The Performance of Hedge Fund Strategies and the Asymmetry of Return Distributions

We present hedge fund performance estimates that adjust for stale prices, Fama‐French risk factors and skewness. We contrast these new performance estimates with traditional performance measures. Using three‐factor models to adjust for staleness in prices and to incorporate Fama‐French factors along with the Harvey‐Siddique (2000) two‐factor model that incorporates skewness, we find that for the period 1990–2003, all hedge fund categories achieve above average performance when measured against an aggregate market index. More significantly, however, when we estimate performance at the individual hedge fund level, we discover that only 40 to 47% of the funds are shown to achieve an above average performance over that time period depending on the model used. These results have important implications for investors, endowments and pensions when they choose hedge fund managers.     

LIQUIDITY AND ASSET PRICING UNDER THE THREE‐MOMENT CAPM PARADIGM

We examine whether the use of the three‐moment capital asset pricing model can account for liquidity risk. We also make a comparative analysis of a four‐factor model based on Fama–French and Pástor–Stambaugh factors versus a model based solely on stock characteristics. Our findings suggest that neither of the models captures the liquidity premium nor do stock characteristics serve as proxies for liquidity. We also find that sensitivities of stock return to fluctuations in market liquidity do not subsume the effect of characteristic liquidity. Furthermore, our empirical findings are robust to differences in market microstructure or trading protocols between NYSE/AMEX and NASDAQ.     

A Dynamic Model of Firm Valuation

We propose a dynamic version of the dividend discount model, solve it in closed form, and assess its empirical validity. The valuation method is tractable and can be easily implemented. We find that our model produces equity value forecasts that are very close to market prices, and explains a large proportion of the observed variation in share prices. Moreover, we show that a simple portfolio strategy based on the difference between market and estimated values earns considerably positive returns. These returns cannot be simply explained either by the Fama‐French three‐factor model (even after adding a momentum factor) or the Fama‐French five‐factor model.     

Conditional Asset Pricing and Stock Market Anomalies in Europe

This study provides European evidence on the ability of static and dynamic specifications of the Fama‐French (1993) three‐factor model to price 25 size‐B/M portfolios. In contrast to US evidence, we detect a small‐growth premium and find that the size effect is still present in Europe. Furthermore, we document strong time variation in factor risk loadings. Incorporating these risk fluctuations in conditional specifications of the three‐factor model clearly improves its ability to explain time variation in expected returns. However, the model still fails to completely capture cross‐sectional variation in returns as it is unable to explain the momentum effect.     

Resolving the Presidential Puzzle

Many financial economists are puzzled by the fact that stock returns are higher under Democratic than Republican presidencies. In this paper, we test whether this return differential is explained by risk using a conditional version of the Fama and French (1993) model that allows risk to vary across political cycles. We find that the presidential puzzle can be explained when risk is properly taken into account. Much of the return differential can be attributed to the fact that Democratic presidencies are associated with higher market and default risk premiums than their Republican counterparts.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors‐in‐variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and the Fama and French (1993) three‐factor model. We find that time‐variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three‐factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm‐specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three‐factor model, the unconditional CAPM, and the unconditional three‐factor model.     

Volatility transmission in the UK equity market

In this study, the three-factor model of Fama and French and the ‘characteristic model’ of Daniel and Titman are tested using the French Stock Market. Stocks are ranked by size and book to market ratio and then by ex-ante β, HML or SMB loadings. Based on average returns, results reject the factor model with ‘characteristic balanced’ portfolios. In contrast, in time-series regressions, results are consistent with the factor pricing model and inconsistent with the characteristic-based pricing model. Because the value premium is small, conclusions must be interpreted carefully. However, size and market premiums allow more powerful tests of the two models.     

On the differences in measuring SMB and HML in the UK – Do they matter?

The Fama–French (FF) three factor model expands the capital asset pricing model (CAPM) to include two additional factors to the market factor – SMB, employed to capture a firm size effect in returns and HML employed to capture book-to-market effects in returns. In the UK, different researchers use different ways of calculating SMB and HML in the context of empirical applications of the three factor model, or extensions of it, perhaps because they believe the differences in the construction of the SMB and HML factors to be relatively unimportant from an empirical standpoint. We investigate whether indeed factor construction methods are unimportant. Our conclusion is that they do matter.     

Divergence of Opinion Surrounding Extreme Events

This paper examines the stock market performance of a large sample of new issues (IPOs and SEOs) following an extreme price movement during the first three years after the offering. Strong underperformance follows either a positive or negative (at least +/?15%) one‐day return event. This poor performance cannot be explained by the Fama‐French four‐factor methodology, or by the generally low stock returns of growth firms. Unlike recent issuers, non‐issuers report no poor performance following a similar extreme event using the four‐factor methodology. The extreme event date shows very high levels of turnover, a measure of divergence of opinion. Finally, there is a strong negative linkage between higher levels of divergence of opinion and subsequent stock performance.     

Risk Measures for Hedge Funds: a Cross‐sectional Approach

This paper analyses the risk‐return trade‐off in the hedge fund industry. We compare semi‐deviation, value‐at‐risk (VaR), Expected Shortfall (ES) and Tail Risk (TR) with standard deviation at the individual fund level as well as the portfolio level. Using the Fama and French (1992) methodology and the combined live and defunct hedge fund data from TASS, we find that the left‐tail risk captured by Expected Shortfall (ES) and Tail Risk (TR) explains the cross‐sectional variation in hedge fund returns very well, while the other risk measures provide statistically insignificant or marginally significant results. During the period between January 1995 and December 2004, hedge funds with high ES outperform those with low ES by an annual return difference of 7%. We provide empirical evidence on the theoretical argument by Artzner et al. (1999) that ES is superior to VaR as a downside risk measure. We also find the Cornish‐Fisher (1937) expansion is superior to the nonparametric method in estimating ES and TR.     

Asset Pricing and the Illiquidity Premium

In this paper, we examine the asset‐pricing role of liquidity (as proxied by share turnover) in the context of the Fama and French (1993) three‐factor model. Our analysis employs monthly Australian data, covering the sample period from 1990 to 1998. The key finding of our research is that the main test is unable to reject the test of over‐identifying restrictions, thus supporting the overall favorability of the liquidity‐augmented Fama–French model. In addition, we find that the asset‐pricing performance of the liquidity factor is generally very robust to a wide range of sensitivity checks.     

Pricing Model Performance and the Two‐Pass Cross‐Sectional Regression Methodology

Over the years, many asset pricing studies have employed the sample cross‐sectional regression (CSR) R² as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R² differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama–French three‐factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.     

Creating Fama and French Factors with Style

This paper utilizes Frank Russell style portfolios to create useful proxies for the Fama and French (1992) factors. The proxy‐mimicking portfolios are shown to represent a pervasive source of exposure across U.S. industry portfolios and to generally possess similar properties to those utilized in the finance literature. Further, a set of multivariate asset‐pricing tests of the three‐factor Fama and French asset‐pricing (FF) model based on the proxy factors fails to reject the model. However, these tests do not reveal strong evidence of significantly positive risk premiums, particularly in the case of the size and book‐to‐market factors.     

Constructing and Testing Alternative Versions of the Fama–French and Carhart Models in the UK

This paper constructs and tests alternative versions of the Fama–French and Carhart models for the UK market with the purpose of providing guidance for researchers interested in asset pricing and event studies. We conduct a comprehensive analysis of such models, forming risk factors using approaches advanced in the recent literature including value‐weighted factor components and various decompositions of the risk factors. We also test whether such factor models can at least explain the returns of large firms. We find that versions of the four‐factor model using decomposed and value‐weighted factor components are able to explain the cross‐section of returns in large firms or in portfolios without extreme momentum exposures. However, we do not find that risk factors are consistently and reliably priced.     

Do Mutual Funds Profit from the Accruals Anomaly?

Using data on both fund stockholdings and fund returns, we examine whether actively managed equity mutual funds trade on and profit from the accruals anomaly. We find that few, if any, mutual funds trade on the anomaly. The top 10% of mutual funds that have the highest portfolio weights in low‐accruals stocks have a greater, but still relatively small, exposure to low‐accruals stocks. Nonetheless, these funds make significant profit net of actual transaction costs, exhibiting an average Fama‐French three‐factor alpha of 2.83% per year. We also find that these funds are smaller, less diversified, and exhibit higher fund return volatility and higher fund flow volatility.     

In America's thrall: the effects of the US market and US security characteristics on Australian stock returns

Can Australian equity returns be modelled by ‘home‐grown’ factors? We examine the indigenous capital asset pricing model, the indigenous Fama–French three‐factor model, and extensions to the latter, and find them all wanting. We find evidence of domestic market segmentation in Australia. For the smallest firms, all the models we study fail. For the largest Australian firms, we find that the US Fama–French three factors (downloaded from French's website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ ) provide a successful model of Australian returns. It is as if the largest firms in the Australian market are simply part of the larger US market.     

INTANGIBLE ASSETS,BOOK‐TO‐MARKET,AND COMMON STOCK RETURNS

I examine two anomalies where the Fama and French three‐factor model fails to adequately explain monthly industry and index returns. Both anomalies are consistent with a bad model problem where the book‐to‐market factor introduces a negative bias in the intercepts. I propose the intangibles model as an alternative where the three‐factor model is known to have difficulty. This alternative model, which replaces the book‐to‐market factor with zero investment portfolio returns based on prior investments in intangible assets, is well specified in random samples, has comparable power, and fully explains both anomalies.     

Using the credit spread as an option-risk factor: Size and value effects in CAPM

This paper takes an option-theoretic approach to explain why pricing anomalies are observed when traditional CAPM is used. By extending CAPM to incorporate the option-risk factor of stocks, we show that stockholders’ limited liability can explain Fama and French’s size and value effects. We use bonds’ excess credit spread as a proxy for stocks’ default risk to control for the changing non-diversifiable option-risk characteristic of stocks. Because sensitivity to the excess credit spread becomes smaller as size increases and as value decreases, excess credit spread explains the CAPM anomalies in a fashion similar to the Fama–French factors. While the excess credit spread is significant in explaining Fama and French’s size and value effects, adding the Fama–French factors does not improve the performance of our model. Our revised model resembles conditional CAPM, but it offers a more intuitive explanation for the size and value effects.     

TRANSITORY MARKET STATES AND THE JOINT OCCURRENCE OF MOMENTUM AND MEAN REVERSION

In this article we derive and investigate the implications of the Fama–French and Poterba–Summers model—in which the market price of equity contains permanent and temporary components—to explain cross‐sectional differences in equity risk premia and returns. Shocks to the transitory component are regarded a Merton risk factor. We obtain estimates from a simple Kalman decomposition of the market price. The transitory component estimate is used in a conditional capital asset pricing model to test implications of the model related to predictability, cross‐sectional performance, and the existence of momentum and mean reversion.