research (estimating systemic risk in the banking sectors, nowcasting GDP growth) and also in a more original area (monitoring developments in sovereign bond markets). In the first essay, we address a task which analytical departments in central banks or commercial banks face very often - nowcasting foreign demand of a small open economy. On the example of the Czech economy, we propose an approach to nowcast foreign GDP growth rates for the Czech economy. For presentation purposes, we focus on three major trading partners: Germany, Slovakia and France. We opt for a simple method which is very general and which has proved successful in the literature: the method based on bridge equation models. A battery of models is evaluated based on a pseudo-real- time forecasting exercise. The results for Germany and France suggest that the models are more successful at backcasting, nowcasting and forecasting than the naive random walk benchmark model. At the same time, the various models considered are more or less successful depending on the forecast horizon....
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Modelling Duration of Financial Transaction Data
Nácovský, Patrik ; Hendrych, Radek (advisor) ; Branda, Martin (referee)
This bachelor thesis deals with ACD (autoregressive conditional duration) model, which is used to estimate durations of time series of financial transaction data. First, duration and time series are defined formally as well as with the intuitive way. Next, model ACD itself is defined and its basic types, which are determined with distribution of its residuals. Then way to use this model for predictions is introduced. In the second part, steps for model identification, construction and revision are described. In the last part models EACD, WACD and GACD are constructed for real data. There are three data sets of thick data, which are Apple stocks, EUR/USD and gold. Data sets contain from 300 thousands to 600 thousands elements (one trading week).
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Normalization of Time Series Data of Landsat
Svoboda, Jan ; Štych, Přemysl (advisor) ; Kolář, Jan (referee)
Spectral reflectance of the Earth surface, obtained from the satellite images, should be independent from the external influences and should reflect the surface properties, specifically the proportion of the radiance reflected from the object. It was proved in this paper that the time series of the 63 images from the Landsat 5 satellite were visibly influenced by the external factors even in the case of the images already atmospherically corrected. These external factors were age of the image and WRS-2 position from which the image was obtained. Age of the image was documented with the steady decrease of the spectral reflectance values of the invariant features, especially in the visible part of the electromagnetic spectrum, caused by the sensor degradation. The influence of the WRS-2 position was documented especially in the infrared bands. The western parts of the images are lighter (have higher values of the surface reflectance) than the eastern parts. That may cause the difference between values when monitoring one spot in two overlapping WRS-2 positions. The method originally used for the relative radiometric normalization IR-MAD was here applied to normalize the surface reflectance data, and resulted in the fact that these influences did not show up any more. In order to extend the time...
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