[4] In Art We Trust (with Yuexin Li and Luc Renneboog),previously titled Trust in Art Markets
Accepted at Management Science
Abstract: As trust is the cornerstone in the functioning of any market, we study the role of authenticity - as proxied by provenance information – of paintings sold in the art markets, which are opaque, illiquid, and unregulated. We examine the economic effects of authenticity on the sales probability of paintings, their hammer prices, and repeat sales returns. We collected provenance data from auction catalogues by implementing textual analysis and separated it to four dimensions related to pedigree, exhibition history, literature coverage, and certification (proof of authenticity by artists and experts). The provenance dimensions increase sales probability by 2% to 4%, lead to a price premium of 14% to 54%, and increase annualized returns by 5 to 16 percentage points, after controlling for artwork characteristics (such as topic, signature), artist, time, and auction house branch fixed effects. We perform a variety of robustness tests, e.g., by means of LASSO estimations. In order to address endogeneity issues in sales decisions, we examined repeat sales samples with longer holding periods and investigate the estate sales following the death of a collector, which we expect to be less affected by past prices. In order to address the potential reverse causality between past prices and changes in provenance, we applied a two-stage regressions on repeat sales, exploited Christie’s provenance policy change as a quasi-natural experiment in a Difference-in-Differences (DiD) setting, and also studied the provenance effects in a DiD setting for artists affected by fakes and forgeries after discovery. The attempts to address endogeneities do not invalidate the results.
Winner of TiSEM Alumni Funding 2018 (10,000 EUR).
Presentations: AEA Poster Session 2020; ESSEC Workshop 2019, Paris; EFMA 2019; FMA EU 2019; New York University 2019; Columbia University 2019; Erasmus University Rotterdam 2019; Tilburg University 2019; SWFA 2019, Houston; Art Markets Workshop Brussels 2018.
[3] Pricing Art and the Art of Pricing: On Returns and Risk in Art Auction Markets (with Yuexin Li and Luc Renneboog)
previously titled Sixty Years of Modern Art Markets: On Painting Prices and Returns
European Financial Management, 2022
Abstract: This paper studies the price determinants and investment performance of art. We apply a hedonic regression to over two million auction transactions of paintings. We conclude that art has appreciated in value by a moderate annualized return of 2.49%, in real U.S. dollar terms, between 1957 and 2016. A three-stage repeat sales and an adjacent-period repeat sales analyses confirm that our results are robust. Next, we investigate the investment performance of paintings by price levels, media, movements, markets, auction houses size, artist nationalities, market segmentations, and artists’ life and career cycle. In particular, Minimalism & Contemporary, Pop, and Abstract Expressionism artworks perform well in the sixty-year period with annualized real returns of 17.70%, 9.00%, and 6.22%, respectively and their performances are resilient in the recent financial crisis. Finally, we compare the investment performance and correlation of painting investment with other art and financial assets. Paintings exhibit negative correlations with stock and bond markets and receives about 7.6% weight in optimal portfolio.
[2] One year after COVID: the challenges and outlook of Chinese micro-and-small enterprises (with Tao Kong, Xiaobo Zhang, et al.)
China Economic Journal, 2022
Abstract: Based on a large Online Survey of Micro-and-small Enterprises (OSOME) conducted in March 2021 on micro-and-small enterprises and self-employed businesses (MSEs) operating on the Alipay platform, this paper examines the operational status, challenges, responses, and confidence of MSEs after exposure to the COVID-19 pandemic for over a year in China. The operational status of micro-and-small enterprises has significantly improved despite cash flow constraints. Rising costs and weak demand were two key challenges. In response to the COVID-19 shock, a higher percentage of newly established businesses adopted online sales and electronic information systems than those established earlier. Tax reduction was the most inclusive type of policy support. The confidence indices on market demand, production, and revenues for the next quarter returned to positive territory, indicating an optimistic outlook. The employment index remained just below the normal level, suggesting subdued expectations of expanded employment in the near future.
[1] Colors, Emotions, and the Auction Value of Paintings (with Charles N. Noussair and Luc Renneboog)
European Economic Review, 2022
Abstract: We study the impact of colors of paintings on prices in the art auction market, using both field and laboratory data. Our field data, consisting of art auction prices, reveal that a standard deviation increase in the percentage of blue (red) hue leads to premiums of 10.63% (4.20%). We conducted laboratory experiments in the US, China, and Europe, and elicited participants’ willingness-to-pay and emotions (pleasure-arousal). We confirm that blue and red colors command a premium. Blue (red) paintings generate 18.57% (17.28%) higher bids and stronger intention to purchase. Although abstract art is visually arousing, only the emotional pleasure channel relates colors and prices. Our results are consistent across all three cultures.
Winner of TiSEM Alumni Funding 2016 (6,500 EUR).
Selected Media Coverage: Artnet; Arttimes; Standaard; Tijd.
Presentations: Society of Experimental Finance Asia Pacific, Keynote Speech 2019; San Jose State University 2019; FMA EU 2019; SWFA 2019; SFS Cavalcade Asia Pacific 2018; City University of Hong Kong 2018; Singapore Management University 2018; Art Markets Workshop Brussels 2018.
Work in Progress
The Origin of Shareholder Activism: A Study on Dutch East India Company
Time is Money: Collectible Watch Investment
Pricing Complex Colors in Paintings
Dutch Buy-to-Let Mortgage
Short-term Price Prediction: Machine Learning in Commodity Order Books in Chinese (with Lun Li)