Cross-section Of Average Stock Returns Research Articles

Systematic default and return predictability in the stock and bond markets

We construct a measure of systematic default defined as the probability that many firms default at the same time. We account for correlations in defaults between firms through exposures to common shocks. Systematic default spikes during recessions, is correlated with macroeconomic indicators, and predicts future realized defaults. More importantly, it predicts future equity and corporate bond index returns both in- and out-of-sample. Finally, we find that the cross-section of average stock returns is related to firm-level exposures to systematic default risk.

Evaluating the Conditional CAPM using Consumption-based State Variables: Evidence from the Korean Stock Market

In this study, we evaluate the empirical performance of conditional asset pricing models using consumption-based measures as state variables. We incorporate three consumption variables known to forecast the equity risk premium as conditioning variables to capture time variations in the risk premium. These three variables are the consumption-aggregate wealth ratio, the surplus consumption ratio, and the labor income to consumption ratio. The asset pricing models evaluated in this study are the CAPM, the CAPM with human capital, the consumption CAPM, and the Fama-French three-factor model. We compare the unconditional and conditional specifications of these four asset pricing models using the two-pass cross-sectional regression methodology, using the size, book-to-market, turnover, and idiosyncratic risk sorted portfolios and sector portfolios as test assets. We demonstrate that the conditional CAPM with human capital performs far better than the unconditional specifications and about as well as the Fama and French three-factor model in explaining the crosssection of average stock returns in Korea.

Asset pricing implications of money: New evidence

We provide new evidence on the role of real money balances in terms of explaining equity risk premia by using a rich cross-section of average stock returns (associated with 11 major CAPM anomalies). By estimating Euler equations associated with a cash-in-advance (CIA) model, we find that such model produces substantially smaller pricing errors than the baseline consumption model, while still generating lower estimates of the risk aversion coefficient. The estimates of the parameter governing the share of cash goods are highly significant and plausible in economic terms. A transaction-costs model and a money-in-the-utility model perform considerably worse than the CIA model, both in terms of statistical fit and in terms of the plausibility of the structural parameter estimates. Moreover, a linear version of the CIA model also largely underperforms the corresponding non-linear model.

Q-Factors and Investment CAPM

The Hou–Xue–Zhang <italic>q</italic>-factor model says that the expected return of an asset in excess of the risk-free rate is described by its sensitivities to the market factor, a size factor, an investment factor, and a return on equity (ROE) factor. Empirically, the <italic>q</italic>-factor model shows strong explanatory power and largely summarizes the cross-section of average stock returns. Most important, it fully subsumes the Fama–French 6-factor model in head-to-head spanning tests. The <italic>q</italic>-factor model is an empirical implementation of the investment-based capital asset pricing model (the Investment CAPM). The basic philosophy is to price risky assets from the perspective of their suppliers (firms), as opposed to their buyers (investors). Mathematically, the investment CAPM is a restatement of the net present value (NPV) rule in corporate finance. Intuitively, high investment relative to low expected profitability must imply low costs of capital, and low investment relative to high expected profitability must imply high costs of capital. In a multiperiod framework, if investment is high next period, the present value of cash flows from next period onward must be high. Consisting mostly of this next period present value, the benefits to investment this period must also be high. As such, high investment next period relative to current investment (high expected investment growth) must imply high costs of capital (to keep current investment low). As a disruptive innovation, the investment CAPM has broad-ranging implications for academic finance and asset management practice. First, the consumption CAPM, of which the classic Sharpe–Lintner CAPM is a special case, is conceptually incomplete. The crux is that it blindly focuses on the demand of risky assets, while abstracting from the supply altogether. Alas, anomalies are primarily relations between firm characteristics and expected returns. By focusing on the supply, the investment CAPM is the missing piece of equilibrium asset pricing. Second, the investment CAPM retains efficient markets, with cross-sectionally varying expected returns, depending on firms’ investment, profitability, and expected growth. As such, capital markets follow standard economic principles, in sharp contrast to the teachings of behavioral finance. Finally, the investment CAPM validates Graham and Dodd’s security analysis on equilibrium grounds, within efficient markets.

Multifactor Models and the APT: Evidence from a Broad Cross-Section of Stock Returns

We seek to describe the broad cross-section of average stock returns. We follow the APT literature and estimate the common factor structure among a large cross-section containing 278 decile portfolios associated with 28 market anomalies. Our statistical model contains seven common factors (with an economic meaning) and prices well both the original portfolio returns and an efficient combination of these portfolios. This model clearly outperforms the empirical workhorses in the literature when it comes to pricing this broad cross-section. Augmenting the empirical models with new factor-mimicking portfolios, based on APT principles, significantly improves their performance. Our results shed light on the number of factors necessary to describe expected stock returns. Moreover, we show that there is significant room for improving the existing multifactor models in terms of explaining the large cross-section of stock returns (and in a way that is consistent with the APT).

The four-factor model and stock returns: evidence from Sri Lanka

There have been numerous studies that have attempted to explain the cross-sectional variation in average returns in developed and emerging markets. However, there is a dearth in the published evidence of research that has looked at frontier markets regarding this aspect. Sri Lanka is considered to be a frontier market and hence the objective of this study is to test the ability of the Carhart four-factor model to explain the variation in the cross-section of average stock returns in the Colombo Stock Exchange (CSE) and to evaluate it in comparison to the capital asset pricing model (CAPM) and the Fama and French three-factor model. The study finds that the four-factor model, incorporating the market factor, size factor, value factor and momentum factor, provides a satisfactory explanation of the variation in the cross-section of average stock returns in the CSE. Further, it is found that the four-factor model performs better than the CAPM and the three-factor model.

The Impact of the Financial Sector on Asset Pricing Tests: Evidence from the Colombo Stock Exchange

&lt;p&gt;This paper aims to identify how the inclusion of financial sector affects the ability of asset pricing models to explain the average stock returns in the CSE. Most of the asset pricing researches, the firms in the financial sector are excluded on the basis that their characteristics and the leverage are notably different than firms in other industries. Therefore the objective of this study is to identify the impact of the inclusion of financial sector on the ability of the Carhart four-factor model to explain the average stock returns in the CSE and to compare its performance with the Capital Asset Pricing Model (CAPM) and the Fama and French three-factor model. The study finds that the four-factor model; incorporating the market premium, size premium, value premium and momentum premium provides a satisfactory explanation of the variation in the cross-section of average stock returns in the CSE, even when the financial sector is included. It is found that the Carhart four-factor model performs better than the CAPM in all scenarios; and that it performs notably better than the Fama and French three-factor model.However, there is no notable difference in the findings either the financial sector is included or not. &lt;/p&gt;

Firm Characteristics, Cross-Sectional Regression Estimates, and Intertemporal Asset Pricing Tests

Researchers typically employ cross-sectional regression methods to identify firm-level characteristics that help to explain the cross-section of average stock returns. I develop a straightforward approach for testing whether the coefficient estimates produced by these methods satisfy the pricing restrictions imposed by a given stochastic discount factor. The empirical analysis reveals that the evidence from cross-sectional regression studies poses a substantial challenge to existing asset pricing models. The tests produce emphatic rejections for several candidate SDF specifications that perform well in prior research. It appears that the rejections are driven in part by the presence of nonlinearities in the data.

The signature of sentiment in conditional consumption CAPM estimates: A note

In this paper, we show that augmenting the consumption CAPM with sentiment, and thus allowing for systematic investor error in forming beliefs, helps reconcile investors’ optimizing behaviour with the cross-section of average stock returns. The fit of specifications that do not include sentiment-related factors, instead, crucially hinges on parameters estimates that are inconsistent with standard risk aversion assumptions, as formulated for example by Kimball (1993). This implies that investors must either commit systematic errors, at least ex-post, in assessing the joint distribution of stock returns and aggregate consumption or they must behave in a way that, at the aggregate level, is inconsistent with expected utility maximization under Kimball’s (1993) standard risk aversion.

Is consumption risk priced in the stock market?

Abstract The paper examines whether the risk in the consumption of stockholders caused by incomplete consumption insurance is priced in the cross-section of average stock returns. Using Taylor series expansion of the average marginal utility of consumption, we show that the risk in the consumption of stock market participants can be decomposed into two components, insurable (hedgeable using financial assets) and uninsurable (caused by incomplete consumption insurance) consumption risks. We argue that the growth rate of average consumption may be viewed as a proxy for the insurable component of consumption risk, while the growth rates of the rescaled higher-order cross-sectional consumption distribution moments may be regarded as a multivariate proxy for uninsurable risk in consumption. Exploiting microlevel household quarterly consumption data from the US Consumer Expenditure Survey, we find that both components of consumption risk are significantly priced when the limited stock market participation is taken into account. Neither the insurable and uninsurable components of consumption risk nor the Fama–French risk factors are rejected as capturing important components of systematic risk when tested against each other in an integrated multifactor asset pricing model.

Safety First, Learning Under Ambiguity, and the Cross-Section of Stock Returns

We examine the empirical implications of learning under ambiguity for the cross-section of stock returns. We introduce a theoretically-motivated ambiguity measure and find that ambiguity is priced in the cross-section of average stock returns. Ambiguity is not subsumed by state variables known to predict stock returns, nor by value, size, and momentum factors. In R-squared comparative tests, a model that takes ambiguity into account performs better than empirical implementations of the Bayesian learning model, the intertemporal CAPM, and the four-factor model of Fama and French (1993) and Carhart (1997). (JEL G12) Conditional asset pricing models based on rational expectations perform poorly empirically (Lewellen and Nagel 2006; Kan, Robotti, and Shanken 2013). While the rational expectations hypothesis assumes investors know the probability law governing asset retu rns, authors such as Keynes (1921), Knight (1921), Shackle (1949), and Roy (1952) have emphasized that investors form expectations based on vague information that cannot be quantified precisely. Related evidence from experimental studies (Ellsberg 1961) has confirmed that individuals are averse not only to uncertainty regarding the outcome of events with known probabilities (risk), but also uncertainty regarding the outcome of events with unknown probabilities (Knightian We are especially grateful to the editor, Wayne Ferson, and an anonymous referee fortheir extensive comments that substantially improved the article. We also appreciate the valuable comments of Damian Damianov, Andrei Nikoforov, Jaime Casassus, and participants at the 2011 regional meetings of the Econometric Society in Santiago, Chile, the 2010 FMA meetings in New York City, and seminar at UTPA, Texas. Send correspondence

Effects of Financial Variables and Market Risk on Investment Returns

Previous studies have shown a positive correlation between the cross-section of averagestock returns and financial variables. Some studies have documented that market risk does not seem to help explain the cross-section of average stock returns. Using data from Compustat, this study examines the relationship between stock returns and stock prices with financial variables considered relevant in predicting stock returns and market risk. The empirical results show thatbook value per share, free-cash flow, return on equity, dividends, and cash flow per share have a weak or no effect on stock returns and stock prices. Earnings from operations and its stability over time are the two more relevant factors affecting stock returns and prices.

Information Uncertainty Risk and Seasonality in International Stock Markets

A parsimonious two-factor model containing the market risk factor and a risk factor related to earnings information uncertainty has been developed to explain the seasonal regularity of January in international stock markets. This two-factor model shows apparently stronger power in explaining time-series behavior of stock returns and the cross-section of average stock returns in all major developed countries than do the competing models. Furthermore, the arbitrage residual return in January, which is the difference in the average residual returns between the smallest and largest size portfolios, is statistically insignificant in all the countries. These results indicate that the risk factor related to earnings information uncertainty plays a special role in explaining the seasonal pattern of stock returns in January, and that January might be a month that potentially tends to differentially reward stocks having uncertain earnings information. It could be argued, therefore, that large returns in January might be a risk premium for taking information uncertainty risk concerning earnings and unexpected earnings surprises faced at the earnings announcement, and that the previously reported strong January seasonality in stock returns might result from the use of misspecified models in adjusting for risk.

Safety First, Robust Dynamic Asset Pricing, and the Cross-Section of Expected Stock Returns

We investigate the implications of investors’ aversion to Knightian uncertainty for the cross-section of average stock returns. First, we derive the fundamental dynamic asset pricing equation in beta regression form of a robust investor concerned with Knightian uncertainty. Second, we provide empirical evidence that learning and uncertainty are both risk dimensions of systematic risk priced in the cross-section of stock returns. However, the risk premium component corresponding to uncertainty dominates the one corresponding to learning as investors’ investment horizon increases. Third, we report that a robust version of the intertemporal CAPM (ICAPM) explains average stock returns better than the four-factor model and the standard ICAPM.

Style Migration and the Cross-Section of Average Stock Returns

Stocks experiencing sharp changes in their style characteristics present unique opportunities to examine how investors view style information in making their portfolio allocation decisions. We examine the average returns of such stocks — which we call “style migrants” — and the covariation of the returns of style migrants with same-style stocks to provide new insight into the way investors use equity style information. Our results indicate that investors strongly judge a stock by its style, even when other information indicates that they should not do so. Specifically, we find that stocks experiencing large levels of variability in their size, book-to-market, and momentum characteristics during the prior five years significantly outperform, during the following year, other stocks with more style stability; thus, investors appear to penalize style risk by requiring higher future returns. In addition, these high style risk stocks covary much more with their new style cohorts than other low style risk stocks that have also moved into the same new style category — this is consistent with investors overreacting to style shifts with high style risk stocks, while exhibiting a “style memory” effect for low style-risk stocks. In further tests, we find some evidence that low-skill mutual fund managers tend to exhibit “style-chasing” behavior, and that their trades are correlated with contemporaneous abnormal returns of style migrants — consistent with their trades affecting migrant returns. Overall, our paper provides new evidence about investor behavior by demonstrating that some investors appear to overweight style information when allocating their portfolios, especially for certain high style-risk stocks.

Can investor heterogeneity be used to explain the cross-section of average stock returns in emerging markets?

This paper examines whether investor heterogeneity can be used for asset-pricing purposes in emerging markets. We pose this question, since the lack of transparency and greater uncertainty, which are typical of those markets, render it more likely that investors will disagree with each other and hold different portfolios, resulting in a mean-variance inefficient market portfolio. Consequently, we examine whether a heterogeneity-based factor can sufficiently augment the market portfolio, so that the two can function as multivariate proxies for the tangency portfolio. We test this hypothesis in the Korean stock market in which the measures of heterogeneity such as foreign ownership and institutional holdings are available for a large number of stocks over an extended period of time. We find that the heterogeneity-augmented two-factor model outperforms the CAPM one-factor and the Fama–French three-factor models. Consistent with the greater severity of investor heterogeneity in emerging markets, a developed market with comparable data availability, namely, Japan, shows similar but weaker test results.

Resurrecting the (C)CAPM: A Cross-Sectional Test when Risk Premia wre Time-Varying

This paper explores the ability of theoretically based asset pricing models such as the CAPM and the consumption CAPM-referred to jointly as the (C)CAPM-to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-to-market ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects.

Are Two Factors Enough? The U.K. Evidence

Some studies in the 1990s documented that book value of equity to market value of equity (BV/MV) and the market value of equity (MVE) capture the cross-sectional variation of stock returns in the U.S. market in the 1963–90 period. Other researchers argued, however, that two other variables—the sales-to-price ratio (S/P) and the debt-to-equity ratio (D/E)—have more explanatory power for stock returns than BV/MV and MVE. The evidence in this article, from London Stock Exchange data, indicates that S/P and D/E do not entirely absorb the roles of BV/MV and MVE in explaining the cross-section of average stock returns in the U.K. market. We did find that S/P has significant explanatory power beyond the contribution of BV/MV and MVE, but the explanatory power of D/E is captured by S/P.

Resurrecting the (C)CAPM: A Cross-Sectional Test When Risk Premia are Time-Varying

This paper explores the ability of theoretically based asset pricing models such as the CAPM and the consumption CAPM-referred to jointly as the (C)CAPM - to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-to-market ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects.

The Interaction of Value and Momentum Strategies

Value and momentum strategies both have demonstrated power to predict the cross-section of stock returns, but are these strategies related? Measures of momentum and value are negatively correlated across stocks, yet each is positively related to the cross-section of average stock returns. We examine whether the marginal power of value or momentum differs depending upon the level of the other variable. Value strategies work, in general, but are strongest among low-momentum (loser) stocks and weakest among high-momentum (winner) stocks. The momentum strategy works, in general, but is particularly strong among low-value (expensive) stocks. These results hold despite finding comparable spreads in value measures among stocks with different levels of momentum and comparable spreads in the momentum measure among stocks with different levels of value. Any explanation for why value and momentum work must explain this interaction.