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This paper uses proprietary data from a leading intermediary to explain the magnitude and determinants of transaction costs in the secondary market for private equity stakes. Most transactions occur at a discount to net asset value. Buyers average an annualized public market equivalent of 1.023 compared with 0.976 for sellers, implying that buyers outperform sellers by a market-adjusted 5 percentage points annually. Both the cross-sectional pattern of transaction costs and the identity of sellers and buyers suggest that the market is one in which relatively flexible buyers earn returns by supplying liquidity to investors wishing to exit.

Standard measures of private equity performance based on cash flows overlook discount rate risk. An index constructed from prices paid in secondary market transactions indicates that private equity discount rates vary considerably. While the standard alpha for our index is zero, measures of performance based on cash flow data for funds in our index are large and positive. To illustrate that results are not driven by idiosyncrasies of private equity secondary markets, we obtain similar results using cash flows and returns of synthetic funds that invest in small cap stocks. Ignoring variation in PE discount rates can lead to a misallocation of capital. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Speculative behavior plays a key role in financial markets, but little is known about its causes. We test for neighborhood effects on speculative behavior using daily lottery sales data, allowing us to separate investor sentiment effects from information transmission effects. In a sample of 160,000 retailers, lottery sales in a census block increase by $0.26, on average, for each $1 increase in neighboring blocks. We test whether this correlation is attributable to contextual effects, correlated effects, or endogenous effects. Our analysis suggests that social interaction, driven by investor sentiment, is a key cause of speculative behavior.

Measuring the performance of private equity investments (buyout and venture) has historically only been possible over long horizons because the IRR on a fund is only observable following the fund’s final distribution. We propose a new approach to evaluating performance using actual prices paid for limited partner shares of funds in secondary markets. We construct indices of buyout and venture capital performance using a proprietary database of secondary market prices between 2006 and 2017. These transaction-based indices exhibit significantly higher betas and volatilities, and lower alphas than NAV-based indices built from Preqin and obtained from Burgiss. There are a number of potential uses for these indices. In particular, they provide a way to track the returns of the buyout and venture capital sectors on a quarter-to-quarter basis and to value illiquid stakes in funds.

An important cost of investing in private equity is the illiquidity of these investments. In response to this illiquidity, a secondary market for transacting stakes in private equity funds has developed. This paper uses proprietary data from a leading intermediary to understand the magnitude and determinants of transaction costs in this market. Most transactions occur at a discount to net asset value. Buyers average an annualized Public Market Equivalent (PME) of 1.023 compared to 0.974 for sellers, implying that buyers outperform sellers by a market-adjusted five percentage points annually. For the most common type of transaction, the sale of stakes in funds four to nine years old, the difference is smaller, about three percentage points. Both the discount to NAV and the difference in returns between buyers and sellers returns appear to be related to factors associated with asymmetric information and market depth. Buyers in this market tend to be funds-of-funds, while sellers are more likely to be traditional private equity investors such as endowments and pension funds.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We investigate the relationship between ex ante total skewness and holding returns on individual equity options. Recent theoretical developments predict a negative relationship between total skewness and average returns, in contrast to the traditional view that only coskewness is priced. We find, consistent with recent theory, that total skewness exhibits a strong negative relationship with average option returns. Differences in average returns for option portfolios sorted on ex ante skewness range from 10% to 50% per week, even after controlling for risk. Our findings suggest that these large premiums compensate intermediaries for bearing unhedgeable risk when accommodating investor demand for lottery-like options.

We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of lagged volatility, lagged returns and other forecasting variables. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it results in a positive-definite estimated covariance matrix. We apply the model to the covariance matrix of size-sorted stock returns and find that two factors are sufficient to capture most of the dynamics.

A diversified firm can trade at a discount to a matched portfolio of single-segment firms if the diversified firm has either lower expected cash flows or higher expected returns than the single-segment firms. We study whether firms with diversification discounts have higher expected returns in order to compensate investors for offering less upside potential (or skewness exposure) than focused firms. Our empirical tests support this hypothesis. First, we find that focused firms offer greater skewness exposure than diversified firms. Second, we find that diversified firms have significantly larger discounts when the diversified firm offers less skewness than matched single-segment firms. Finally, we find that up to 53% of the excess returns received on diversification-discount firms relative to diversification-premium firms can be explained by differences in exposure to skewness.

We develop a one-period model of investor asset holdings where investors have heterogeneous preference for skewness. Introducing heterogeneous preference for skewness allows the model's investors, in equilibrium, to underdiversify. We find suppport for our model's three key implications using a dataset of 60,000 individual investor accounts. First, we document that the portfolio returns of underdiversified investors are substantially more positively skewed than those of diversified investors. Second, we show that the apparent mean-variance inefficiency of underdiversified investors can be largely explained by the fact that investors sacrifice mean-variance efficiency for higher skewness exposure. Furthermore, contrary to the asset-pricing predictions of models that incorporate return skewness in the context of full diversification, we show that idiosyncratic skewness, and not just coskewness, can impact equilibrium prices. Third, the underdiversification of investors does not appear to be coincidentally related to skewness. Stocks most often selected by underdiversified investors have substantially higher average skewness -- especially idiosyncratic skewness -- than stocks most often selected by diversified investors.

Constant-quality commercial indices generated by ordinary least squares may suffer an efficiency loss due to leptokurtosis caused by outliers in transactions data. When the subsequent nonnormality occurs, substantial improvement in index precision is obtained by estimating the hedonic model using a semiparametric adaptive estimator technique. When this method was applied to 1,846 office transactions that occurred in the Phoenix metropolitan area from January 1997 through June 2004, a substantial standard error reduction of approximately 9% was realized relative to ordinary least squares estimates. The difference in average returns between the semiparametric method and ordinary least squares was about 0.25% in each period, which represents a substantial increase in commercial property index precision.

The proposition that investors are overconfident about their valuation and trading skills can explain high observed trading volume. With biased self-attribution, the level of investor overconfidence and thus trading volume varies with past returns. We test the trading volume predictions of formal overconfidence models and find that share turnover is positively related to lagged returns for many months. The relationship holds for both market-wide and individual security turnover, which we interpret as evidence of investor overconfidence and the disposition effect, respectively. Security volume is more responsive to market return shocks than to security return shocks, and both relationships are more pronounced in small-cap stocks and in earlier periods where individual investors hold a greater proportion of shares. (JEL G11, G12) Copyright 2006, Oxford University Press.

The valuation of Canadian paintings is analyzed empirically. Using a sample of auction prices for major Canadian painters for the period 19682001, we run hedonic regressions to analyze the in‡uence of various factors, including painter identity, on auction prices, as well as to construct a market price index. This index is used in a second stage analysis in which we analyze the properties of Canadian art viewed as an investment asset. We consider the extent to which standard asset pricing theory, as incorporated in the capital asset pricingmodel (CAPM), can account for price movements in the market for Canadian paintings.

We develop a preference-based equilibrium asset pricing model that explains low-frequency conditional volatility. Similar to Barberis, Huang, and Santos (2001), agents in our model care about wealth changes, experience loss aversion, and keep a mental scorecard that affects their level of risk aversion. A new feature of our model is that when perturbed by unexpected returns, investors become temporarily more sensitive to news. Gradually investors become accustomed to the new level of wealth, restoring prior levels of risk aversion and news sensitivity. The state-dependent sensitivity to news creates the type of volatility clustering found in low-frequency stock returns. We find empirical support for our model's predictions that relate the scorecard to conditional volatility and skewness. Copyright 2004, Oxford University Press.

The valuation of Canadian paintings is analysed empirically. Using a sample of auction prices for major Canadian painters for the period 1968-2001, we run hedonic regressions to analyse the influence of various factors, including painter identity, on auction prices, as well as to construct a market price index. This index is used in a second-stage analysis in which we analyse the properties of Canadian art viewed as an investment asset. We apply standard asset pricing theory, as incorporated in the capital asset pricing model (CAPM), to the analysis of price movements in the market for Canadian paintings.

We compare and contrast some existing ordinary least squares (OLS)- and generalized method of moments (GMM)-based tests of asset pricing models with a new more general test. This new test is valid under the assumption that returns are elliptically distributed, a necessary and sufficient assumption of the linear capital asset pricing model (CAPM). This new test fails to reject the CAPM on a dataset of stocks sorted by market valuations, whereas similar tests constructed from OLS and GMM estimation methods reject the linear CAPM. We also find that outliers reduce the OLS-estimated mispricing of the linear CAPM on monthly returns sorted by previous performance, that is, momentum. Monte Carlo evidence supports superior size and power properties of the new test relative to OLS- and GMM-based tests. Copyright 2003, Oxford University Press.

A semiparametric efficient estimation procedure is developed for the parameters of multivariate generalized autoregressive conditional heteroscedasticity-in-mean models when the disturbances have a conditional distribution assumed to be elliptically symmetric but otherwise unrestricted. Under high-level assumptions, the resulting estimator achieves the asymptotic semiparametric efficiency bound. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that would otherwise arise in estimating the unknown error distribution. This framework is suitable for the estimation and testing of conditional asset-pricing models, such as the conditional capital asset-pricing model. We apply our procedure in an empirical study of stock prices, with Monte Carlo simulation results also reported.

We further the understanding of cross-sectional differences in trading activity. Specifically, we link a firm’s visibility, as measured by advertising, to its stock turnover. First, we suggest three mechanisms (beyond simple awareness) capable of explaining how the repeated and consistent ads from well-known firms in the marketplace are linked to turnover. Second, using a data base of firms reporting advertising between January 1971 and December 2007, we regress average daily turnover within a year on annual ad spending for the year and find a positive and significant coefficient after controlling for extant determinants of turnover. Third, using a twenty year Super Bowl advertising event study, we again document a positive relationship between advertising and turnover.

We test the prediction of recent theories that stocks with high idiosyncratic skewness should have low expected returns. Because lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Consistent with recent theories, we find that expected idiosyncratic skewness and returns are negatively correlated. Specifically, the Fama-French alpha of a low-expected-skewness quintile exceeds the alpha of a high-expected-skewness quintile by 1.00% per month. Furthermore, the coefficients on expected skewness in Fama-MacBeth cross-sectional regressions are negative and significant. In addition, we find that expected skewness helps explain the phenomenon that stocks with high idiosyncratic volatility have low expected returns. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Les auteurs presentent un nouveau modele de la matrice des covariances realisees des rendements boursiers dans lequel la matrice est exprimee sous forme logarithmique et les facteurs latents sont fonction a la fois de la volatilite passee et des rendements historiques. Le modele offre plusieurs avantages : il est parcimonieux, il ne necessite pas l'imposition de restrictions sur les parametres et il produit une matrice des covariances definie positive.

We apply semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric to study the efficiency of the foreign exchange market. We consider both cointegrating regressions and standard stationary regressions. The elliptical symmetry assumption allows us to avoid the curse of dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate elliptically symmetric density function can be written as a function of a scalar transformation of the observed multivariate data. We test the unbiasedness hypothesis on both weekly and daily exchange rate data and strongly reject unbiasedness at the weekly horizon, but fail to reject the unbiasedness hypothesis on the daily data. Estimates of the semiparametric procedure in some cases differ substantially from traditional OLS estimates.

A semiparametric efficient estimation procedure is developed for the parameters of multivariate GARCH-in-mean models when the disturbances have a distribution that is assumed to be elliptically symmetric but is otherwise unrestricted. Under high level restrictions, the resulting estimator achieves the asymptotic semiparametric efficiency bound. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that would otherwise arise in estimating the unknown error distribution. This framework is suitable for the estimation and testing of conditional asset pricing models such as the conditional CAPM, and we apply our estimator in an empirical study of stock prices, with Monte Carlo simulation results also being reported. Nous développons un nouvel estimateur pour les paramètres d'un modèle de GARCH en moyenne (" GARCH-M ") avec plusieurs variables. L'estimateur a l'efficacité semiparamétrique quand les erreurs suivent une loi de probabilité qui est elliptiquement symétrique mais n'aucune autre restriction. Sous les hypothèses de haut niveau, notre estimateur obtient la limite d'efficacité semiparamétrique. L'hypothèse de la symétrie elliptique nous permet d'éviter le problème d'estimer non-paramétriquement une fonction de haut dimension, parce qu'on peut écrire la densité d'un loi elliptique comme un fonction d'une transformation unidimensionnelle de la variable aléatoire multidimensionnelle. Ce cadre est approprié pour analyser des modèles conditionnels des prix des actifs financiers, comme le CAPM conditionnel. Nous appliquons notre méthodologie à l'étude des prix des actions, et nous rendons compte des résultats d'une étude simulation "Monte-Carlo".

We develop new tests of the capital asset pricing model (CAPM) that take account of and are valid under the assumption that the distribution generating returns is elliptically symmetric; this assumption is necessary and sufficient for the validity of the CAPM. Our test is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric, but otherwise unrestricted. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate elliptically symmetric density function can be written as a function of a scalar transformation of the observed multivariate data. The elliptically symmetric family includes a number of thick-tailed distributions and so is potentially relevant in financial applications. Our estimated betas are lower than the OLS estimates, and our parameter estimates are much less consistent with the CAPM restrictions than the corresponding OLS estimates. Nous développons de nouveaux tests du modèle d'évaluation des actifs financiers (" CAPM ") qui tiennent compte de, et sont valides sous, l'hypothèse que les retours des actifs découlent d'un loi de probabilité elliptiquement symétrique. Cette hypothèse est nécessaire et suffisante pour la validité du CAPM. Notre test utilise un estimateur des paramètres du modèle qui a l'efficacité semiparamétrique quand on a un modèle de régression apparemment sans relation et qui a des erreurs qui suivent une loi elliptiquement symétrique. L'hypothèse de la symétrie elliptique nous permet d'éviter le problème d'estimer non-paramétriquement une fonction de haute dimension parce qu'on peut écrire la densité d'une loi elliptique comme une fonction d'une transformation unidimensionnelle de la variable aléatoire multidimensionnelle. La famille des lois elliptiquement symétriques inclue plusieurs lois leptokurtiques, donc elle est pertinente à des applications financières. Les bêtas obtenus avec notre estimateur sont plus bas que ceux qui sont obtenus en utilisant des moindres carrés, et sont moins compatibles avec le CAPM.