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.     

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.     

Research and Development Activity and Expected Returns in the United Kingdom

Fama and French (1992) show that size and book-to-price dominate CAPM betaand other variables such as the price-earnings ratio and dividend yield in explainingthe cross-section of US stock returns. Comparable evidence for the UK points to abook-to-price effect, but not a size effect (Chan and Chui, 1996; Strong and Xu, 1997).In this paper, our first contribution is to show that a measure of research and development (RD) helps explain cross-sectional variation in UK stock returns. Our cross-sectional results on the association between stock returns and RD are consistent with recent US evidence reported by Lev and Sougiannis (1996, 1999) and Chan, Lakonishok and Sougiannis (2001). Fama and French (1993, 1995, 1996) also show that a three-factor model captures a high proportion of the time series variation in portfolio returns, again for the US. Our second contribution is to show, for the UK, that a modification to the three-factor model to take account of RD activity can significantly enhance the explanatory power of the three-factor model. We show that, as a practical matter, estimated risk premia based on the modified three-factor model can differ considerably from risk premia estimated using the CAPM or the three-factor model. In particular, risk premia for industries in whichfew firms undertake RD activities tend to be over-estimated.     

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.     

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.     

Short-term Contrarian Strategies in the London Stock Exchange: Are They Profitable? Which Factors Affect Them?

Abstract:  This paper provides evidence on short-term contrarian profits and their sources for the London Stock Exchange. Profits are decomposed to sources due to factors derived from the Fama and French (1996) three-factor model. For the empirical testing, size-sorted sub-samples are used, and adjustments for infrequent trading and bid-ask biases are also made. Results indicate that UK short-term contrarian strategies are profitable and more pronounced for extreme market capitalization stocks. These profits persist even when the sample is adjusted for market frictions, risk, seasonality, and irrespective of whether equally-weighted or value-weighted portfolios are employed. The most important factor that drives contrarian profits appears to be investor overreaction to firm-specific information.     

Testing multi-beta asset pricing models

This paper presents a complete solution to the estimation and testing of multi-beta models by providing a small sample likelihood ratio test when the usual normality assumption is imposed and an almost analytical GMM test when the normality assumption is relaxed. Using 10 size portfolios from January 1926 to December 1994, we reject the joint efficiency of the CRSP value-weighted and equal-weighted indices. We also apply the tests to analyze a new version of Fama and French's [Fama, E.F., French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33, 3–56.] three-factor model in addition to two standard ones, and find that the new version performs the best.     

Information-based trade in the Shanghai stock market

We show that the probability of information-based trade (PIN) played a significant role in explaining monthly returns on Shanghai A shares over the period 2001 to 2006. In particular, PIN, as approximated by order imbalance as a proportion of total transactions, appears to explain returns even after controlling for risk in the much-cited Fama and French [Fama, E. F. & French, K. R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, XLVII, 427–465.] three-factor model. However, we also find that some of the PIN effect appears to be indistinguishable from a turnover effect.     

THE FAMA‐FRENCH MODEL,LEVERAGE, AND THE MODIGLIANI‐MILLER PROPOSITIONS

For a cost‐of‐equity model to conform to the Modigliani‐Miller cost‐of‐capital propositions, any sensitivity coefficients in the model must be related to the firm's leverage. In this paper I apply these principles to the Fama‐French model for the cost of equity and develop the relation between its sensitivity coefficients and firm leverage. I then examine an empirical process developed by Fama and French (1997) to model the evolution through time of their sensitivity coefficients and show that this empirical process is inconsistent with the Modigliani‐Miller propositions. Separable functions are proposed for these sensitivity coefficients that are consistent with the Modigliani‐Miller propositions.     

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.     

Firm Size,Book-to-Market Ratio,and Security Returns: A Holdout Sample of Financial Firms

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.     

What do the Fama–French factors add to C-CAPM?

This study extends standard C-CAPM by including two additional factors related to firm size (SMB) and book-to-market value ratio (HML) — the Fama–French factors. C-CAPM is least able to price firms with low book-to-market ratios. The explanation of these returns, as well as the returns on the SMB and HML portfolios, is significantly improved by the inclusion of the HML factor. The component of the risk premia explained by consumption varies across size. We suggest that a possible explanation for the role of HML is its association with the investment growth prospects of firms.     

Foreign exchange volatility and stock returns

This paper explores whether foreign exchange volatility is a priced factor in the US stock market. Our investigation is motivated by a number of empirical as well as theoretical considerations. Empirically, Menkhoff et al. (2012) find that foreign exchange volatility is a pervasive factor across a variety of test assets. Theoretically, Shapiro (1974), Dumas (1978), and Levi (1990) imply that foreign exchange volatility can influence firms’ cash flow volatility therefore the discount rate. In terms of empirical implementation, we employ the cross-sectional regression methodology of Fama and MacBeth (1973) as well as the time-series regression approach of Fama and French (1996). For robustness, we also use the mimicking portfolio approach of Fama and French (1993). We find that foreign exchange volatility has no power to explain either the time-series or the cross-section of stock returns, which calls for more research on foreign exchange risk. Bartov et al. (1996) and Adrian and Rosenberg (2008) suggest an alternative and maybe promising direction.     

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.     

Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Information based trade,the PIN variable,and portfolio style differences: Evidence from Tokyo stock exchange firms

We investigate whether the variables related to information based trade proposed by Easley et al. [Easley, D., Kiefer, N.M., O'Hara, M., Paperman, J.B., 1996. Liquidity, information, and infrequently traded stocks. Journal of Finance 51, 1405–1436.] help explain the daily price discovery process in an electronically order-driven market of the Tokyo Stock Exchange using the microstructure tick data. We find strong evidence that the value firms show higher probability of bad news occurrences than the growth firms. We also find that the PIN is higher for smaller firms as is the case in the U.S. With the portfolio ranking tests and the Fama and MacBeth test we find that the alpha variable, which represents the information event occurrence rate, is systematically related to required returns, while the evidence related to the PIN is weaker. In the final Fama and MacBeth test, in which the PIN or alpha variable is used as an additional explanatory variable to the benchmark Fama and French three factor model, we find that the sign of the alpha variable supports our hypothesis that the arrival of new information reduces the risk of the stock, though not significantly. We also find that the higher PIN value increases the risk of the stock, at the same time it can marginally improve the explanatory power of the multifactor model. We conclude that the PIN variable cannot be a substitutable proxy variable for the book-to-market factor unlike in the U.S., and that it is strongly related to the size variable.     

Oil Risk Exposure: The Case of the U.S. Oil and Gas Sector

We estimate oil price risk exposures of the U.S. oil and gas sector using the Fama‐French‐Carhart's four‐factor asset pricing model augmented with oil price and interest rate factors. Results show that the market, book‐to‐market, and size factors, as well as momentum characteristics of stocks and changes in oil prices are significant determinants of returns for the sector. Oil price risk exposures of U.S. oil and gas companies in the oil and gas sector are generally positive and significant. Our study also finds that oil price risk exposures vary considerably over time, and across firms and industry subsectors.     

CAPM, Higher Co‐moment and Factor Models of UK Stock Returns

In this paper we examine the variables that explain the cross‐section of UK stock returns. Previous studies have found that the CAPM beta has moderate or even insignificant explanatory power once the Fama French factors are included. However, we control for different realised risk premia in up and down markets by using the same methodology as Pettengill, Sundaram and Mathur (1995). Unlike previous work, we find that beta is highly significant in explaining the cross‐section of UK stock returns and more importantly remains significant even when the Fama French factors are included in the cross‐sectional regressions. We also investigate whether higher co‐moments (co‐skewness and co‐kurtosis) have any explanatory power but find that empirical support is weaker.     

THE IMPORTANCE OF LIQUIDITY AS A FACTOR IN ASSET PRICING

We employ the Fama‐French time‐series regression approach to examine liquidity as a risk factor affecting stock returns. Prior studies establish liquidity as an important consideration in investment decisions. Here, liquidity is found to be an important factor affecting portfolio returns, even after the effects of market, size, book‐to‐market equity, and momentum are considered. Nonzero intercepts remain, however, indicating continued missing risk factors.     

Macroeconomic risks and characteristic-based factor models

We show that book-to-market, size, and momentum capture cross-sectional variation in exposures to a broad set of macroeconomic factors identified in the prior literature as potentially important for pricing equities. The factors considered include innovations in economic growth expectations, inflation, the aggregate survival probability, the term structure of interest rates, and the exchange rate. Factor mimicking portfolios constructed on the basis of book-to-market, size, and momentum therefore, serve as proxy composite macroeconomic risk factors. Conditional and unconditional cross-sectional asset pricing tests indicate that most of the macroeconomic factors considered are priced. The performance of an asset pricing model based on the macroeconomic factors is comparable to the performance of the Fama and French (1993) model. However, the momentum factor is found to contain incremental information for asset pricing.