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Financial crowdfunding from the perspective of Czech and British legal regulation
Dubšíková, Radka ; Sejkora, Tomáš (advisor) ; Kotáb, Petr (referee)
Financial crowdfunding is not regulated by any special law in the Czech Republic. The aim of this thesis is to appraise which current legislation in the area of financial law is applicable to provision of financial crowdfunding and whether the legislation is sufficient. Opposite to that, the British regulation of financial crowdfunding is comprehensive and it might serve as an example to the potential future Czech regulation. Therefore, the thesis presents some aspects of the British legal regulation. The first part of the thesis defines the concept of crowdfunding, its types, development and some possible business models of crowdfunding platforms. Crowdfunding is subdivided into several types by theory, thus it is appropriate for the purpose of the thesis to describe which types fall under the term financial crowdfunding. The second part of the thesis focuses on existing Czech legislation covering provision of financial services which could be either relevant to financial crowdfunding. This section dedicates to the Act on Payments, the Act on Consumer Credit, the Act on Capital Market Business and the Act on Management Companies and Investment Funds. In the third part of the thesis the basics elements of the British legal regulation are outlined. The emphasis is placed primarily on P2P lending due...
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Impact of Regional Differences on P2P Lending, Evidence from China
Liang, Na ; Pečená, Magda (advisor) ; Semerák, Vilém (referee)
Taking the representative P2P lending platform Renrendai as an example, this paper focuses on the impact of borrower's region on the behavior of lenders and borrowers in the market. According to the Chinese six geographical regions the borrowers are from, this paper empirically analyzes the difference of success rate of borrowing and default rate in the six regions using the binary logistic regression model and further studies the reasons behind the regional difference. The result shows that the impact of regional difference is significant and the borrower from northern China are more likely to fund successfully, but the impact of regional difference on the default rate is insignificant, and the economic, financial and education development level in regions have a significant impact on the success rate of borrowing. This paper studies the regulatory differences of P2P platforms in various regions of China, the result shows that eastern China, central and southern China, and Beijing (in northern China) have paid more attention and importance to the regulation of P2P platforms. Keywords ： China; P2P lending; the success rate of borrowing; regional difference; regulation policy
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Portfolio optimization for an P2P investor on Zonky
Jonáš, Filip ; Polák, Petr (advisor) ; Máková, Barbora (referee)
This thesis analyzes the Czech peer-to-peer lending platform Zonky. The goal was to find the optimal portfolio for a risk-averse investor investing in Zonky loans. For this purpose, the Modern portfolio theory from Markowitz was used. Based on the provided loan book containing information about loans which Zonky has provided since its foundation we examined the statistical properties of the individual risk categories represented by the interest rate charged. The optimization was done using the Excel Solver tool assuming that the loan categories are uncorrelated as well as considering the correlation we found using the variance- covariance matrix. For both cases, the portfolio minimizing the standard deviation as well as the portfolio which maximizes the Sharpe ratio was found. Generally, both types of portfolios were comprised mainly of loans with lower interest rate. According to our results, it seems that such loans offer better relationship between risk and return compared to categories which are riskier. Also, we showed that the platform's recovery rate has a significant impact on the performance of the loan categories especially of those which are among the riskiest. Furthermore, we demonstrated that the correlation between individual risk categories should not be ignored when a portfolio...
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Credit Risk of P2P Lending on the Czech Market
Čermáková, Jolana ; Dědek, Oldřich (advisor) ; Čech, František (referee)
This thesis analyzes an emerging peer-to-peer lending industry, while intro- ducing its main features and risks, where the risk of default and its moder- ation gets the most attention. Uniquely provided data from the front Czech platform Zonky containing nearly 6 000 observations serve as a baseline for credit risk modeling. It has been investigated which variables have the largest effect on default on the Czech P2P market. The final model is used to predict the associated probability of default and to compute the credit score for potential borrowers using these online platforms. Results support the fact that education, age, way of living, expenses, marital and employment status, income and the number of children are significant variables when determining the risk of default. Many of these findings are in accordance with previous international papers published on this topic.
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Sharing economy as an innovative exchange instrument in the area of financial services in the context of an ongoing digital revolution in the Czech Republic in 2016
Demyanenko, Sergey ; Barák, Vladimír (advisor) ; Brabec, Petr (referee)
The bachelor thesis examines the issue of so called sharing economy and its impact on consumer behavior of individuals in the Czech Republic. The theoretical part of thesis defines the historical, social and economic context, that enabled the emergence of this modern exchange model and provides an overview of the financial services in the sharing economy operating on the Czech market. The empirical part presented in the form of a questionnaire survey reveals the main benefits of services of the sharing economy, defines the perception of financial services of collaborative consumption by a specific group of population of the Czech Republic and examines the potential development of this model as a whole. The results of the research indicate that, notwithstanding the relatively low level of trust in the collaborative consumption services and quite limited familiarity with the financial services of the sharing economy, the future positive development of this exchange model, which under the right conditions will gain significant national economic significance, may be expected.
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Analýza trhu P2P půjček
Rolák, Martin ; Radová, Jarmila (advisor) ; Rajl, Jiří (referee)
The subject of this thesis is to map the development of p2p lending in the US. Firstly, we compare the different business models and roles in the current monetary system of commercial banks and p2p platform lenders. The structure as well as trends of p2p lending industry are described. The returns of p2p loans are compared with traditional assets such as bonds, stocks and commodities in the 2011-2016 period. The last part of the thesis examines the loanbook of the most prominent p2p platform lender, Lending Club.
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Machine Learning Methods for Credit Risk Modelling
Drábek, Matěj ; Witzany, Jiří (advisor) ; Málek, Jiří (referee)
This master's thesis is divided into three parts. In the first part I described P2P lending, its characteristics, basic concepts and practical implications. I also compared P2P market in the Czech Republic, UK and USA. The second part consists of theoretical basics for chosen methods of machine learning, which are naive bayes classifier, classification tree, random forest and logistic regression. I also described methods to evaluate the quality of classification models listed above. The third part is a practical one and shows the complete workflow of creating classification model, from data preparation to evaluation of model.
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Performance Analysis of Credit Scoring Models on Lending Club Data
Polena, Michal ; Teplý, Petr (advisor) ; Pečená, Magda (referee)
In our master thesis, we compare ten classification algorithms for credit scor- ing. Their prediction performances are measured by six different classification performance measurements. We use a unique P2P lending data set with more than 200,000 records and 23 variables for our classifiers comparison. This data set comes from Lending Club, the biggest P2P lending platform in the United States. Logistic regression, Artificial neural network, and Linear discriminant analysis are the best three classifiers according to our results. Random forest ranks as the fifth best classifier. On the other hand, Classification and regression tree and k-Nearest neighbors are ranked as the worse classifiers in our ranking. 1
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P2P lending
Dobiasová, Dana ; Bártová, Hana (advisor) ; Vacek, Vladislav (referee)
The aim of this bachelors thesis is to determine the effect of P2P lending on the economic indicators and the status of small and medium enterprises in the United States, specifically for the period form 2011 to 2015. To better understand the practical part, two first chapters will be focused on defining the concept of P2P lending and analysing the current situation in the United States. Besides that, it is also an emphasis on regulatory requirements which are newly starting to appear. This knowledge is later used in practical part, which analyse the state of SMEs and their way of financing, which makes them able to safeguard its economic growth and offer new job opportunities. All the essential facts, identified by market analysis, were described and evaluate in the overall summary.
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