IS THE BOOK‐TO‐MARKET RATIO A MEASURE OF RISK?

We develop a leverage‐based alternative to traditional asset pricing models to investigate whether the book‐to‐market ratio acts as a proxy for risk. We argue that the book‐to‐market ratio should act as a proxy because of the expected relations between (1) financial risk and measures of capital structure based on the market value of equity and (2) asset risk and measures of capital structure based on the book value of equity. We find no relation between average stock returns and the book‐to‐market ratio in all‐equity firms after controlling for firm size, and an inverse relation between average stock returns and the book‐to‐market ratio in firms with a negative book value of equity.     

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.     

Interaction of size,book‐to‐market and momentum effects in Australia

This study seeks to disentangle the effects of size, book‐to‐market and momentum on returns. Initial results show that each characteristic has a role in explaining returns, but that there is interaction between size and momentum, as well as between size and book‐to‐market. Three key findings emerge. First, the size premium is the strongest, particularly in the loser portfolios. Second, the value premium is generally limited to the smallest portfolios. Third, the momentum premium is evident for the large‐ and middle‐sized portfolios, but loser stocks significantly outperform winner stocks in the smallest size portfolio. When these interactions are controlled with multivariate regression, we find a significant negative average relation between size and returns, a significant positive average relation between book‐to‐market and returns, and a significant positive average relation between momentum and 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.     

Profitability,Value, and Stock Returns in Production‐Based Asset Pricing without Frictions

In a production‐based asset pricing model without adjustment costs and with decreasing returns to scale following Brock (1982), stock returns at the firm level are determined by profitability, the book‐to‐market ratio, and the change in future profitability prospects. Although firms with low book‐to‐market ratios are normally more profitable and profitable firms are predicted to have higher returns, the stylized fact that book‐to‐market ratios positively forecast returns still holds theoretically, but with specific predicted exceptions. These implications are confirmed empirically.     

Bad beta good beta,state‐space news decomposition and the cross‐section of stock returns

This study employs an innovative market‐based approach, where return on equity (ROE) is employed as a proxy for cash‐flow news and a state‐space model is used for market news decomposition. We document that the bad beta good beta (BBGB) model of Campbell and Vuolteenaho (2004) explains about 30 per cent of the cross‐sectional variations in US stock returns. We also find that the BBGB model adequately explains the size effect leading to its superior performance in this area. Our method controls for the news decomposition method and market news proxies’ bias. We contribute to the literature by providing an alternative easy‐to‐implement and consistent market‐based method for news decomposition.     

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.     

APPLICABILITY OF THE FAMA‐FRENCH THREE‐FACTOR MODEL IN FORECASTING PORTFOLIO RETURNS

For the model‐based estimation of the equity cost of capital, evidence shows that the common practice of using the average historical factor premiums as the estimates of the next‐period factor premiums generates inaccurate estimates. I propose an alternative way to estimate factor premiums by using the structural variables that are important predictors of future asset returns. Based on the out‐of‐sample results from a trading strategy with four in‐sample model‐selection criteria, I find that my estimation procedure performs better than the common practice even when transaction costs are considered.     

UNDERSTANDING SIZE AND THE BOOK‐TO‐MARKET RATIO: AN EMPIRICAL EXPLORATION OF BERK'S CRITIQUE

Because they are scaled by price, the ability of size (i.e., the market capitalization of a firm) and the book‐to‐market equity ratio to determine expected returns may, according to Berk (1995) , reflect only a simultaneity bias. The two‐stage least squares approach is used to control for this bias and to investigate the economic meanings of these variables. We discover that size and the book‐to‐market ratio contain distinct and significant components of financial distress, growth options, the momentum effect, liquidity, and firm characteristics. Our findings support Berk in his contention that that size and the book‐to‐market ratio reflect a combination of different economic mechanisms that are misspecified in the expected return process.     

The Three‐Factor Model: A Practitioner's Guide

The three‐factor model (3FM) has slowly but steadily become a popular alternative to the CAPM for measuring risk from the perspective of both corporate finance and portfolio management. The evidence clearly shows a negative relationship between market capitalization and returns, and a positive relationship between the book‐to‐market ratio and returns. Under the assumption that size and value are risk factors, the 3FM incorporates a market risk premium, a size premium, and a value premium into a model that aims to assess risk in a more comprehensive way, and ultimately to provide a more reliable estimation of required return. The required return produced by the 3FM has corporate finance applications (such as cost of capital estimation, project evaluation, and firm valuation) as well as portfolio management applications (such as performance evaluation). This article discusses the foundations and intuition behind the 3FM, as well as its application to the estimation of the cost of equity and excess returns.     

Tangibility and investment irreversibility in asset pricing

Zhang (2005) and Cooper (2006) provide a theoretical risk‐based explanation for the value premium by suggesting a nexus between firms’ book‐to‐market ratio and investment irreversibility. They argue that unproductive physical capacity is costly in contracting conditions but provides growth opportunities during economic expansions, resulting in covariant risk between firms’ investment in tangible assets and market‐wide returns. This article uses the Australian accounting environment to empirically test this theory – a test that is not possible using US data. Consistent with the theoretical argument, tangibility is priced in equity returns, and augmenting the Fama and French three‐factor model with a tangibility factor increases model explanatory power.     

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.     

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.     

Can tail risk explain size,book‐to‐market,momentum, and idiosyncratic volatility anomalies?

We examine the impact of tail risk on the return dynamics of size, book‐to‐market ratio, momentum and idiosyncratic volatility sorted portfolios. Our time‐series analyses document significant portfolio return exposures to aggregate tail risk. In particular, portfolios that contain small, value, high idiosyncratic volatility and low momentum stocks exhibit negative and statistically significant tail risk betas. Our cross‐sectional analyses at the individual stock level suggest that tail risk helps in explaining the four pricing anomalies, particularly size and idiosyncratic volatility anomalies.     

Real‐Time Forecasting and Political Stock Market Anomalies: Evidence for the United States

Using monthly data from 1953 to 2003, we apply a real‐time modeling approach to investigate the implications of U.S. political stock market anomalies for forecasting excess stock returns in real‐time. Our empirical findings show that political variables, chosen on the basis of widely used model‐selection criteria, are often included in real‐time forecasting models. However, political variables do not contribute systematically to improving the performance of simple trading rules. For this reason, political stock market anomalies are not necessarily an indication of market inefficiency.     

On the Stability of the Cross‐Section of Expected Stock Returns in the Cross‐Section: Understanding the Curious Role of Share Turnover

In this paper, we shed further light on cross‐sectional predictors of stock return performance. Specifically, we explore whether the cross‐section of expected stock returns is robust within stock groups sorted by past monthly return. We find that the book/market and momentum effects are remarkably robust to sorting on past returns. However, share turnover is negatively related to future returns for stocks with abnormally low stock price performance in the recent past, but postively related to returns for well‐performing stocks. This casts doubt on the use of turnover as a liquidity proxy, but is consistent with turnover being a proxy for momentum trading which pushes prices in the direction of past price movements. Our results are robust to both NYSE/AMEX and Nasdaq stocks, and also robust to stratifying the sample by time period.     

Characterizing the Risk of IPO Long‐Run Returns: The Impact of Momentum,Liquidity, Skewness,and Investment

We study 6,686 initial public offerings (IPOs) spanning the period 1981‐2005 and find that the new issues puzzle disappears in a Fama‐French three‐factor framework. IPOs do not underperform in the aftermarket on a risk‐adjusted basis and do not underperform a matched sample of nonissuers. IPO underperformance is concentrated in the 1980s and early 1990s, and IPOs either perform the same as the market or outperform on a risk‐adjusted basis from 1998 to 2005. We find that outperformance in the later period is driven by large firms. Factors for momentum, investment, liquidity, and skewness help to explain aftermarket returns, although size and book‐to‐market tend to proxy for skewness. IPO investors receive smaller expected returns due to negative momentum and investment exposure and in exchange for higher liquidity.     

DAYLIGHT AND INVESTOR SENTIMENT: A SECOND LOOK AT TWO STOCK MARKET BEHAVIORAL ANOMALIES

Kamstra, Kramer, and Levi (2000, 2003) describe two stock market behavioral anomalies associated with changes in investor sentiment caused by daylight saving time (DST) changes and seasonal affective disorder (SAD). According to the hypothesized effects, DST changes and SAD affect asset prices by changing investors’ risk aversion. Although changes in the timing or amount of daylight are correlated with unusual stock market returns, I present evidence they do not cause those unusual returns. Instead, seasonal patterns in market‐related information during the sample period are the likely cause of the correlation between stock market returns and DST changes or SAD.     

Partial Price Adjustments and Equity Carve‐Outs

We examine the extent to which market‐adjusted ex date returns reflect public information for 271 equity carve‐outs in 1988–2006. Although prior studies focus on ex post determinants of equity carve‐out and initial public offering returns, our study is the first to explore ex ante predictors of equity carve‐out returns. We use three primary variables: filing range adjustments, the percentage of the offering used to retire subsidiary debt or to pay dividends, and the CBOE Volatility Index (VXO) to predict initial returns. We show that 11–35% of the variation in market‐adjusted equity carve‐out returns can be predicted using public information known prior to the offer date.     

Investment Dynamics and Earnings‐Return Properties: A Structural Approach

We propose the standard neoclassical model of investment under uncertainty with short‐run adjustment frictions as a benchmark for earnings‐return patterns absent accounting influences. We show that our proposed benchmark generates a wide range of earnings‐return patterns documented in accounting research. Notably, our model generates a concave earnings‐return relation, similar to that of Basu [1997], and predicts that the earnings‐return concavity increases with the volatility of firms’ underlying shock processes and decreases with the level of firms’ investments. We find strong empirical support for these predictions. Overall, our evidence suggests that our proposed benchmark is useful for understanding the joint dynamics of variables of interest to accounting research (e.g., earnings, returns, investment, market‐to‐book) absent accounting influences, a necessary precondition for inferring the effects of accounting from these dynamics.