|
|
Analysis of Economic Indicators Using Statistical Methods
Ivachshenko, Olga ; Šustrová, Tereza (referee) ; Novotná, Veronika (advisor)
The bachelor thesis focuses on the financial analysis of ABB s. r. o company by using statistical methods. The thesis consists of two main parts. The main purpose of the first part is the describing of individually economic indicators of the company and regressive analysis with the time series. The second part includes the financial analysis and the prediction of further development for the company. As the conclusion of these two parts, there is suggested recommendations for improving the financial situation in the future.
|
|
| |
|
|
Corporate Performance Assessment using Maple Software
Kopečná, Barbora ; Hanušová, Helena (referee) ; Chvátalová, Zuzana (advisor)
The diploma thesis focuses on the evaluation of the performance of a selected company operating in the food industry in the period 2013-2018. The thesis uses PEST analysis, regression analysis and time series analysis in Maple software and benchmarking. Using these tools and the results of analyses, proposals are made to improve the company's performance and also future potential development of the company is descripted.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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
|
guest ::
