|
|
Evaluation of Contemporary art as an alternative investment
Havlovicová, Alice ; Vácha, Lukáš (advisor) ; Hronec, Martin (referee)
Bibliographic note HAVLOVICOVÁ, Alice. Evaluation of Contemporary art as an alternative in- vestment Prague 2020. 66 pp. Bachelor thesis (Bc.) Charles University, Faculty of Social Sciences, Institute of Economic Studies. Thesis supervisor Mgr. Lukáš Vácha, Ph.D. Abstract This thesis investigates the investment performance of contemporary art. In order to analyze the risks and returns of the unique art market environment, the reader is presented with the market specifics, trends, and inefficient. The financial performance of contemporary art is estimated by means of extended models of hedonic regression and repeat-sales regression. Both methods allowš for the treatment of volatility of the art market caused by the infrequency of trading, resulting in two monthly contemporary art market indices. The in- dices are estimated based on auction results of contemporary art spanning from 2003 to 2015, including all artworks sold at least once, which presents a general overview of the contemporary art market. In line with the academic literature on the topic of art investment, the results suggest lower returns of contemporary art than traditional financial assets. Volatility and Sharpe ratios differ in the two indices. Based on the resulting price indices, we conclude that contempo- rary art presents moderate returns...
|
|
|
Selection Bias Reduction in Credit Scoring Models
Ditrich, Josef ; Hebák, Petr (advisor) ; Pecáková, Iva (referee) ; Zamrazilová, Eva (referee)
Nowadays, the use of credit scoring models in the financial sector is a common practice. Credit scoring plays an important role in profitability and transparency of lending business. Given the high credit volumes, even a small improvement of discriminatory and predictive power of a credit scoring model may provide a substantial additional profit. Scoring models are applied on the through-the-door population, however, for creating them or adjusting already existing credit rules, it is usual to use only the data corresponding to accepted applicants for which payment discipline can be observed. This discrepancy can lead to reject bias (or selection bias in general). Methods trying to eliminate or reduce this phenomenon are known by the term reject inference. In general, these methods try to assess the behavior of rejected applicants or to obtain an additional information about them. In the dissertation thesis, I dealt with the enlargement method which is based on a random acceptance of applicants that would have been rejected. This method is not only time consuming but also expensive. Therefore I looked for the ways how to reduce the cost of acquiring additional information about rejected applicants. As a result, I have proposed a modification which I called the enlargement method with sorting variable. It was validated on real bank database with two possible sorting variables and the results were compared with the original version of the method. It was shown that both tested approaches can reduce its cost while retaining the accuracy of the scoring models.
|
guest ::
