National Repository of Grey Literature 131 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
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...
Fractality of Stock Markets: A Comparative Study
Krištoufek, Ladislav ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The main focus of the thesis is the introduction of new method for interpretation of fractality aspects of financial time series together with its application. We begin with description of various techniques of estimation of Hurst exponent - rescaled range, modified rescaled range and detrended fluctuation analysis. Further on, we present original theoretical results based on simulations of three mentioned procedures which have not been presented in literature yet. The results are then used in the new method of time-dependent Hurst exponent with confidence intervals developed in this thesis. Moreover, we show important advantage of using the mentioned techniques together to clearly distinguish between independent, trending, short-term dependent and long-term dependent properties of the time series. We eventually apply the proposed procedure on 13 different world stock indices and come to interesting results. To the author's best knowledge, the thesis presents the broadest application of timedependent Hurst exponent on stock indices yet.
Application of machine learning methods for estimating apartment prices in the Czech Republic
Nikodym, Jakub ; Krištoufek, Ladislav (advisor) ; Baruník, Jozef (referee)
In this thesis, we propose alternative ways to apartments' mass appraisal. This work enriches the current literature by combining several techniques of data extraction and price estimation. We are not aware of any similar work providing an in-depth overview of the Czech apartment market. Throughout the empirical analysis, five different methods (OLS, LASSO, decision tree, random forests, and kNN) are applied to the dataset of 15,848 classifieds. The aim of the study is to find the most accurate method of esti- mating offering prices, using structured variables as well as data extracted by text mining. We use various accuracy statistics and graphical analysis to vali- date our results. Tree-based methods, specifically the random forest algorithm, results with the highest accuracy in predicting offering prices. Additionally, text-based variables included in the model cause the reduction of errors on linear models. The last part of the analysis covers the main determinants of property value in Prague and the rest of the Czech Republic. We show that prices in Prague can be estimated with higher preciseness and with the lower number of independent variables.
Examining the Link between Financial Market Efficiency and Monetary Transmission Mechanism
Krejčí, Tadeáš ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
In an effort to examine role of capital markets' efficiency in transmission of monetary policy, 28 time series of market efficiency development are estimated with use of long-term memory and fractal dimension measures and a panel of 27 inflation targeting countries is constructed to run a random effect regres- sion. The cases of Czech Republic and Austria are thereafter more closely examined with use a vector-autoregressive and threshold vector-autoregressive frameworks on macroeconomic data spanning from 1996:Q3 to 2018:Q4. The evidence obtained through the conducted analyses support the hypothesis, that a more efficiently functioning capital market better contributes to monetary policy pass-through, or conversely, that high transaction costs, barriers to cap- ital market entry, or poor information availability may hinder the effects of central bank's monetary policy. JEL Classification F12, F21, F23, H25, H71, H87 Keywords capital market efficiency, inflation targeting, monetary transmission mechanism Author's e-mail teddy.krejci@gmail.com Supervisor's e-mail LK@fsv.cuni.cz
Pairs Trading in Cryptocurrency Markets
Fil, Miroslav ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
Pairs trading is a trading strategy which tries to exploit mean-reversion among prices of certain securities. It is market-neutral and self-financing, and has been shown to produce high excess returns in historical backtests. We employ the most common distance and cointegration approaches on cryp- tocurrency data from an exchange called Binance spanning the year 2018. The strategy is mostly unprofitable under transaction costs, but certain combinations of hyperparameters can perform well. Overall, the distance method performs far better, being able to achieve 3% monthly profit even in our baseline real-life con- ditions while the cointegration method always achieves only a slight loss. We also found that increasing the sampling frequency of the data from daily to hourly brings mixed results. Moreover, since we have to reuse estimates of real-life considerations from equity markets, it is unclear if our results are truly representative of the cryp- tocurrency market. The strategy is found to be very sensitive to execution diffi- culties and transaction costs, making their determination crucially important. It is somewhat easy to get returns in excess of 5% monthly under ideal conditions, but whether this could be achieved in real trading conditions is still unclear. Keywords pairs trading,...
Private Equity funds and their performance in the post-crisis period
Koníř, Štěpán ; Krištoufek, Ladislav (advisor) ; Kučera, Adam (referee)
The work covers the topic of private equity funds performance and attempt to identify the impact of macroeconomic conditions on the entire industry. The recent central banks' actions put a question about the impact of changes in interest rates on the private equity funds performance. With the sample of 100 observations provided by Cambridge Associates, we identified the significant negative effect of prevailing low interest rates on the growth of private equity funds performance. We further attempt to answer the question, whether private equity funds operating in post-crisis years has on average higher growth rate, however, we could not provide the answer as we failed to reject the null, neutral effect hypothesis. Additionally, with a sample of 3092 observations provided by Bloomberg, we found that the effect of cheap debt has increased on average in the postcrisis period, predicting that the private equity performance can suffer once the interest rates rises enough.

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