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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.
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.
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.
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.
Emotions-at-risk: an experimental investigation into emotions, option prices and risk perception
(2014)
This paper experimentally investigates how emotions are associated with option prices and risk perception. Using a binary lottery, we find evidence that the emotion ‘surprise’ plays a significant role in the negative correlation between lottery returns and estimates of the price of a put option. Our findings shed new light on various existing theories on emotions and affect. We find gratitude, admiration, and joy to be positively associated with risk perception, although the affect heuristic predicts a negative association. In contrast with the predictions of the appraisal tendency framework (ATF), we document a negative correlation between option price and surprise for lottery winners. Finally, the results show that the option price is not associated with risk perception as commonly used in psychology.
We investigate the effect of the tone of news on investor stock price expectations and beliefs. In an experimental study we ask subjects to estimate a future stock price for twelve real listed companies. As additional information we provide them with historical stock prices and extracts from real newspaper articles. We propose a way to manipulate the tone of news extracts without distorting its content. Subjects in different treatment groups read news items that are written either in positive or negative tone for each stock. We find that subjects tend to predict a significantly higher (lower) return for stocks after reading positive (negative) tone news. The effect is especially pronounced for stocks with poor past performance. Subjects are more likely to be optimistic (pessimistic) about the economy and to buy (sell) stocks after reading positive (negative) than negative (positive) tone news. Our results show that the news media might affect not only how investors perceive information, but also what they do in response to it.
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.
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.
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.
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.
This paper investigates whether the overpricing of out-of-the money single stock calls can be explained by Tversky and Kahneman’s (1992) cumulative prospect theory (CPT). We argue that these options are overpriced because investors overweight small probability events and overpay for such positively skewed securities, i.e., characteristics of lottery tickets. We match a set of subjective density functions derived from risk-neutral densities, including CPT with the empirical probability distribution of U.S. equity returns. We find that overweighting of small probabilities embedded in CPT explains on average the richness of out-of-the money single stock calls better than other utility functions. The degree that agents overweight small probability events is, however, strongly timevarying and has a horizon effect, which implies that it is less pronounced in options of longer maturity. We also find that time-variation in overweighting of small probabilities is strongly explained by market sentiment, as in Baker and Wurgler (2006).
Low probability events are overweighted in the pricing of out-of the-money index puts and single stock calls. We find that this behavioral bias is strongly time-varying, linked to equity market sentiment, and higher moments of the risk-neutral density. An implied volatility (IV) sentiment measure that is jointly derived from index and single stock options explains investors' overweight of tail events the best. Our findings also suggest that IV-sentiment predicts equity markets reversals better than overweight of small probabilities itself. When employed in a trading strategy, IV-sentiment delivers economically significant results, which are more consistent than the ones produced by the market sentiment factor. The joint use of information from the single stock and index option markets seems to explain the forecasting power of IV-sentiment. Out-of-sample tests on reversal prediction show that our IV-sentiment measure adds value over and above traditional factors in the equity risk premium literature, especially as an equity-buying signal. This reversals prediction seems to improve time-series and cross-sectional momentum strategies.
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.
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.
This chapter analyzes the risk and return characteristics of investments in artists from the Middle East and Northern Africa (MENA) region over the sample period 2000 to 2012. With hedonic regression modeling we create an annual index that is based on 3,544 paintings created by 663 MENA artists. Our empirical results prove that investing in such a hypothetical index provides strong financial returns. While the results show an exponential growth in sales since 2006, the geometric annual return of the MENA art index is a stable13.9 percent over the whole period. We conclude that investing in MENA paintings would have been profitable but also note that we examined the performance of an emerging art market that has only seen an upward trend without any correction, yet.
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 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.
The record-breaking prices observed in the art market over the last three years raise the question of whether we are experiencing a speculative bubble. Given the difficulty to determine the fundamental value of artworks, we apply a right-tailed unit root test with forward recursive regressions (SADF test) to detect explosive behaviors directly in the time series of four different art market segments (“Impressionist and Modern”, “Post-war and Contemporary”, “American”, and “Latin American”) for the period from 1970 to 2013. We identify two historical speculative bubbles and find an explosive movement in today’s “Post-war and Contemporary” and “American” fine art market segments.
Euro crash risk
(2015)
This paper sets the background for the Special Issue of the Journal of Empirical Finance on the European Sovereign Debt Crisis. It identifies the channel through which risks in the financial industry leaked into the public sector. It discusses the role of the bank rescues in igniting the sovereign debt crisis and reviews approaches to detect early warning signals to anticipate the buildup of crises. It concludes with a discussion of potential implications of sovereign distress for financial markets.
We investigate the effect of overreaction in the fine art market. Using a unique sample of auction prices of modern prints, we define an overvalued (undervalued) print as a print that was bought for a price above (below) its high (low) auction pricing estimate. Based on the overreaction hypothesis, we predict that overvalued (undervalued) prints generate a negative (positive) excess return at a subsequent sale. Our empirical findings confirm our expectations. We report that prints that were bought for a price 10 percent above (below) its high (low) pricing estimate generate a positive (negative) excess return of 12 percent (17 percent) after controlling for the general price movement on the prints market. The price correction for overvalued (undervalued) prints is more pronounced during recessions (expansions).
This paper investigates the impact of news media sentiment on financial market returns and volatility in the long-term. We hypothesize that the way the media formulate and present news to the public produces different perceptions and, thus, incurs different investor behavior. To analyze such framing effects we distinguish between optimistic and pessimistic news frames. We construct a monthly media sentiment indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and/or the lead paragraph. Our results indicate that pessimistic news media sentiment is positively related to global market volatility and negatively related to global market returns 12 to 24 months in advance. We show that our media sentiment indicator reflects very well the financial market crises and pricing bubbles over the past 20 years.
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.
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.
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.
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.
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 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.
A premise of the capabilities perspective in strategy is that firm-specific capabilities allow some firms to be unusually adept at exploiting growth opportunities. Since few firms have the capacity to internally generate the quantity or variety of strategic resources needed to exploit growth opportunities, the ability to externally acquire complementary resources is critical to the acquisition of competitive advantage. However, the external sourcing of resources exposes the firm’s strategic resources to risks of expropriation. We argue this threat gives capable firms incentive to use internally generated strategic resources to pursue growth opportunities before turning to external sources. A pecking order theory of strategic resource deployment is implied. Data from a 22-year sample of cross-border investment partnership decisions made by U.S.-based venture capital firms lend support to our theory.
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.
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.