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Assessing Selected Indicators Using Statistical Methods
Maulová, Kristýna ; Součková, Markéta (referee) ; Doubravský, Karel (advisor)
This bachelor thesis deals with an evaluation of the financial situation of the company 2G - spol. s r.o. - Přikrývky a polštáře using statistical methods. The first part contains theoretical starting points of the thesis, which define the necessary terms of financial analysis and statistical methods, such as the method of regression analysis and time series. In the second part of the thesis, this theory is applied in practice to the mentioned company, where selected financial indicators are analyzed and prediction of future development of these indicators is determined. On the basis of this analysis and evaluation, subsequently, they are submitted own proposals to improve the current state of the selected company.
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Mobile App with Predictions of e-Sports Matches
Věčorek, David ; Szentandrási, István (referee) ; Herout, Adam (advisor)
E-Sports, also known as progaming (professional gaming) has grown a lot in the last few years. Professional gamers are regularly attending tournaments watched by hundreds of thousands of fans and with prize pools of millions of dollars. There are many video broadcasts of those events and recently betting on e-Sports has also become available. The main goal of this thesis was to create a mobile app for OS Android, which aims to utilize this growth and create a service of providing predictions of results of the e-Sports matches, similar to that existing in regular sports. The application in its current form receives the predictions via Google Cloud Messaging service and shows an Android notification on their arrival. The predictions are then stored on the device into SQLite database so they are available for further view and filtering. After the matches are finished, their results are shown in comparison to the predictions and balance of the predictions is calculated. Users can display information about their subscriptions and predictions under that subscriptions. The app was created in Android Studio IDE with appearance based on the material design guidelines. The app was tested on several devices of different brand and Android version, then it was placed on Google Play for open beta testing. In the future the app will be offered to the users of the service of providing predictions of results of the e-Sports matches.
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Assessing Economic Situation of a Company and Proposals for Its Improvement
Votápková, Anna ; Rochla, Jiří (referee) ; Doubravský, Karel (advisor)
This master thesis focuses on the assessment of the economic situation of the company using methods of financial analysis and statistical methods. The first of the three main parts of the thesis describes the basic theoretical background, especially issues related to financial analysis, time series, regression and correlation analysis. In the second part, the company is analyzed with the help of selected financial indicators, some of which are subjected to subsequent statistical analysis to predict possible future developments and to determine the relationships between the indicators. The outputs serve as a basis for the third part of the work, which contains several proposals that should lead to the improvement of the current situation in the selected company.
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Big Data Processing from Large IoT Networks
Benkő, Krisztián ; Podivínský, Jakub (referee) ; Krčma, Martin (advisor)
The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.
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Information and Cyber Threats in 2019
Bača, Jonatán ; MSc, Michal Mezera (referee) ; Sedlák, Petr (advisor)
Diploma thesis focuses on information and cyber threats in 2019. It comprises theoretical basis for better understanding of the issue. Afterward the thesis describes the analysis of the current situation which combined several analyses primarily aimed on Czech companies. In the last part draft measures is created which contain predictions and preventive actions and recommendations for companies.
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Assessing Selected Indicators Using Statistical Methods
Vašková, Helena ; Blaháček, Libor (referee) ; Doubravský, Karel (advisor)
This bachelor thesis deals with an evaluation of economic situation of a company Vašíček – bakery and confectionery, Ltd. Provided data are processed by selected financial analysis indicators, time series and regression analysis. Applying regression analysis, the text provides predictions for selected indicators in the following two years. Furthermore, there is computational and graphical comparison with the performance of a competitor. At the end of the thesis, I provide my own improvement proposals for the company’s current state using the previously ascertained data and its analysis.
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Inflation: Predictive Power of Google Trends Data
Suchánek, Jan ; Stráský, Josef (advisor) ; Holub, Tomáš (referee)
This thesis explores the utility of Google Trends data in enhancing predictive power accuracy of ARIMA models for forecasting inflation in the Czech republic. The research was structured to address two core hypotheses: the rationality of inflation expectations as reflected in Google Trends search queries and the ability of the data to augment the predictive power of traditional inflation forecasting models. Our findings indicate that Google Trends data, when incorporated as an ex- ternal regressor in ARIMA models, significantly improve the model's predictive accuracy, especially in periods characterized by high inflation rates and eco- nomic turbulence. This provides evidence for the claim that Google Trends is able to effectively capture shifts in consumer sentiment and expectations. However, the study acknowledges limitations, including the specificity of the time domains analyzed and the exclusive focus on the Czech Republic. These factors may limit the generalizability of the results. In summary, this thesis contributes to the evolving field of economic fore- casting by demonstrating the value of integrating unconventional digital data sources like Google Trends into traditional econometric models. It opens av- enues for future research to explore the broader applicability of such data in...
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Assessing Economic Situation of a Company and Proposals for Its Improvement
Votápková, Anna ; Rochla, Jiří (referee) ; Doubravský, Karel (advisor)
This master thesis focuses on the assessment of the economic situation of the company using methods of financial analysis and statistical methods. The first of the three main parts of the thesis describes the basic theoretical background, especially issues related to financial analysis, time series, regression and correlation analysis. In the second part, the company is analyzed with the help of selected financial indicators, some of which are subjected to subsequent statistical analysis to predict possible future developments and to determine the relationships between the indicators. The outputs serve as a basis for the third part of the work, which contains several proposals that should lead to the improvement of the current situation in the selected company.
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Does LSTM neural network improve factor models' predictions of the European stock market?
Zelenka, Jiří ; Baruník, Jozef (advisor) ; Čech, František (referee)
This thesis wants to explore the forecasting potential of the multi-factor models to predict excess returns of the aggregated portfolio of the European stock mar- ket. These factors provided by Fama and French and Carhart are well-known in the field of asset pricing, we also add several financial and macroeconomic factors according to the literature. We establish a benchmark model of ARIMA and we compare the forecasting errors of OLS and the LSTM neural networks. Both models take the lagged excess returns and the inputs. We measure the performance with the root mean square error and mean absolute error. The results suggest that neural networks are in this particular task capable of bet- ter predictions given the same input as OLS but their forecasting error is not significantly lower according to the Diebold-Mariano test. JEL Classification C45, C53, C61, E37, G11, G15 Keywords Stocks, European market, Neural networks, LSTM, Factor Models, Fama-French, Predic- tions, RMSE Title Does LSTM neural network improve factor mod- els' predictions of the European stock market?
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