Working Paper
Refine
Year of publication
- 2022 (5) (remove)
Document Type
- Working Paper (5) (remove)
Language
- English (5)
Has Fulltext
- yes (5)
Is part of the Bibliography
- no (5)
Keywords
- Performance (2)
- Art investment (1)
- Blockchain (1)
- ESG (1)
- Environmental (1)
- Fund family (1)
- Governance (1)
- Greenwashing (1)
- Liquidity (1)
- NFT (1)
Institute
Employing the art-collection records of Burton and Emily Hall Tremaine, we consider whether early-stage art investors can be understood as venture capitalists. Because the Tremaines bought artists’ work very close to an artwork’s creation, with 69% of works in our study purchased within one year of the year when they were made, their collecting practice can best be framed as venture-capital investment in art. The Tremaines also illustrate art collecting as social-impact investment, owing to their combined strategy of art sales and museum donations for which the collectors received a tax credit under US rules. Because the Tremaines’ museum donations took place at a time that U.S. marginal tax rates from 70% to 91%, the near “donation parity” with markets, creating a parallel to ESG investment in the management of multiple forms of value.
Venture capital (VC) funds backed by large multi-fund families tend to perform substantially better due to cross-fund cash flows (CFCFs), a liquidity support mechanism provided by matching distributions and capital calls within a VC fund family. The dynamics of this mechanism coincide with the sensitivity of different stage projects owing to market liquidity conditions. We find that the early-stage funds demand relatively more intra-family CFCFs than later-stage funds during liquidity stress periods. We show that the liquidity improvement based on the timing of CFCF allocation reflects how fund families arrange internal liquidity provision and explains a large part of their outperformance.
This study examines the recent literature on the expectations, beliefs and perceptions of investors who incorporate Environmental, Social, Governance (ESG) considerations in investment decisions with the aim to generate superior performance and also make a societal impact. Through the lens of equilibrium models of agents with heterogeneous tastes for ESG investments, green assets are expected to generate lower returns in the long run than their non- ESG counterparts. However, at the short run, ESG investment can outperform non-ESG investment through various channels. Empirically, results of ESG outperformance are mixed. We find consensus in the literature that some investors have ESG preference and that their actions can generate positive social impact. The shift towards more sustainable policies in firms is motivated by the increased market values and the lower cost of capital of green firms driven by investors’ choices.
This paper provides a review of the development of the non-fungible tokens (NFTs) market, with a particular focus on its pricing determinants, its current applications and future opportunities. We investigate the current state of the NFT markets and highlight the perception and expectations of investors towards these products. We summarize and compare the financial and econometric models that have been used in the literature for the pricing of non-fungible tokens with a special focus on their predictive performance. Our intention is to design a framework that can help understanding the price formation of NFTs. We further aim to shed light on the value creating determinants of NFTs in order to better understand the investors’ behavior on the blockchain.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.