UK Evidence on the Characteristics versus Covariance Debate

We evaluate the Fama–French three‐factor model in the UK using the approach of Daniel and Titman (1997) to determine whether characteristics or covariance risk better explains the size and value premiums. Across all three factors, we find that return premiums bear little relationship to the corresponding loadings. We show that small and value stocks earn higher returns irrespective of their return covariance. Our study contributes to the existing literature by reporting original findings on the Fama–French three‐factor model in the UK and by reporting results that complement existing evidence from similar studies in the USA and Japan.     

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.     

Does Idiosyncratic Risk Matter for Individual Securities?

This paper investigates the relationship between idiosyncratic risk and returns for individual securities within a generalized autoregressive conditional heteroskedascticity (GARCH)‐in‐mean framework. We demonstrate that, on average, 15% of stocks exhibit a significant relationship between returns and risk, of which 9% are positive. These proportions vary over time and with model specifications. Some characteristics influence the probability of a positive and a negative relationship, while others appear to affect only one, but not the other. This evidence implies that the factors that explain a positive connection between idiosyncratic risk and returns are different from the factors that explain a negative connection.     

The risk in hedge fund strategies: Theory and evidence from long/short equity hedge funds

Theory suggests that long/short equity hedge funds' returns come from directional as well as spread bets on the stock market. Empirical analysis finds persistent net exposures to the spread between small vs large cap stocks in addition to the overall market. Together, these factors account for more than 80% of return variation. Additional factors are price momentum and market activity. Combining two major branches of hedge fund research, our model is the first that explicitly incorporates the effect of funding (stock loan) on alpha. Using a comprehensive dataset compiled from three major database sources, we find that among the three thousand plus hedge funds with similar style classification, less than 20% of long/short equity hedge funds delivered significant, persistent, stable positive non-factor related returns. Consistent with the predictions of the Berk and Green (2004) model we find alpha producing funds decays to “beta-only” over time. However, we do not find evidence of a negative effect of fund size on managers' ability to deliver alpha. Finally, we show that non-factor related returns, or alpha, are positively correlated to market activity and negatively correlated to aggregate short interest. In contrast, equity mutual funds and long-bias equity hedge funds have no significant, persistent, non-factor related return. Expressed differently, L/S equity hedge funds, as the name suggests, do benefit from shorting. Besides differences in risk taking behavior, this is a key feature distinguishing L/S funds from long-bias funds.     

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.     

Monetary Policy and the Cross-Section of Expected Stock Returns

Ample evidence shows that size and book-to-market equity explain significant cross-sectional variation in stock returns, whereas beta explains little or none of the variation. Recent studies also demonstrate that proxies for monetary stringency increase the explained variation in stock returns. We reexamine a three-factor model that includes beta, size, and book-to-market equity, while allowing monetary conditions to influence the relations between these risk factors and average stock returns. We find that ex-ante proxies for monetary stringency significantly influence the relations between stock returns and all three risk factors. Additionally, all three variables are found to contribute significantly to explaining cross-sectional returns in a three-factor model that includes the monetary sector.     

Abnormal Returns from Takeover Prediction Modelling: Challenges and Suggested Investment Strategies

While takeover targets earn significant abnormal returns, studies tend to find no abnormal returns from investing in predicted takeover targets. In this study, we show that the difficulty of correctly identifying targets ex ante does not fully explain the below‐expected returns to target portfolios. Target prediction models’ inability to optimally time impending takeovers, by taking account of pre‐bid target underperformance and the anticipation of potential targets by other market participants, diminishes but does not eliminate the potential profitability of investing in predicted targets. Importantly, we find that target portfolios are predisposed to underperform, as targets and distressed firms share common firm characteristics, resulting in the misclassification of a disproportionately high number of distressed firms as potential targets. We show that this problem can be mitigated, and significant risk‐adjusted returns can be earned, by screening firms in target portfolios for size, leverage and liquidity.     

The Return of the Size Anomaly: Evidence from the German Stock Market

This paper examines the size effect in the German stock market and intends to address several unanswered issues on this widely known anomaly. Unlike recent evidence of a reversal of the size anomaly this study documents a conditional relation between size and returns. I also detect strong momentum across size portfolios. The results indicate that the marginal effect of firm size on stock returns is conditional on the firm's past performance. I use an instrumental variable estimation to address Berk's critique of a simultaneity bias in prior studies on the small firm effect and to investigate the economic rationale behind firm size as an explanatory variable for the variation in stock returns. The analysis in this paper indicates that firm size captures firm characteristic components in stock returns and that this regularity cannot be explained by differences in systematic risk.     

Is liquidity the missing link?

Durand et al. (2006a ) argue that the Australian market is both internationally integrated and domestically segmented. They find that the US‐based three‐factor model captures returns of the largest stocks in Australia (evidence of international integration), but that it is unable to account for the returns of the smallest stocks (evidence of domestic segmentation). This study resolves the puzzle left by Durand et al. (2006a) . Incorporating a liquidity factor provides the missing link in their analysis: it results in a model that permits both the international integration of the largest stocks and the model can account for the returns of the smallest stocks. Our analysis highlights the important role of liquidity in Australian asset pricing.     

Anomalies: Is it the Economy?

There is still no consensus on whether small firm or value stock anomalies exist. We examine the last half of the 20th century and apply a six‐factor macroeconomic model to test for the presence of these abnormal returns. Using four proxies for value, we find that detecting this anomaly is sensitive to choice of proxy, the magnitude of the abnormal returns varies over time, and the anomaly does not persist through time. Additional tests provide evidence that abnormal returns for small, value‐oriented, and growth‐oriented firms differ significantly under restrictive versus expansive monetary policy regimes.     

Option‐Adjusted Delta Credit Spreads: a Cross‐Country Analysis

This study analyses the determinants of the variation in option‐adjusted credit spreads (OASs) using a unique database and enlarges the traditional analysis to include disaggregated indexes, new variables, and a complete set of markets (USA, UK, and the Eurozone). An extended set of regressors explains almost half the variability of OASs in the three markets. We find that institutional trading activity significantly affects corporate bond spreads, signalling either variation in perceptions of risk or the existence of an indirect measure of liquidity. We also find that US business cycle indicators significantly affect the variability of OASs in the UK and the Eurozone. Finally, we find evidence that stock returns have more influence on high‐yield bonds in the Eurozone than in the USA.     

Risk factors in Australian bond returns

This study examines the risk factors in Australian bond returns. The study quantifies bond liquidity and estimates a liquidity risk factor in the Australian setting. We develop a three‐factor asset pricing framework that uses term, default and liquidity risk factors to explain the variation of Australian bond returns. Our findings corroborate the US evidence on the pervasiveness of these risk factors faced by bond investors. The three‐factor model developed in this study has practical applications when calculating the cost of debt, evaluating the performance of an active bond fund manager and hedging underlying risk in a bond portfolio.     

Hedge Fund Regulation and Misreported Returns

This paper introduces a cross‐country law and finance analysis of the misreporting behaviour in the hedge fund industry in terms of smoothing returns so that a fund consistently generates positive returns. We find strong evidence that international differences in hedge fund regulation are significantly associated with the propensity of fund managers to misreport monthly returns. We find a positive association between wrappers and misreporting, particularly for funds that do not have a lockup provision. Also, we find some evidence that misreporting is less common among funds in jurisdictions with minimum capitalisation requirements and restrictions on the location of key service providers. We assess the robustness of our finds to a number of specifications, including, different specifications of misreporting bin widths, subsets of the data by fund type, as well as specifications controlling for collinearity and selection effects and other robustness checks. We show misreporting significantly affects capital allocation, and calculate the wealth transfer effects of misreporting and relate this wealth transfer to differences in hedge fund regulation.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

The Australian asset‐pricing debate

Utilising a comprehensive data set for Australian firms, we examine a range of competing asset‐pricing models, including the four‐ and five‐factor models where the equity‐risk premium is augmented by size, value, momentum and liquidity premia, and find that none of the models tested appears to adequately explain the cross section of Australian returns. A model accounting for Australia's integration with the US equity market appears to be the best of the competing models we study. Our argument that a model recognising Australia's integration with the USA is supported when we apply the portfolio and factor construction methodology suggested by Brailsford et al. (2012a,b).     

Returns to scale and asset prices

The q‐theory of investment is proposed to explain firm growth effects, where previous papers identify a negative effect of firm growth, including asset growth, real investment and net share issuance, on future stock returns. This paper uses returns to scale from the production function to test the dynamic q‐theory, which predicts that the firm growth effect is theoretically weaker for firms with decreasing returns to scale (DRS) than for non‐DRS firms. Our empirical results generally support the prediction of dynamic q‐theory. However, we find that the dynamic q‐theory explains little of the value, momentum and ROE effects from the standpoint of returns to scale.     

Contrarian Profits and the Overreaction Hypothesis: the Case of the Athens Stock Exchange

This paper investigates the existence of contrarian profits and their sources for the Athens Stock Exchange (ASE). The empirical analysis decomposes contrarian profits to sources due to common factor reactions, overreaction to firm‐specific information, and profits not related to the previous two terms, as suggested by Jegadeesh and Titman (1995). Furthermore, in view of recent evidence that common stock returns are related to firm characteristics such as size and book‐to‐market equity, the paper decomposes contrarian profits to sources due to factors derived from the Fama and French (1993, 1996) three‐factor model. For the empirical testing, size‐sorted sub‐samples that are rebalanced annually are employed, and in addition, adjustments for thin and infrequent trading are made to the data. The results indicate that serial correlation is present in equity returns and that it leads to significant short‐run contrarian profits that persist even after we adjust for market frictions. Consistent with findings for the US market, contrarian profits decline as one moves from small stocks to large stocks, but only when market frictions are considered. Furthermore, the contribution to contrarian profits due to the overreaction to the firm‐specific component appears larger than the underreaction to the common factors.     

Systematic risk and the cross section of hedge fund returns

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

Risk‐based explanation for the book‐to‐market effect

This paper proposes a risk‐based explanation for the book‐to‐market (B/M) effect. I decompose B/M into net operating asset‐to‐market (NOA/M) and net financing asset‐to‐market (NFA/M) components. Portfolio analysis shows that (i) positive B/M, NOA/M and NFA/M are positively related to future returns and (ii) negative B/M, NOA/M and NFA/M are negatively related to future returns. To the extent that positive B/M, NOA/M and NFA/M act as measures of asset risk and negative B/M, NOA/M and NFA/M act as inverse measures of borrowing risk, the nonlinear relations between B/M, NOA/M and NFA/M and future returns provide some evidence to support the risk‐based explanation for the book‐to‐market effect in stock returns.