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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.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

It is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.

The key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.

The backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.

This paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

This paper aims to examine the Chinese investment anomaly and dissect it from a perspective of rational expectation framework.

Characteristic-based sorting and Fama–MacBeth two-stage cross-sectional regression are adopted to test the relationship between corporate investment and expected returns in both portfolio and individual stock levels. Under the framework of pricing kernels, an investment-based common risk factor is constructed to test the role of risk played in the negative investment-return relationship. Moreover, a Markov regime switching model is adopted to investigate the time-varying risk premium across market regimes.

Empirical results provide ample evidence showing that there is a negative relationship between investment and expected returns in the Chinese stock market. The new investment-based risk factor is found to capture the return differences across characteristic-based portfolios. In addition, risk premium of the new risk factor is not only statistically positive throughout the sample period, but also has an asymmetry that is higher during market downturn but lower under bull market.

This paper merely tests the hypotheses derived from rational school.

Investment strategies based on characteristic-sorted portfolios should be adjusted to different market regimes.

First, this paper provides comprehensive empirical results by adopting different methodologies for investigating the investment anomaly in China. Second, an investment-based factor is constructed specifically for the Chinese stock market for the first time. Finally, this is the first paper to investigate the asymmetric risk premium across the Chinese bear and bull regimes by using a multivariate Markov regime switching model.

This paper aims to test the predictability of the three asset classes, namely direct property, bond and property stocks in Singapore.

Using the generalized method of moment (GMM) estimation methodology, the authors first estimate the excess returns of assets on five instrumental variables and a constant term. Next the common risk factors are tested in three parts involving different portfolio of sample assets.

The empirical results shows that there are at most three common risk factors that can be used to predict the excess returns of six asset classes, that include four direct property assets, bonds and property stocks. The results also indicate that there are separate common risk premia that are priced in property stock and direct property markets, which indirectly reject the hypothesis that the two property markets are integrated.

The empirical results that reject the market integration between property and property stock markets imply that there are significant diversification benefits for holding both assets in investors' portfolios. The two property assets capture different risk premia in the markets.

The GMM specifications that include five instrumental variables may not fully capture all risk information. Omission of other variables is, however, traded‐off against the parsimony of the model specification. More independent variables could be included in the future studies, and more asset classes could also be added to the tests.

The study provides alternative evidence to the test of market integration between property and property stocks in Singapore. It also verifies the earlier study in the USA that property and stock market effects could be separately priced by the market.

This chapter analyses the ability of some structural models to predict corporate bankruptcy. The study extends the existing empirical work on default risk in two ways. First, it estimates the expected default probabilities (EDPs) for a sample of bankrupt companies in the USA as a function of volatility, debt ratio, and other company variables. Second, it computes default correlations using a copula function and extracts common or latent factors that drive companies’ default correlations using a factor-analytical technique. Idiosyncratic risk is observed to change significantly prior to bankruptcy and its impact on EDPs is found to be more important than that of total volatility. Information-related tests corroborate the results of prediction-orientated tests reported by other studies in the literature; however, only a weak explanatory power is found in the widely used market-to-book assets and book-to-market equity ratio. The results indicate that common factors, which capture the overall state of the economy, explain default correlations quite well.

The growth in euro-denominated bond debt issued by emerging market sovereigns picked up considerably after the Asian currency crises. However, while many emerging market governments now have outstanding euro-denominated issues, the market for this debt remains considerably smaller and less liquid than its US dollar counterpart. This has implications for both investors and sovereigns as they try to balance liquidity and cost of capital considerations against portfolio diversification and exchange rate movements. Broadly speaking, spreads on emerging market bonds across countries tend to move in tandem over time. This chapter takes an introductory look at the market for euro-denominated sovereign debt, and investigates the degree to which spreads on euro-denominated emerging market sovereign debt react to common forces. Following a similar analysis of the US dollar market in McGuire and Schrijvers (2003) (hereafter MS2003), we use principal factor analysis to determine the number of common factors that drive movements in spreads, and then seek to assign meaning to these factors through simple correlations with economic variables.

The purpose of this paper is to reinvestigate the performance of common stock returns with respect to two popularly known firm level characteristics: size and book‐to‐market ratio.

All of New York Stock Exchange, American Stock Exchange, and National Association of Securities Dealers Automated Quotations stocks between July 1926 and June 2007 are used, and divided into various size and book‐to‐market equity groups. The extension of the various versions of the simple Fama‐French model is implemented.

From the findings, it is inferred that: two risk factors based on the mimicking return for the size and book‐to‐market ratio play a significant role in capturing strong variation in stock returns; and volatility persistence can significantly improve the common risk factors' impact in explaining the time series variation in size and book‐to‐market sorted portfolios.

In some sense, the model is based on only two firm level variables. In reality there exists plenty of other sources of average return anomalies. For a clearer understanding, an integration of various firm level characteristics would be an interesting issue to explore. A general equilibrium model that incorporates volatility exposure in a Fama‐French framework would be a challenging task as well.

The approach will help scholars and investment professionals make robust quantification of risk and average returns with respect to various measures of fundamental value.

The patterns in the monthly and yearly average excess returns with respect to two firm level characteristics, which documented are consistent with earlier studies. Even though the important role of firm level characteristics on the average‐return anomalies of common stocks is widely known, the approach is the very first that extends its support with respect to volatility models.

This study aims to compare the risk‐return characteristics and performance of real estate investment trust (REIT) hotel companies (hotel REITs hereafter) with those of C‐corporation hotel companies (hotel C‐corps hereafter).

The risk‐return characteristics and performance of hotel REITs and C‐corps were examined by estimating single‐factor and Fama‐French three‐factor asset pricing models for each portfolio. Differences between the hotel REIT and C‐corp estimations were tested using Wald test statistics.

Little evidence was found that hotel REITs have significantly different risk‐return characteristics and performance than hotel C‐corps, which suggests that hotel REITs and C‐corps are not significantly different in terms of market risk‐return characteristics and performance. The market portfolio had a significantly positive effect on the returns of both hotel REITs and C‐corps. The size and book‐to‐market factors of common stock also had a significant explanatory power for the returns of hotel REITs and C‐corps. Both hotel REITs and C‐corps performed similarly to the market portfolio, on a risk‐adjusted basis, during the 2000s.

Despite the fact that the three‐factor asset pricing model explains a significantly greater proportion of the variation in the hotel firms' returns than the single‐factor asset pricing model, approximately 30 percent of the total variation still remains unexplained.

The risk‐return characteristics and performance of hotel REITs and C‐corps revealed by this study will render hotel investors' decisions between the two organizational structures less complicated. In addition, the findings can be used by portfolio managers to construct a well‐diversified portfolio.

A multifactor asset pricing model was used for the first time in this article to examine the risk‐return characteristics and performance of hotel companies. In addition, the importance of understanding differences between REIT and C‐corp structures in the lodging industry is emphasized.