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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.
We test whether asymmetric preferences for losses versus gains as in Ang, Chen, and Xing (2006) also affect the pricing of cash flow versus discount rate news as in Campbell and Vuolteenaho (2004). We construct a new four-fold beta decomposition, distinguishing cash flow and discount rate betas in up and down markets. Using CRSP data over 1963–2008, we find that the downside cash flow beta and downside discount rate beta carry the largest premia. We subject our result to an extensive number of robustness checks. Overall, downside cash flow risk is priced most consistently across different samples, periods, and return decomposition methods, and is the only component of beta that has significant out-of-sample predictive ability. The downside cash flow risk premium is mainly attributable to small stocks. The risk premium for large stocks appears much more driven by a compensation for symmetric, cash flow related risk. Finally, we multiply our premia estimates by average betas to compute the contribution of the different risk components to realized average returns. We find that up and down discount rate components dominate the contribution to average returns of downside cash flow risk. Keywords: Asset Pricing, Beta, Downside Risk, Upside Risk, Cash Flow Risk, Discount Rate Risk JEL Classification: G11, G12, G14
Deviations from normality in financial return series have led to the development of alternative portfolio selection models. One such model is the downside risk model, whereby the investor maximizes his return given a downside risk constraint. In this paper we empirically observe the international equity allocation for the downside risk investor using 9 international markets’ returns over the last 34 years. The results are stable for various robustness checks. Investors may think globally, but instead act locally, due to greater downside risk. The results provide an alternative view of the home bias phenomenon, documented in international financial markets. JEL Classification: G11, G12, G15
We examine the empirical predictions of a real option-pricing model using a large sample of data on mergers and acquisitions in the U.S. banking sector. We provide estimates for the option value that the target bank has in waiting for a higher bid instead of accepting an initial tender offer. We find empirical support for a model that estimates the value of an option to wait in accepting an initial tender offer. Market prices reflect a premium for the option to wait to accept an offer that has a mean value of almost 12.5% for a sample of 424 mergers and acquisitions between 1997 and 2005 in the U.S. banking industry. Regression analysis reveals that the option price is related to both the price to book market and the free cash flow of target banks. We conclude that it is certainly in the shareholders best interest if subsequent offers are awaited. JEL Classification: G34, C10
This study analyzes the short-term dynamic spillovers between the futures returns on the DAX, the DJ Eurostoxx 50 and the FTSE 100. It also examines whether economic news is one source of international stock return co-movements. In particular, we test whether stock market interdependencies are attributable to reactions of foreign traders to public economic information. Moreover, we analyze whether cross-market linkages remain the same or whether they do increase during periods in which economic news is released in one of the countries. Our main results can be summarized as follows: (i) there are clear short term international dynamic interactions among the European stock futures markets; (ii) foreign economic news affects domestic returns; (iii) futures returns adjust to news immediately; (iv) announcement timing of macroeconomic news matters; (v) stock market dynamic interactions do not increase at the time of the release of economic news; (vi) foreign investors react to the content of the news itself more than to the response of the domestic market to the national news; and (vii) contemporaneous correlation between futures returns changes at the time of macroeconomic releases. JEL Classification: G14, G15
This paper focuses on dynamic interactions of equity prices among theoretically related assets. We explore the existence of intraday non-linearities in the FTSE 100 cash and futures indices. We test whether the introduction of the electronic trading systems in the London Stock Exchange in 1997 and in the London International Financial Futures and Options Exchange (LIFFE) in 1999 has eliminated the non-linear dynamic relationship in the FTSE 100 markets. We show that the introduction of the electronic trading systems in the FTSE 100 markets has increased the efficiency of the markets by enhancing the price discovery process, namely by facilitating the increase of the speed of adjustment of the futures and cash prices to departures of the mispricing error from its non-arbitrage band. Nevertheless, we conclude that the automation of the markets has not completely eliminated the non-linear properties of the FTSE 100 cash and futures return series. JEL Classification: G12, G14, G15
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.
The 2011 European short sale ban on financial stocks: a cure or a curse? : [version 31 july 2013]
(2013)
Did the August 2011 European short sale bans on financial stocks accomplish their goals? In order to answer this question, we use stock options’ implied volatility skews to proxy for investors’ risk aversion. We find that on ban announcement day, risk aversion levels rose for all stocks but more so for the banned financial stocks. The banned stocks’ volatility skews remained elevated during the ban but dropped for the other unbanned stocks. We show that it is the imposition of the ban itself that led to the increase in risk aversion rather than other causes such as information flow, options trading volumes, or stock specific factors. Substitution effects were minimal, as banned stocks’ put trading volumes and put-call ratios declined during the ban. We argue that although the ban succeeded in curbing further selling pressure on financial stocks by redirecting trading activity towards index options, this result came at the cost of increased risk aversion and some degree of market failure.
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.