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Analysis and Aspects of Art as an Alternative Investment and its Price Determinants
Mozolikova, Alica ; Benáček, Vladimír (advisor) ; Moravcová, Hana (referee)
The purpose of this thesis is to examine the potential of art as an alternative investment. To do this, we start with a brief history of art market that is followed by an analysis of art market structure. We cover numerous risks affecting the behavior in the market subject to information asymmetry. As far as the feasibility of art as an investment is concerned, we identify different types of alternative investment and compare their potential. Despite high opacity of the art market, more data are being made available giving us an opportunity to present a clearer explanation of the current state of the art market. A question has to be asked, does the value of art solely depend on the subjectivity of human taste or on an opportunity to speculate? The subject of the detailed discussion are, both, internal and external factors, which influence the value of artworks, and hence the sale price. Using econometric analysis of auction records, we aim to uncover objective factors, which determine the sale price. The analyses lead to a conclusion that the price determinants, the most notable of which are auction houses and auction location, affect the sale price to a different extent. Yet, regardless the price, our subjective personal pleasure from a particular art piece might be crucial. JEL Classification Key...
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Price Determinants of Art Photography at Auctions
Habalová, Veronika ; Šopov, Boril (advisor) ; Bauer, Michal (referee)
In the recent years, prices of art have repeatedly broken records, and the interest in investing in fine art photography has been growing. Although there is plenty of research dedicated to studying prices of paintings, fine art photography has been largely overlooked. This thesis aims to shed light on identifying price determinants for this particular medium. A new data set is collected from sold lot archives of Sotheby's and Phillips auction houses, which also provide images of some of the sold items. These images are then used to create new variables describing visual attributes of the artworks. In order to inspect the effect of color-related predictors on price, four different methods are discussed. Color is found to be significant in OLS model, but the effect diminishes when model averaging is applied. Machine learning al- gorithms - regression trees and random forests - suggest that the importance of color is relatively low. The thesis also shows that expert estimates can improved by incorporating available information and using random forests for prediction. The fact that the expert estimates are not very accurate sug- gest that they either do not use all the available information or they do not process it efficiently. 1
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Analysis of Price Determinants in the Art Market
Mizeráková, Elena ; Šopov, Boril (advisor) ; Moravcová, Hana (referee)
1 Abstract What qualities make the best-selling artworks worth so much? Does the in- terest of the general public influence the probability that the art will be sold in auction? The art market research focuses on various aspects that affect the potential of art as an investment. The boom of big data offers a unique op- portunity to utilize its global impact and improve the present models with a novel measure. Into the econometric analysis of auction results the thesis im- plements a change in the Internet searching volume provided by Google Trends as a reflection of the taste and the state of mind of society. The subject of the detailed discussion are not only the price determinants, but also the factors that affect the selling probability. The findings lead to a conclusion that the proposed measure based on Google Trends is significant for determining both, the odds of selling the artwork and its price. Beside that, an important effect on the price and the probability have auction houses, the personal brand of the artist or the medium of artwork. JEL Classification D44, C25, F23, Z10, Z11 Keywords art market, auctions, Google Trends, prices, price determinants, odds of selling Author's e-mail elena.mizerakova@gmail.com Supervisor's e-mail boril.sopov@gmail.com
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