National Repository of Grey Literature 77 records found  previous4 - 13nextend  jump to record: Search took 0.00 seconds. 
Synchronization Signal Generator for Musical Applications
Vácha, Lukáš ; Přinosil, Jiří (referee) ; Schimmel, Jiří (advisor)
This bachelor thesis deals with analysis of MIDI´s protocol and especially by the implementation of synchronization methods. The aim of this thesis is suggestion and creating of a prototype of equipment, which transmits one sort of synchronization code and enables its direct extension on produced equipment. First part of solution of this thesis deals with MIDI in general, its history, hardware definition and protocol analysis, especially by analysis of methods and by types of synchronization. The main attention is devoted to MIDI Click/SPP code, which is also implemented in created prototype. The second part of this thesis deals with proposal of hardware solution of prototype, where is parsed a principle of involvement and used components in detail. The third part describes software proposal of solving from necessary initializations of microcontrollers up to operations with individual circumferences. There is also analyzed a customer service. Last, fourth part of thesis deals with realization of prototype, its revival and testing. There is stated value of properties of prototype, some suggestions for possible extension and summary of achieved aims of this thesis in the end.
Transition Periods and Long Memory Property
März, Jan ; Vácha, Lukáš (advisor) ; Polák, Petr (referee)
This thesis examines the relationship between the distribution of structural breaks within a data sample and the estimated parameter of long memory. We use Monte Carlo simulations to generate data from processes with specific values of parameters. Subsequently we join the data with various shifts to mean and examine how the estimates of the parameters vary from their true values. We have discovered that the overestimate of the long memory parameter is higher when the breaks are clustered together. It further increases when the signs of the shifts are positively correlated within the clusters while negative correlation reduces the bias. Our findings enable the improvement of robustness of estimators against the presence structural breaks. Powered by TCPDF (www.tcpdf.org)
Geopolitical risk and financial markets: trends, co-movements and effects
Jarina, Vesna ; Horváth, Roman (advisor) ; Vácha, Lukáš (referee)
This thesis explores the impact of geopolitical risk on cross-market co-movements in both global stock markets and regional foreign exchange markets over the period of 1995-2023. Employing two novel approaches, namely the return co- exceedances within the quantile regression framework and the GDCCX-GARCH model, our findings reveal that geopolitical risk has a tendency to weaken ex- treme return co-exceedances and dynamic conditional correlations within these markets, although there are few exceptions from this behaviour. Additionally, we emphasize the significance of considering geopolitical risk when building portfolio strategies by providing evidence for gold's hedging and safe haven properties, the resilience of clean energy investments, and the rise in crude oil prices in response to heightened geopolitical risk.
Effect of covered calls on portfolio performance
Ježo, Tomáš ; Polák, Petr (advisor) ; Vácha, Lukáš (referee)
This thesis aims to evaluate the performance of a covered call strategy writ- ten on Exchange-traded funds compared to a buy-and-hold strategy of the Exchange-traded fund on the US stock market. The strategy is constructed us- ing at-the-money, two-percent and five-percent out-of-the-money call options. The premium for the former is taken from historical market data and for the latter two calculated using the Black-Scholes-Merton formula adjusted for div- idends. The results further provide a two-period distinction to better account for di erent market periods, namely Covid-19 and the geopolitical conflict in Ukraine. The results fail to show evidence of a significant di erence between a covered call strategy and the buy-and-hold strategy. However, we provide possible applications of the strategy in certain market settings. The perfor- mance is evaluated on the basis of annualized returns and standard deviation, as ratios based on the mean-variance framework are omitted due to possible bias of negatively skewed distribution of returns of the covered call strategy. JEL Classification G10, G11, G12, G13, C02 Keywords Covered calls, ETF, Black-Scholes model, Op- tions pricing, Portfolio performance Title E ect of covered calls on portfolio performance
Predicting stock price movements from financial news using deep neural networks
Kramoliš, Richard ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Financial media are an important source of information and many articles about companies and stocks are released every day. This thesis assesses the informa- tion value of the articles and utilizes these articles for the stock price move- ment prediction task. For this purpose, models with transformer architecture are used, specifically Bidirectional Encoder Representations from Transform- ers. These models are able to process the text data and create the contextual representation of the text sequence. After adding the classification layer, the models are applied for the stock price movement predictions. The thesis evalu- ates multiple models including different techniques and parameters to find the best performing model. It focuses on two data filters that are expected to de- crease the noise in the data. Moreover, it introduces a new method to recognize the company of interest. As a result of the hyperparameter optimization, the final model is constructed. JEL Classification C45, C51, C52, C53, G11, G14, G17 Keywords BERT, Transformer, Financial Articles, Stock Trading Title Predicting stock price movements from financial news using deep neural networks
Price Prediction Using Machine Learning Methods on the European Market of Used Cars
Dvořáček, Petr ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
This master's thesis proposes accurate predictions of the prices of used cars. It builds its fundamentals on the available research and broadens the academic literature by applying several modern techniques to the European market. Us- ing machine learning models and unique data, high accuracy of predictions was obtained. The precise prediction of the residual value of a used car might benefit both the buyers and the sellers, and also reduce market inefficiencies. We are not aware of any similar work in the particular field focusing on the European market. An application programming interface (API) was exploited in order to col- lect the data. Therefore, a large set of data consisting of 221,704 used car classifieds was gathered and used in various models (MLR, PCR, LASSO, De- cision Tree, Random Forests, and ANNs). This study aims to find the most precise model for estimating the prices of used cars with the help of several performance statistics (R2, RMSE, and MAE). We support the available lit- erature as the random forest approach provided the highest accuracy when predicting the used car prices. A model using ANNs seemed to be the second best in terms of predictive performances, however, required comparably much more computing power. The effects of various attributes of used vehicles on their...
Machine Learning Methods in Payment Card Fraud Detection
Sinčák, Jan ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Protection of clients from fraudulent transactions is a complicated task. Banks tend to rely on rule-based systems which require manual creation of rules to identify fraud. These rules have to be set up by employees of the bank who need to look for any trends in fraudulent transactions themselves. This thesis deals with the problem of detection of fraudulent card transactions as it com- pares multiple machine learning models for fraud detection. These models can find complex relationships in the data and potentially outperform standard fraud detection systems, Logistic regression, neural network, random forest, and extreme gradient boosting (XGBoost) models are trained on a simulated dataset that closely follows properties of real card transactions. Performance of the models is measured by sensitivity, specificity, precision, AUC, and time to predict on the testing dataset. XGBoost shows the highest performance among the tested models. It is then compared to a standard fraud detection system used in a Czech bank. The bank system achieves higher specificity but XGBoost still shows promising performance. It is possible that certain machine learning models could outperform today's fraud detection systems if they are well-tuned. JEL Classification G21, K42 Keywords machine learning, card fraud, fraud...
Team cooperation in the activity of a construction manager
Vácha, Lukáš ; Hubner, Jan (referee) ; Linkeschová, Dana (advisor)
The bachelor's thesis Teamwork in the activity of a construction manager focuses on the role of construction managers in developing working teams and teamwork in the construction industry. The thesis defines the theoretical foundations of this issue, including the specifics of the construction enterprise, team theory, management theory, and current trends in management. The second part of the thesis presents the results of a survey conducted among employees in the construction sector and controlled interviews with management staff of construction companies. This part examines the impact of the work of a construction manager on their team and seeks ways to support team collaboration in construction companies. This thesis contributes to the understanding of the importance of team collaboration in the construction industry and provides recommendations for construction managers on how to enhance teamwork within their teams.
Three Essays on Data-Driven Methods in Asset Pricing and Forecasting
Gregor, Barbora ; Baruník, Jozef (advisor) ; Chen, Cathy Yi-Hsuan (referee) ; Baumohl, Eduard (referee) ; Vácha, Lukáš (referee)
This dissertation thesis consists of three papers focusing on applications of data-driven methods in asset pricing and forecasting. In the first paper, we decompose the term structure of crude oil futures prices using dynamic Nelson-Siegel model and propose to forecast them with the generalized regression framework based on neural networks. We find the neural networks to produce significantly more accurate forecasts as compared to several benchmark models. The second paper demonstrates how time-varying coefficients model can help to explore dynamics in risk-return trade-off on sovereign bond market across entire term structure. Our extensive 12-year dataset of high-frequency data of U.S. and German sovereign bond prices of 2-year, 5-year, 10-year and 30-year tenors allows us to construct realized measures of risk as well as exploring risk-return relationship under various market conditions. In addition to realized volatility, we find realized kurtosis to be priced in bond returns. Importantly, we detect the risk factor captured by realized kurtosis to have positive effect on returns in crisis turning to negative values in calm periods. In the third paper, we use time- varying coefficients methodology and higher realized moments in bond volatility forecasting challenging the HAR model. We detect realized...
Climate Change Risk Premium, Stock Returns and Volatility Analysis in Relation to ESG Score
Barotov, Timur ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The purpose of this study is to provide the evidence in regards to how the ESG score integration in the investment strategies affects the stock portfolio performances. The 10 year long panel data on European stocks were used to test how does the corporate ESG score correlate with returns and volatility on corporate stocks and does it (if at all) hold any explanatory power if added to popularly used asset pricing models. Data sample was divided in two based on long and short ESG reporting periods, where on each the analysis was performed separately. Furthermore, both the single sort and double sort analyses were performed to isolate size and ESG effects. Using Fama-MacBeth regression the results seem to suggest that investors are already pricing in the climate related risks as shown by the negative risk premium associated with high ESG firms. Returns and volatility of corporate stocks tend to be lower with higher ESG score, although not uniformly nor very significantly. Comparing Leaders portfolio showed that high (European) ESG scorers underperfomed S&P 500 index both in terms of return and volatility.

National Repository of Grey Literature : 77 records found   previous4 - 13nextend  jump to record:
See also: similar author names
2 VÁCHA, Ladislav
Interested in being notified about new results for this query?
Subscribe to the RSS feed.