The 21st edition of the International Conference on Cultural Economics (ACEI2020+1) took place online from 6 to 9 July 2021. On this occasion, members of TIAMSA Prof. Andrej Srakar (University of Ljubljana), Prof. Marilena Vecco (Burgundy School of Business), and Dr Anne-Sophie V. Radermecker (Erasmus University Rotterdam/ULB) chaired a special art market panel entitled: “Art Markets and Econom(etr)ics: New Approaches and Perspectives.” The goal of the meeting was to present and reflect on novel modelling approaches in art market studies, by inviting three specialists who have made an impressive mark on the development of the field.
Art markets have a long tradition of research in cultural economics. Singer and Lynch (1997) distinguish three phases of its development: the ﬁrst phase with the path-breaking study of Baumol (Art Investment as Floating Crap Game, 1986), generally acknowledged as the founder of art market research in economics. The second phase which is characterized by a number of studies directly inﬂuenced by Baumol but which go beyond him in clearly identiﬁable ways; and the third phase, devoted to the study of particular aspects of the art market. This last phase has used more advanced techniques of study devoted to increasingly narrow and intricate issues. Frey (1997) also lists six areas of possible advancement for the future of economic research on art markets: taxation, methodological approaches specifically focusing on economic modelling, testable propositions, behavioral anomalies, practice and management implications, and policy.
This special panel focused on Frey’s second area: the developments in methodology to model auction prices and behaviors, art indices, and determinants of art sales. While the growing success of quantitative and data-driven art market studies is increasingly compelling researchers from different disciplines to exploit basic economic tools such as hedonic pricing models or repeat sales regressions, technical improvements strictly speaking are still possible and even needed to better capture the complexity of a segmented art market made up of particularly heterogeneous artworks. While attempts to improve current models can be found in several papers testing, for example, the potential of Bayesian modelling, different parametric, semiparametric and nonparametric possibilities, model averaging, network analysis, ethnographic research, and machine learning, it is worth noting that current art market literature does not exhaust the many possibilities provided by contemporary statistics and econometrics.
In order to reflect on these recent developments, the panel welcomed three scholars whose works have significantly contributed to developing existing models, especially in the field of finance, one of the most advanced in the econometric analysis of the art market. After a brief introduction by Dr. Johannes Nathan, co-chair of TIAMSA, Tim Fry, professor of econometrics at RMIT University (Melbourne), opened the panel by discussing the case of Australian indigenous art at auction. His research chiefly deals with market heterogeneity and segmentation and suggests not to pool data when considering artworks by different artists with heterogenous visual characteristics and economic value. Using cutting-edge technology, Christophe Spaenjers, associate professor of finance at HEC Paris Business school, presented his co-authored paper that exploits the potential of Machine Learning (ML) and neural network to predict art prices. Their findings suggest that, in several respects, ML tends to perform better than traditional hedonic pricing models in the prediction of auction prices. The third panelist was Fabian Bocart, chief data scientist at Artnet in New York. His research intends to better consider infrequently traded assets such as artworks when building price indices for investment purposes. The originality of this approach, still based on a repeat sales model, consists in creating some correlations between monthly art market indices and other markets characterized by higher liquidity and high frequency trading.
Following the three presentations, a collective discussion was initiated by Andrej Srakar who pushed the reflection further by addressing questions related, among others, to distributional problems in art price regressions, sample selectivity, probabilistic opportunities for modelling the evolution of art prices, price volatility, and causal perspective in the art market. While all panelists acknowledged the limitations of traditional linear models, the necessity of aligning econometric methods with research purposes was emphasized. The potential of econometric tools to better understand buyers’ preferences, taste formation mechanisms, dynamics of fashion, and artist careers was evoked, just as experimental research to examine drivers of enjoyment and willingness-to-pay and the potential of neuro-aesthetics and neuro-economics were introduced as promising research avenues in art market studies.
Lisa Farrell, Fry, Jane, and Tim Fry. 2018. Determinants of sales and price at auction for three Australian Indigenous artists: to pool or not to pool? Journal of Cultural Economics 42(3): 507-520. https://doi.org/10.1007/s10824-017-9314-0
Aubry, Mathieu, Kräussl, Roman, Manso, Gustavo and Spaenjers, Christophe. 2021. Biased Auctioneers. HEC Paris Research Paper No. FIN-2019-1332. Available at SSRN: https://ssrn.com/abstract=3347175 or http://dx.doi.org/10.2139/ssrn.3347175
Bocart, Fabian, Ghysels, Eric, Hafner, Christian. 2020. Monthly Art Market Returns. Journal of Risk and Financial Management 13(5): 100. https://doi.org/10.3390/jrfm13050100