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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.
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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...
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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.
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Stock Trading Using a Deep Reinforcement Learning and Text Analysis
Benk, Dominik ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The thesis focuses on exploiting imperfections on the stock market by utilizing state-of-the-art learning methods and applying them to algorithmic trading. The automated decisions are expected to have the capability of outperforming professional traders by considering much more information, reacting almost instantly and being unaffected by emotions. As an alternative to traditional supervised learning, the proposed model of reinforcement learning employs a principle of trial-and-error, which is essential for learning behaviours of all organisms. In the context of stocks, this allows to consider the involved uncer- tainty and therefore more precisely estimate the long-run returns. To collect the most relevant information for each trading decision, additionally to tech- nical indicators the models build on investor's opinion - financial sentiment. This is derived from two textual sources, news and social media, and the main goal is to compare their relative contribution to trading. Models are applied to 11 different stocks and later combined into portfolio for greater robustness of results. The textual analysis proves to be important for the learning process, especially in case of stocks with good media coverage. The Twitter is found to provide more valuable information compared to news, but their...
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Momentum trading strategy performance before, during, and after the COVID-19 crisis
Řeřicha, Dávid ; Fanta, Nicolas (advisor) ; Vácha, Lukáš (referee)
This thesis investigates the well-known momentum trading strategy from January 2013 to May 2022 on the US stock market. The goal of this thesis is to examine whether the phenomenal momentum anomalies occurred during COVID-19 crisis. The main part is addressed to the creation of momentum portfolios from the whole US stock market using daily data from 500 firms in the S&P 500 index and additional 11 sectoral momentum portfolios. Results confirm the power of momentum portfolios as the past winners accumulated the highest returns over the whole observed period and clearly outperformed the market. Focusing closely on COVID- 19 period we observed past losers outperforming past winners, which confirms another momentum anomaly on the US stock market. Therefore, this thesis referred to the Carhart Four - Factor Model model that is based on the Fama-French Three - Factor model with additional momentum factor. Unfortunately, results indicate no statistically significant power to explain the momentum behaviour during COVID-19 crisis.
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Price gaps in the stock market
Vosmanský, Jakub ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
This thesis aims to scrutinise price gaps in the stock market. The key objective is to analyse candlestick charts surrounding price gaps and determine whether any patterns accompany their presence. Firstly, the thesis briefly describes candlestick patterns, literature relevant to price gaps and Convolutional Neural Network (CNN) as the method of choice. Price gaps are studied in a 5-minute time frame in the data of all S&P 500 constituents in the years from 2015 to 2021. By feeding images of the candlestick chart into the CNN, the proposed model reaches an Accuracy of 74.2% in predicting whether a future price will be higher or lower than the price at the gap. This result can be translated into a statement that the CNN detects hidden patterns around the price gaps. Furthermore, the thesis finds that these patterns di er across individual stocks. The thesis also shows that including news sentiment in the analysis does not improve the ability to discover patterns. JEL Classification C45, C55, C88, G14, G15, G41 Keywords price gap, convolutional neural network, pattern detection, news sentiment Title Price gaps in the stock market
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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Low Interest Rates and Asset Price Fluctuations: Empirical Evidence
Ali, Bano ; Horváth, Roman (advisor) ; Vácha, Lukáš (referee)
The thesis focuses on estimating the effect of expansionary monetary policy concerning asset prices, specifically house and stock prices as they are of pri- mary importance in financial markets. A structural vector autoregressive model is used including data for the Euro Area, the United Kingdom, and the United States from 2007 to 2017. Moreover, instead of short-term nominal interest rate, the shadow policy rate is used to measure the stance of both conventional and unconventional monetary policy. It is useful when policy rates of central banks are at or near zero as it neglects the zero-lower bound. Using both impulse response functions and forecast error variance decomposition, results suggest that higher interest rates are indeed associated with lower asset prices. That is confirmed by including two different estimates of shadow rates into the model and observing the effect for two specific types of assets. More precisely, house prices react almost immediately showing the most substantial decrease for the United Kingdom, while stock prices slightly increase at first and de- crease afterward with similar size of the effect for all areas under consideration. Finally, the discussion of how the monetary authority should react to asset price fluctuations is provided, summarizing the vast amount of literature...
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Time-scale analysis of sovereign bonds market co-movement in the EU
Šmolík, Filip ; Vácha, Lukáš (advisor) ; Krištoufek, Ladislav (referee)
The thesis analyses co-movement of 10Y sovereign bond yields of 11 EU mem- bers (Greece, Spain, Portugal, Italy, France, Germany, Netherlands, Great Britain, Belgium, Sweden and Denmark) divided into the three groups (the Core of the Eurozone, the Periphery of the Eurozone, the states outside the Eurozone). In the center of attention are changes of co-movement in the crisis period, especially near the two significant dates - the fall of Lehman Brothers (15.9.2008) and the day, when increase of Greek public deficit was announced (20.10.2009). Main contribution of the thesis is usage of alternative methodol- ogy - wavelet transformation. It allows to research how co-movement changes across scales (frequencies) and through time. Wavelet coherence is used as well as wavelet bivariate and multiple correlation. The thesis brings three main findings: (1) co-movement significantly decreased in the crisis period, but the results differ in the groups, (2) co-movement significantly differs across scales, but its heterogeneity decreased in the crisis period, (3) near to the examined dates sharp and significant decrease of wavelet correlation was observable across lower scales in some states. JEL Classification C32, C49, C58, H63 Keywords Co-movement, Wavelet Transformation, Sovereign Debt Crisis, Sovereign Bond Yields,...
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