Macroeconomic risk and the cross-section of stock returns

We develop a conditional version of the consumption capital asset pricing model (CCAPM) using the conditioning variable from the cointegrating relation among macroeconomic variables (dividend yield, term spread, default spread, and short-term interest rate). Our conditioning variable has a strong power to predict market excess returns in the presence of competing predictive variables. In addition, our conditional CCAPM performs approximately as well as Fama and French’s (1993) three-factor model in explaining the cross-section of the Fama and French 25 size and book-to-market sorted portfolios. Our specification shows that value stocks are riskier than growth stocks in bad times, supporting the risk-based story.     

A liquidity-augmented capital asset pricing model

Using a new measure of liquidity, this paper documents a significant liquidity premium robust to the CAPM and the Fama–French three-factor model and shows that liquidity is an important source of priced risk. A two-factor (market and liquidity) model well explains the cross-section of stock returns, describing the liquidity premium, subsuming documented anomalies associated with size, long-term contrarian investment, and fundamental (cashflow, earnings, and dividend) to price ratios. In particular, the two-factor model accounts for the book-to-market effect, which the Fama–French three-factor model fails to explain.     

Pricing innovations in consumption growth: A re-evaluation of the recursive utility model

We re-evaluate the cross-sectional asset pricing implications of the recursive utility function of  and , using innovations in future consumption growth in our tests. Our empirical specification helps explain the size, value and momentum effects. Specifically, we find that (?) the beta associated with news about consumption growth has a systematic pattern: beta decreases along the size dimension and increases along the book-to-market and momentum dimensions, (??) innovation in consumption growth is significantly priced in asset returns using both the Fama and MacBeth (1973) and the stochastic discount factor approaches, and (???) the model performs better than both the CAPM and Fama–French model.     

Common risk factors in bank stocks

This paper provides evidence on the risk factors that are priced in bank equities. Alternative empirical models with precedent in the nonfinancial asset pricing literature are tested, including the single-factor CAPM, three-factor Fama–French model, and ICAPM. Our empirical results indicate that an unconditional two-factor ICAPM model that includes the stock market excess return and shocks to the slope of the yield curve is useful in explaining the cross-section of bank stock returns. However, we find no evidence that firm specific factors such as size and book-to-market ratios are priced in bank stock returns. These results have a number of important implications for the estimation of the banks’ cost of capital as well as regulatory initiatives to utilize market discipline to evaluate bank risk under Basel II.     

Do relative leverage and relative distress really explain size and book-to-market anomalies?

In a capital asset pricing model (CAPM) framework, Ferguson and Shockley [2003. Equilibrium “anomalies”. Journal of Finance 58, 2549–2580] propose two factors constructed on relative leverage and relative distress, and show that the two factors subsume Fama and French's [1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56] factors constructed on size and book-to-market (BM) in explaining the cross-sectional average returns of the 25 size-BM portfolios. Based on tests on individual securities, we find that all factors fail to fully explain the common asset-pricing anomalies. In the spirit of Merton's [1973. An intertemporal capital asset pricing model. Econometrica 41, 867–887] intertemporal CAPM, we propose an augmented five-factor model, which incorporates Ferguson and Shockley's [2003. Equilibrium “anomalies”. Journal of Finance 58, 2549–2580] factors into the Fama–French three-factor model. The empirical results show that a simple conditional version of the augmented model is able to explain most well-known asset-pricing anomalies.     

Testing conditional asset pricing models: An emerging market perspective

The CAPM as the benchmark asset pricing model generally performs poorly in both developed and emerging markets. We investigate whether allowing the model parameters to vary improves the performance of the CAPM and the Fama–French model. Conditional asset pricing models scaled by conditioning variables such as Trading Volume and Dividend Yield generally result in small pricing errors. However, a graphical analysis reveals that the predictions of conditional models are generally upward biased. We demonstrate that the bias in prediction may be the consequence of ignoring frequent large variation in asset returns caused by volatile institutional, political and macroeconomic conditions. This is characterised by excess kurtosis. An unconditional Fama–French model augmented with a cubic market factor performs the best among some competing models when local risk factors are employed. Moreover, the conditional models with global risk factors scaled by global conditioning variables perform better than the unconditional models with global risk factors.     

What explains the investment growth anomaly?

We examine if an existing asset pricing model in an unconditional or conditional setting can explain the investment growth anomaly, as represented by higher returns on stocks of the firms with lower growth in capital expenditures. Our results indicate that the conditional Fama–French 3-factor model that allows factor loadings to be time-varying and further linked to firm-level characteristics and the business cycle can explain the anomaly.     

Estimation of expected return: CAPM vs. Fama and French

Most practitioners favour a one-factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns, academics recommend the Fama and French three-factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that 5 years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However, performance of the model is very poor; it explains on average 3% of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using 5 years of monthly data. This model, however, does not do much better; independent of the index used, it explains on average 5% of differences in returns. These results therefore bring into question the use of either model for estimation of individual expected stock returns.     

A Comparison of the Efficacy of Liquidity,Momentum, Size and Book‐to‐Market Value Factors in Equity Pricing on a Heterogeneous Sample: Evidence from Asia

This paper compares the size and book‐to‐market value factors of Fama and French (1993) alongside Momentum of Jagadeesh and Titman ( 1993 ) with two Liu ( 2006 ) liquidity factors formed from 1 year rebalancing and 1 month rebalancing respectively. A heterogeneous and comprehensive sample of the top blue chip stocks of all national Asian equity markets with further differentiation undertaken between sub samples formed for Japan only and Asia excluding Japan for period January 2000 to August 2014. Our empirical results suggest that multifactor time invariant pricing models based on augmented capital asset pricing model (CAPM) framework are ineffective in explaining the cross section of stock returns in the presence of significant inter and intra‐market segmentation. However an alternative model specification based on a time varying parameter specification and using same sets of factors yields significant enhancements in explaining cross section of stock returns across universe. We find that momentum factor largely lacks significance while a time varying two factor model, based on CAPM plus liquidity factor, is optimal. The liquidity factor being that of Liu (2006) and annually rebalanced. Our findings are important for investment managers seeking appropriate factors and modelling techniques to hedge against risks as well as firm's financial managers seeking to reduce costs of equity capital.     

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.     

Evaluating asset pricing models in the Korean stock market

This paper evaluates and compares asset pricing models in the Korean stock market. The asset pricing models considered are the CAPM, APT-motivated models, the Consumption-based CAPM, Intertemporal CAPM-motivated models, and the Jagannathan and Wang conditional CAPM model. By using various test portfolios as well as individual stocks, we conduct time-series tests and cross-sectional regression tests based on individual t-tests, the joint F-tests, the Hansen and Jagannathan (1997) distance, and R-squares. Overall, the Fama and French (1993) five-factor model performs most satisfactorily among the asset pricing models considered in explaining the intertemporal and cross-sectional behavior of stock returns in Korea. The Fama and French (1993) three-factor model, the Chen et al. (2010) three-factor model, and the Campbell (1996) model are the next. The results indicate that the two bond portfolios, term spread and default spread, play an important role in explaining stock returns in Korea.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

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.     

New Paradigms in Stock Market Indexing

Considerable recent interest has been shown in a new set of stock‐market indices that are weighted by fundamental factors such as sales, earnings, dividends or book values, rather than by capitalization. In this paper, we analyze the performance of Fundamental Indexing? (“FI”). First, we show that the source of FI's recent excellent performance is not from its ability to systematically arbitrage mispricing in a noisy market but from increasing the portfolio's exposure to stocks with low price‐to‐book values and with small capitalizations. We find that FI does not produce a positive alpha when its excess returns are explained by the Fama‐French three‐factor model of CAPM beta, the value premium and the size premium. Second, we show that it is possible to construct a portfolio of exchange‐traded funds with similar factor loadings that can replicate, and sometimes, even outperform FI. However, we caution investors not to expect consistent outperformance from portfolios tilted towards value and small‐cap stocks. Historical data shows evidence of mean reversion in the performance of such strategies.     

Do zero-cost portfolios have the ability to predict economic growth? Evidence from Hong Kong, South Korea and Taiwan

In this paper we investigate the relationship between growth in future Gross Domestic Product (GDP) and Industrial Production (IDP) and the performance of SMB (small stocks minus big stocks) and HML (High book-to-market stocks minus low book-to-market stocks) portfolios for equities listed in Hong Kong, South Korea and Taiwan.We find evidence to suggest that: (a) the excess market return is positively related to future GDP or IDP growth in South Korea and Taiwan; (b) contrary to most European markets, Australia, Japan and the US, future economic growth is in general significantly negatively related to SMB in Hong Kong and South Korea; and, (c) a negative relationship between future economic growth and HML for Hong Kong. Our results cast doubt if SMB and HML portfolios are positive risk factors in the Fama and French (Fama, E. F., and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56) three-factor asset pricing model for Hong Kong, South Korea and Taiwan.     

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.     

Are the Fama–French factors proxying news related to GDP growth? The Australian evidence

Inspired by Vassalou (J Financ Econ 68:47–73, 2003), we investigate the contention that the Fama and French (J Financ Econ 33:3–56, 1993) model’s ability to explain the cross sectional variation in equity returns is because the Fama–French factors are proxying for risk associated with future GDP growth in the Australian equities market. To assess the validity of Vassalou’s findings, we augment the CAPM and the Fama–French model with a GDP growth factor and run system regressions of the GDP-enhanced models using the GMM approach. Our results suggest that news about future GDP growth is not priced in equity returns and that any ability that SMB and HML exhibit in explaining equity returns is not because they contain information about future GDP growth.

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.     

中国股票市场流动性风险溢价研究

本文在对Fama三因素模型和LACAPM模型进行改进的基础上,实证研究了中国股票市场的流动性风险溢价、规模效应以及价值效应。实证结果发现,改进的FAMA三因素模型能够比CAPM更好地解释价值效应,但却不能解释规模效应和流动性风险溢价现象;而改进的LACAPM在解释市场异象上的有效性则明显优于其他定价模型。     

Stock price reaction following large one-day price changes: UK evidence

We examine the short-term price reaction of 424 UK stocks to large one-day price changes. Using the GJR-GARCH(1,1), we find no statistical difference amongst the cumulative abnormal returns (CARs) of the Single Index, the Fama–French and the Carhart–Fama–French models. Shocks ?5% are followed by a significant one-day CAR of 1% for all the models. Whilst shocks ?−5% are followed by a significant one-day CAR of −0.43% for the Single Index, the CARs are around −0.34% for the other two models. Positive shocks of all sizes and negative shocks ?−5% are followed by return continuations, whilst the market is efficient following larger negative shocks. The price reaction to shocks is unaffected when we estimate the CARs using the conditional covariances of the pricing variables.