National Repository of Grey Literature 137 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Liquidity and Predictability of Cryptoassets
Mjartanová, Viktória ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
The relationship between liquidity and return predictability may be an im- portant aspect to consider when investing in cryptoassets. We examine this relation using both cross-sectional as well as panel data. First, we calculate a set of predictability measures and aggregate the results into four variables. We then regress the predictability variables on a set of controls and two measures of liquidity, specifically the Amihud illiquidity ratio and the Corwin-Schultz spread estimate. The other independent variables include the logarithm of volume, turnover ratio and Garman-Klass volatility. Results from the cross- sectional analysis indicate that liquidity negatively impacts the degree of return predictability. Moreover, findings from a subset of panel data, including only 50 cryptoassets with the largest market capitalization, provide some evidence in favor of this relationship. Results from full panel data, however, present contradictory evidence. For these regressions, liquidity is found to be either in- significant or to possess a positive impact on the degree of return predictability. Altogether, we obtain mixed evidence about the effect of cryptoasset liquidity on return predictability. JEL Classification C53, C58, G14 Keywords Cryptoassets, Predictability, Liquidity, Panel data Title...
Using spin model to determine FTTx connectivity market potential in the Czech Republic
Munduch, Pavel ; Krištoufek, Ladislav (advisor) ; Červinka, Michal (referee)
The our thesis "Using spin model to determine FTTx connectivity market po- tential in the Czech Republic", we firstly map the current landscape of the Czech broadband technology market. Additionally, we present an overview of Ising model's interdisciplinary applications. Afterwards, we describe the dy- namics of the Ising model and in particular we study the convergence tendencies of Ising model generated series as well as the spin positioning in the Ising model lattices based on the input parameters. Consequently, we assume the spins in the model to represent the fiber tech- nology and alternative technology and thus we link the Ising model, its parame- ters and outputs to the problem of fiber connectivity potential. Apart from the standard input parameters of the Ising model, we also introduce variability in terms of the distribution of the initial lattice and we define four archetypes to represent real market situations. Ultimately, we describe the sets of parameters for which the market appears to have the most potential of fiber deployment. JEL Classification A12, C6, C15 Keywords Ising model, econophysics, fiber technology, broadband connection Title Using spin model to determine FTTx connectiv- ity market potential in the Czech Republic
Multi-horizon equity returns predictability via machine learning
Nechvátalová, Lenka ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictabil- ity of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, be- fore and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting hori- zon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reduc- ing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S. JEL Classification G11, G12, G15, C55 Keywords Machine learning, asset pricing, horizon pre- dictability, anomalies Title Multi-horizon equity returns predictability via machine learning
A Meta-Analytic Approach to Gender Pay Gap: A Case of Discrimination?
Konstantinova, Elizaveta ; Krištoufek, Ladislav (advisor) ; Pertold-Gebicka, Barbara (referee)
1 Abstract The phenomenon of the gender pay gap has now been vastly examined for sev- eral decades and the research to date is extensive and diverse. In the thesis at hand, we accumulate, review and summarize empirical findings across the recent literature on the gender pay gap by means of meta-regression analysis. Our sample covers almost 20 years of the latest research and 35 developed coun- tries. The meta-regression results explain almost 90 percent of the study-to- study variation and suggest that the largest impact on the estimated adjusted gender pay gap results from not controlling for hours worked. Furthermore, the publication status of the original study is shown to have a large downward impact on the estimated gender pay gap. Conversely, the empirical findings suggest that a study affiliated with a certain institution promoting feminism or gender equality tend to overestimate the adjusted gender pay gap. JEL Classification J16, J31, J71 Keywords gender pay gap, discrimination Title A Meta-Analytic Approach to Gender Pay Gap: A Case of Discrimination?
Dynamics of Bitcoin mining profitability and its break-even electricity costs
Pěnkavová, Markéta ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
The aim of this thesis is to investigate the Bitcoin mining profitability throughout the years 2014 to 2020 with the focus on the year 2020. The analysis is based on the break-even electricity price estimates which are obtained by using a set of variables entering the Bitcoin mining process such as the block reward, transaction fees, network hash rate or power consumption. The calculations are performed under the assumption that miner owns the most efficient mining hardware available at the time while disregarding the original investments in the necessary hardware. To further examine the relationship between the estimated break-even electricity price and the Bitcoin market price the cointegration analysis is performed employing a vector error correction model as the series seem to be nonstationary. The final results illustrate the substantial effect the Bitcoin market price has on the break-even electricity price estimates to the extent that there is rather long-term reaction in the break-even electricity price values to the shocks in the Bitcoin market price. The findings from the research offer insights to the Bitcoin mining process suggesting that an access to extremely low electricity prices is needed to earn any profits from the Bitcoin mining activity in 2020.
Predicting purchasing intent on ecommerce websites
Vařeka, Marek ; Krištoufek, Ladislav (advisor) ; Baruník, Jozef (referee)
This thesis analyzes behavior of customers on an e-commerce website in order to predict whether the customer is willing to buy something or is just window shopping. In addition the secondary model predicts, if the customer is going to leave the e-commerce website in next few clicks. To answer this questions different frameworks are tested. The base model used is the Logit model. The base model is compared with more sophisticated methods in machine learning - with neural networks. The best results were yielded by Recurrent neural network - the Long Short-Term Memory (LSTM). The results of the analysis confirm importance of the click stream data and calculated features that track user behavior on the e-commerce website, type of the page (product, category, information), product variance and category variance. The thesis emphasizes practical implications of this models. Two possible practical implementations are presented. The models are tested in novel ways to see how would they perform if implemented on the real e-commerce website.
The future of credit scoring modelling using advanced techniques
Čermáková, Jolana ; Krištoufek, Ladislav (advisor) ; Geršl, Adam (referee)
Machine learning is becoming a part of everyday life and has an indisputable impact across large array of industries. In the financial industry, this impact lies particularly in predictive modelling. The goal of this thesis is to describe the basic principles of artificial intelligence and its subset, machine learning. The most widely used machine learning techniques are outlined both in a theoretical and a practical way. As a result, four models were assembled within the thesis. Results and limitations of each model were discussed and these models were also mutually compared based on their individual per- formance. The evaluation was executed on a real world dataset, provided by Home Credit company. Final performance of machine learning methods, measured by the KS and GINI metrics, was either very comparable or even worse than the performance of a traditional logistic regression. Still, the problem may lie in an insu cient dataset, in the improper data prepara- tion, or in inappropriately used algorithms, not necessarily in the models themselves.
Fundamental Analysis and Stock Return: The Case of Big Tech
Tran Nguyen, Thai Nhat Phi ; Krištoufek, Ladislav (advisor) ; Máková, Barbora (referee)
Bibliographic note TRAN NGUYEN, Thai Nhat Phi. Fundamental Analysis and Stock Return: The Case Of Big Tech. Prague 2020. 102 pp. Bachelor thesis (Bc.) Charles University, Faculty of Social Sciences, Institute of Economic Studies. Thesis supervisor doc. PhDr. Ladislav Krištoufek Ph.D. Abstract Six out of the ten most valuable companies by market capitalisation are, at their core, technology companies and four of these have at some time crossed the $1 trillion market cap, which has ignited a public discussion regarding their astronomic valuations and the tech bubble. This work addresses this development, with the analysis of four companies, namely Google, Apple, Facebook and Amazon (GAFA), which have dominated their respective fields of business in the "new economy". We go beyond the stock analysis and also examine the company's fundamentals and their effect on the valuations, furthermore we fuse the insights of both analyses to offer a more comprehensive evaluation of these four companies. The results suggest that their stock value accurately portrays their market dominance and that it is deeply rooted in the companies' fundamentals which are fairly well reflected in the stock price movements. Ultimately, we find that these companies do not contribute to the tech bubble as GAFA show unparalleled financial...
Capturing the Effects of Renewable Resources on Electricity Prices: Evidence from the Czech Republic
Zítek, Jan ; Krištoufek, Ladislav (advisor) ; Herman, Dominik (referee)
In this thesis, we investigate the impact of intermittent renewable energy sources on the level and volatility of the Czech electricity spot prices dur- ing the period from 2015 to 2019. The analysis is warranted due to the obligations of the member states of the European Union to augment the share of clean energy in the gross final energy consumption by 2030. The technique applied in the empirical part concerns univariate GARCH-class models (namely, plain vanilla and exponential) which are extended with additional explanatory variables in the form of total load, solar and wind power generations. By constructing daily, peak and off-peak indices from the dataset comprised of hourly observations, we establish a comparative framework throughout the text. More specifically, this approach allows us to contrast price dynamics under the regimes of high and low demand for electricity as well as to explore the patterns of solar and wind production. The findings indicate that both Czech solar and wind power sources induce the so-called merit order effect. In contrast, once the volatility of electric- ity prices is taken into account, the examined sources of energy behave in a different manner. Owing to the daily index, while solar power generation decreases the volatility of electricity prices, the opposite...

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