Refine
Year of publication
Document Type
- Working Paper (55)
Language
- English (55) (remove)
Has Fulltext
- yes (55)
Is part of the Bibliography
- no (55)
Keywords
- USA (4)
- Venture Capital (4)
- Credit Ratings (3)
- Kreditmarkt (3)
- Sovereign Risk (3)
- machine learning (3)
- Alternative investments (2)
- Asset Pricing (2)
- Blockchain (2)
- Disposition Effect (2)
Institute
- Center for Financial Studies (CFS) (55) (remove)
The pricing of digital art
(2023)
The intersection of recent advancements in generative artificial intelligence and blockchain technology has propelled digital art into the spotlight. Digital art pricing recognizes that owners derive utility beyond the artwork’s inherent value. We incorporate the consumption utility associated with digital art and model the stochastic discount factor and risk premiums. Furthermore, we conduct a calibration analysis to analyze the effects of shifts in the real and digital economy. Higher returns are required in a digital market upswing due to increased exposure to systematic risk and digital art prices are especially responsive to fluctuations in business cycles within digital markets.
We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG disparity have a higher option-implied cost of protection against downside tail risk. The impact of the misalignment across the different dimensions of the ESG score is distinct from that of ESG score level itself. Aggregate downside risk bears a negative price for firms with low ESG disparity.
Art-related non-fungible tokens (NFTs) took the digital art space by storm in 2021, generating massive amounts of volume and attracting a large number of users to a previously obscure part of blockchain technology. Still, very little is known about the attributes that influence the price of these digital assets. This paper attempts to evaluate the level of speculation associated with art NFTs, comprehend the characteristics that confer value on them and design a profitable trading strategy based on our findings. We analyze 860,067 art NFTs that have been deployed on the Ethereum blockchain and have been involved in 317,950 sales using machine learning methods to forecast the probability of sale, the trade frequency and the average price. We find that NFTs are highly speculative assets and that their price and recurrence of sale are heavily determined by the floor and the last sale prices, independent of any fundamental value.
The discount control mechanisms that closed-end funds often choose to adopt before IPO are supposedly implemented to narrow the difference between share price and net asset value. We find evidence that non-discretionary discount control mechanisms such as mandatory continuation votes serve as costly signals of information to reveal higher fund quality to investors. Rents of the skill signaled through the announcement of such policies accrue to managers rather than investors as differences in skill are revealed through growing assets under management rather than risk- adjusted performance.
We analyze the performance of marketplace lending using loan cash flow data from the largest platform, Lending Club. We find substantial risk-adjusted performance of about 40 basis points per month for the entire loan portfolio. Other loan portfolios grouped by risk category have similar risk-adjusted performance. We show that characteristics of the local bank sector for each loan, such as concentration of deposits and the presence of national banks, are related to the performance of loans. Thus, marketplace lending has the potential to finance a growing share of the consumer credit market in the absence of a competitive response from the traditional incumbents.
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.
This paper examines how the implementation of a new dark order - Midpoint Extended Life Order on NASDAQ - impacts financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of M-ELO trading on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed.
Is it true that speed bumps level the playing field, make financial markets more stable and reduce negative externalities of high-frequency trading (HFT) firms? We examine how the implementation of a particular speed bump – Midpoint Extended Life order (M-ELO) on Nasdaq impacted financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book message data around the implementation date and apply difference-in-differences analysis to estimate the average treatment effect of the speed bump on market stability and liquidity provision. The results suggest that the introduction of the M-ELO decreases the average number of crashes on Nasdaq compared to other exchanges by 4.7%. Liquidity provision by HFT firms also improves. These findings imply that technology-based solutions by exchanges are feasible alternatives to regulatory intervention towards safer markets.
We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique—neural networks—to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers’ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts’ systematic biases in expectations formation—and identify ex ante situations in which such biases are likely to arise.
We investigate the characteristics of infrastructure as an asset class from an investment perspective of a limited partner. While non U.S. institutional investors gain exposure to infrastructure assets through a mix of direct investments and private fund vehicles, U.S. investors predominantly invest in infrastructure through private funds. We find that the stream of cash flows delivered by private infrastructure funds to institutional investors is very similar to that delivered by other types of private equity, as reflected by the frequency and amounts of net cash flows. U.S. public pension funds perform worse than other institutional investors in their infrastructure fund investments, although they are exposed to underlying deals with very similar project stage, concession terms, ownership structure, industry, and geographical location. By selecting funds that invest in projects with poor financial performance, U.S. public pension funds have created an implicit subsidy to infrastructure as an asset class, which we estimate within the range of $730 million to $3.16 billion per year depending on the benchmark.
Direct financing of consumer credit by individual investors or non-bank institutions through an implementation of marketplace lending is a relatively new phenomenon in financial markets. The emergence of online platforms has made this type of financial intermediation widely available. This paper analyzes the performance of marketplace lending using proprietary cash flow data for each individual loan from the largest platform, Lending Club. While individual loan characteristics would be important for amateur investors holding a few loans, sophisticated lenders, including institutional investors, usually form broad portfolios to benefit from diversification. We find high risk-adjusted performance of approximately 40 basis points per month for these basic loan portfolios. This abnormal performance indicates that Lending Club, and similar marketplace lenders, are likely to attract capital to finance a growing share of the consumer credit market. In the absence of a competitive response from traditional credit providers, these loans lower costs to the ultimate borrowers and increase returns for the ultimate lenders.
We study the relevance of signaling and marketing as explanations for the discount control mechanisms that a closed-end fund may choose to adopt in its prospectus. These policies are designed to narrow the potential gap between share price and net asset value, measured by the fund’s discount. The two most common discount control mechanisms are explicit discretion to repurchase shares based on the magnitude of the fund discount and mandatory continuation votes that provide shareholders the opportunity to liquidate the fund. We find very limited evidence that a discount control mechanism serves as costly signal of information. Funds with mandatory voting are not more likely to delist than the rest of the CEFs in general or whenever the fund discount is large. Similarly, funds that explicitly discuss share repurchases as a potential response do not subsequently buy back shares more often when discounts do increase. Instead, the existence of these policies is more consistent with marketing explanations because the policies are associated with an increased probability of issuing more equity in subsequent periods.
This paper investigates how biases in macroeconomic forecasts are associated with economic surprises and market responses across asset classes around US data announcements. We find that the skewness of the distribution of economic forecasts is a strong predictor of economic surprises, suggesting that forecasters behave strategically (rational bias) and possess private information. Our results also show that consensus forecasts of US macroeconomic releases embed anchoring. Under these conditions, both economic surprises and the returns of assets that are sensitive to macroeconomic conditions are predictable. Our findings indicate that local equities and bond markets are more predictable than foreign markets, currencies and commodities. Economic surprises are found to link to asset returns very distinctively through the stages of the economic cycle, whereas they strongly depend on economic releases being inflation- or growth-related. Yet, when forecasters fail to correctly forecast the direction of economic surprises, regret becomes a relevant cognitive bias to explain asset price responses. We find that the behavioral and rational biases encountered in US economic forecasting also exists in Continental Europe, the United Kingdom and Japan, albeit, to a lesser extent.
In the secondary art market, artists play no active role. This allows us to isolate cultural influences on the demand for female artists’ work from supply-side factors. Using 1.5 million auction transactions in 45 countries, we document a 47.6% gender discount in auction prices for paintings. The discount is higher in countries with greater gender inequality. In experiments, participants are unable to guess the gender of an artist simply by looking at a painting and they vary in their preferences for paintings associated with female artists. Women's art appears to sell for less because it is made by women.
While record-making prices at art auctions receive headline news coverage, artists typically do not receive any direct proceeds from those sales. Early-stage creative work in any field is perennially difficult to value, but the valuation, reward, and incentivization for artistic labor are particularly fraught. A core challenge in studying the real return on artists’ work is the extreme difficulty accessing data from when an artwork was first sold. Galleries keep private records that are difficult to access and to match to public auction results. This paper, for the first time, uses archivally sourced primary market records, for the artists Jasper Johns and Robert Rauschenberg. Although this approach restricts the size of the data set, this innovative method shows much more accurate returns on art than typical regression and hedonic models. We find that if Johns and Rauschenberg had retained 10% equity in their work when it was first sold, the returns to them when the work was resold at auction would have outperformed the US S&P 500 by between 2 and 986 times. The implication of this work opens up vast policy recommendations with regard to secondary art market sales, entrepreneurial strategies using blockchain technology, and implications about how we compensate creative work.
Reliability and relevance of fair values : private equity investments and investee fundamentals
(2018)
We directly test the reliability and relevance of fair values reported by listed private equity firms (LPEs), where the unit of account for fair value measurement attribute (FVM) is an investment stake in an individual investee company. FVMs are observable for multiple investment stakes, fair values are economically important, and granular data on investee economic fundamentals that should underpin fair values are available in public disclosures. We find that LPE fund managers determine valuations based on accounting-based fundamentals—equity book value and net income—that are in line with those investors derive for listed companies. Additionally, our findings suggest that LPE fund managers apply a lower valuation weight to investee net income if direct market inputs are unobservable during investment value estimation. We interpret these findings as evidence that LPE fund managers do not appear mechanically to apply market valuation weights for publicly traded investees when determining valuations of non-listed. We also document that the judgments that LPE fund managers apply when determining investee valuations appear to be perceived as reliable by their investors.