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The purpose of this paper is to explore the effect of leverage mimicking factor portfolios in explaining stock return variations. This paper broadens the focus of the current asset pricing literature by forming portfolios mimicking the leverage factor.

Following Fama and French's and Carhart's procedure in forming size, book‐to‐market and momentum mimicking portfolios, the authors of this paper form leverage mimicking factor portfolios to explain stock returns. A five factor model is constructed that explains the variations in stock returns better relative to the other asset pricing models including the Fama‐French‐Carhart four factor model.

The findings indicate that the leverage mimicking portfolio helps to explain stock return variations better relative to the other asset pricing models including the Fama‐French‐Carhart four factor model. Results are robust to other risk factors.

The results lead us to explore further avenues in using other risk factors in asset pricing such as future work to consider other cross‐sectional attributes such as the stochastic behaviour of earnings or profitability that might also produce common variation in stock returns. There may be other risk factors that carry a premium and thus can be used for asset pricing.

The paper's findings are important in fund management when selecting or evaluating portfolio performance. The authors introduce an additional factor that has a sound theoretical appeal and show that leverage mimicking factor portfolios provide additional information in pricing assets, both in the cross section of all shares and in different sectors.

To the best of the authors' knowledge this is the first study of the effect of leverage mimicking factor portfolios in explaining stock return variations.

Whether the risk factors or firm characteristics cause the value premium of stocks still needs further investigation. This paper shows that the factor‐based models are significant but not sufficient for the stock returns in Taiwan. Size or book‐to‐market ratio alone cannot influence the stock returns under a factor‐based model. However, size along with book‐to‐market is significant under a factor‐based model. Furthermore, the risk characteristics are more influential than the factor load in stock return behavior. We conclude that employing only a factor‐based model or only risk characteristics will not consider some important content in stock returns.

We would like to thank C. Y. Chen, Wenchih Lee, two anonymous referees and the seminar participants at the 2000 FMA annual meeting for their helpful comments and encouragement. All of the remaining errors are our responsibility.

This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.

The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.

The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.

To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

García Lara et al. (2011) argue that there is a conservatism-related priced risk factor in US stock returns. To put this to the test, the authors aim to analyze whether the conditional conservatism effect comes from the loading on a conditional conservatism-related factor-mimicking portfolio (systematic risk) or the conservatism characteristic itself.

The authors form characteristic-balanced portfolios from dependent sorts of stocks on the firm’s degree of conservatism and the firm’s loading on the conservatism-related factor-mimicking portfolio as proposed by Daniel and Titman (1997) and Davis et al. (2000).

The tests indicate that it is the conditional conservatism characteristic rather than the factor loading that explains the cross-sectional differences in average stock returns. Consequently, they do not find evidence for a conservatism-related priced risk factor.

This finding suggests that investors misvalue the conservatism characteristic and casts doubt on the rational risk explanation as proposed by García Lara et al. (2011).

The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe.

To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model.

The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion.

Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios.

This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

This paper aims to examine whether idiosyncratic volatility and other asset pricing factors predict growth rates of the ten Australian economic indicators.

The authors use the Liew and Vassalou (2000) model augmented with an idiosyncratic volatility factor to investigate the issue.

Using regression analysis, the authors find that the asset pricing factors can be used to predict the growth rates for eight out of the ten economic indicators. Moreover, using portfolio performance analysis, the authors find that high returns of size factor and a book-to-market factor portfolios precede periods of good macroeconomic states, whereas high returns of HIMLI portfolios precede periods of bad macroeconomic states.

To the authors’ knowledge, the relationship between idiosyncratic volatility and Australian economic growth has not been investigated explicitly in the literature.

This paper aims to examine the investment performance of pension funds in the UK using the three standard performance measurement models, the capital asset pricing model (CAPM), Fama-French model and the Carhart model.

The authors use the CAPS-Mellon survey data for the period 1990-2008 and employ the three standard performance measurement models, the CAPM, Fama-French model and the Carhart model in assessing the investment performance of the pension funds.

The authors show that the abnormal returns of pension funds cannot be fully explained by size, book-to-market values, market returns, momentum and the term spread. The authors find larger abnormal returns in bond than in equity portfolios and that smaller funds outperform larger funds. The paper also shows that the addition of the momentum factor does not improve on the three-factor Fama-French model. The authors find that pension funds exhibit superior performance relative to the linear factor models.

First, this study contributes to the extant literature on pension funds performance. Future research may also extend the authors' work to incorporate economic, tax, political and legal differences across the countries on the performance of pension funds. Second, due to data constraints, this study excludes the default probability of corporate bonds as an additional variable in their tests on bond returns. Future work may add the default probability as an additional variable whilst examining bond returns.

The authors believe that the findings will be considerable food for thought for fund managers who continuously attempt to explore opportunities to provide a higher return to investors.

To the authors' knowledge, this is the first comprehensive study that investigates the performance of UK equity and bond pension funds relative to standard linear factor models such as the CAPM, Fama and French, and Carhart.

This paper aims to hunt for the driving force behind the accrual anomaly and revisit the risk versus mispricing debate.

In sorts of stock returns on abnormal and normal accruals, the authors find that abnormal accruals are the driving force behind the accrual anomaly. The authors then construct characteristic-balanced portfolios from dependent sorts of stock returns on the abnormal accrual characteristic and a related factor-mimicking portfolio to test whether the accrual anomaly is due to risk or mispricing (Daniel and Titman, 1997; Davis et al., 2000).

Similar to Hirshleifer et al. (2012), the authors find that the accrual anomaly is due to mispricing and that the measure of accruals used in Hirshleifer et al.’s study (2012) is a very broad measure of accruals. The authors therefore recommend the use of abnormal accruals in future research.

The results suggest that there are limits to arbitrage or behavioral biases with regard to the trading of low-accrual firms. Showing that the accrual effect is driven by the level of abnormal accruals, the findings of this study strongly challenge the rational risk explanation proposed by the extant literature.

The objective of this paper is to empirically evaluate alternative multifactor explanations of cash‐flows and earnings momentum portfolios. It aims to examine whether the common risk factors, which are related to firm level accounting characteristics, can reflect the behavior of average portfolio returns based on such measures as cash‐flows and earnings momentum in the presence of each other's systematic components and time‐varying measures of volatility.

The paper uses monthly stock returns for all NYSE firms on CRSP database and constructs average portfolio returns between July 1951 and June 2008. It investigates the interdependence of stock returns for cash‐flows and earnings momentum portfolios using their systematic components. The methodology is implemented by extending various characteristic‐based factor models of returns.

The main finding of the study suggests that there is strong information transmission – both in the temporal variation and risk sensitivities of the average returns of cash‐flows and earnings momentum portfolios. Also, there is compelling empirical evidence that the associated systematic components well complement the ability of common risk factors to explain the temporal behavior of all NYSE stocks.

While the results are statistically significant, the effect of aggregate risk in factor model is dubious. An integration of other accruals based accounting characteristics would be an interesting issue to explore.

The goal of the paper is to examine how different combinations of empirically determined variables that are instrumental in the creation of style‐specific benchmarks can capture the time‐series variation of average portfolio returns. It will provide added value to scholars and investment professionals in making effective portfolio management decisions.

Compared to the existing literature, in the evaluation of earnings and cash‐flows based measures, the paper focuses on the predictive power of the systematic components. It shows that paying close attention to the systematic components clearly provides additional information about the time‐varying behavior of average stock returns. The findings that the economic characteristics of the firm can complement the comparative role of the systematic components of cash‐flows and earnings add significantly to the literature.

In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors.

To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum.

The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample.

The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.