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The main purpose of this study is to examine the role of quality as a determinant of a cross-sectional variation in country-level stock returns. The study attempts to address the question: Is there any special premium for top-quality stock markets with decent profitability, indebtedness and liquidity ratios?

The computations are based on the listings of 66 country portfolios over the period between 2000 and 2013. Long/short country portfolios from sorts on characteristics related to quality are examined with asset-pricing models.

The inter-market variation in returns may be explained with profitability and debt ratios: the more profitable and the less indebted is the stock market, the better is its performance. Moreover, the performance of country-level value, size and momentum strategies may be improved by double sorting on quality characteristics.

The practical implications include such issues as the global asset allocation, the development of investment products, asset pricing and investment performance measurement. The country selection strategies that are based on leverage and profitability prove to be a useful tool for investors with a global investment mandate. Furthermore, additional sorting on quality metrics may markedly improve the performance of inter-market value, size and momentum strategies.

This paper examines the role of quality metrics related to financial leverage, profitability and liquidity in explaining the cross-sectional variation in country returns.

The beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.

The study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.

The results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.

The data are limited to the index securities as beta could be time varying.

Results overall provide insight into the cost of equity calculation and emerging market assets valuation.

The framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets.

The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R²).

The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable.

Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

The purpose of this paper is to explore whether stock selection strategies based on four fundamental quality indicators can generate superior returns compared to overall market.

The sample of stocks comprises the constituents of BSE-500 index, which is a broad based index consisting of highly liquid stocks from all 20 major industries of the Indian economy. Portfolios are constructed on the basis of quality indicator rankings of companies and the returns of these portfolios are compared with the overall market. Excess returns on quality based portfolios are also determined using OLS regressions of quality portfolio returns on market, size, value and momentum factor returns.

The results suggest that two of the four quality strategies, namely Grantham Quality indicator and Gross Profitability have generated superior returns after controlling for market returns as well as common anomalies such as size, value and momentum. Combining value strategies with quality strategies do not yield any significant gains relative to quality only strategies.

For investors looking to invest in the Indian stock market for a long term, this study provides evidence on the performance of some fundamental indicators that can help predict long run stock performance. The findings suggest that investors can distinguish between high-performing and low-performing stocks based on stock quality indicators.

This is the first such study to look into the performance of quality investing in the Indian stock market. As most quality investing studies have been focussed on developed economies, this paper provides out-of-sample evidence for quality investing in the context of an emerging market.

The purpose of this paper is to investigate the pricing of European real estate equities. The study examines the main drivers of real estate equity returns and determines whether loadings on systematic risk factors – the excess market return, small minus big (SMB), HIGH minus low (HML) – can explain cross‐sectional return differences in unconditional as well as in conditional asset pricing tests.

The paper draws upon time‐series regressions to investigate determinants of real estate equity returns. Rolling Fama‐French regressions are applied to estimate time‐varying loadings on systematic risk factors. Unconditional as well as conditional monthly Fama‐MacBeth regressions are employed to explain cross‐sectional return variations.

Systematic risk factors are important drivers of European real estate equity returns. Returns are positively related to the excess market return and to a value factor. A size factor impacts predominantly negatively on real estate returns. The results indicate increasing market integration after the introduction of the Euro. Loadings on systematic risk factors have weak explanatory power in unconditional cross‐section regressions but can explain returns in a conditional framework. Beta – and to a lesser extent the loading on HML – is positively related to returns in up‐markets and negatively in down markets. Equities which load positively on SMB outperform in down markets.

The implementation of a liquidity or a momentum factor could provide further evidence on the pricing of European real estate equities.

The findings could help investors to manage the risk exposure more effectively. Investors should furthermore be able to estimate their cost of equity more precisely and might better be able to pick stocks for time varying investment strategies.

This is the first paper to examine the pricing of real estate equity returns in a pan‐European setting.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The capital asset pricing model predicts that in equilibrium, investors should hold the market portfolio. As a result, investors should only be rewarded for carrying undiversifiable systematic risk and not for diversifiable idiosyncratic risk. The study is adding to the growing body of countering studies by first examining time trends of idiosyncratic risk and subsequently the pricing of idiosyncratic risk in European real estate equities. The paper aims to discuss these issues.

The study analyses 293 real estate equities from 16 European capital markets over the 1991-2011 period. The framework of Fama and MacBeth is employed. Regressions of the cross-section of expected equity excess returns on idiosyncratic risk and other firm characteristics such as beta, size, book-to-market equity (BE/ME), momentum, liquidity and co-skewness are performed. Due to recent evidence on the conditional pricing of European real estate equities, the pricing is also investigated using the conditional framework of Pettengill et al. Either realised or expected idiosyncratic volatility forecasted using a set of exponential generalized autoregressive conditional heteroskedasticity models are employed.

The initial analysis of time trends in idiosyncratic risk reveals that while the early 1990s are characterised by both high total and idiosyncratic volatility, a strong downward trend emerged in 1992 which was only interrupted by the burst of the dotcom bubble and the 9/11 attacks along with the global financial and economic crisis. The largest part of total volatility is idiosyncratic and therefore firm-specific in nature. Simple cross-correlations indicate that high beta, small size, high BE/ME, low momentum, low liquidity and high co-skewness equities have higher idiosyncratic risk. While size and BE/ME are priced unconditionally from 1991 to 2011, both measures of idiosyncratic risk fail to achieve significance at reasonable levels. However, once conditioned on the general equity market or real estate equity market, a strong positive relationship between idiosyncratic risk and expected returns emerges in up-markets, while the opposite relationship exists in down-markets. The relationship is robust to firm-specific factors and a series of robustness checks.

The results show that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky (real estate) assets.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The study reveals differences in the pricing of European real estate equities and US REITs. The study highlights that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky assets.

The purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market βs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.

Following Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.

Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market ßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.

Given data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.

Information about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.

To the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

The purpose of this paper is to examine financial integration across North American stock markets from January 1984 to December 2003.

The paper uses an arbitrage pricing theory framework. The risk factors considered are the three Fama and French factors augmented with momentum for both countries as well as their international counterparts. Both the domestic and international four factor models in cross section and test for partial, mild, and strong financial integration are estimated. The domestic and international model are estimated on domestic portfolios and on a subset of Canadian cross listings matched with American stocks.

Results can be summarized as follows: first, results show stronger evidence of mild rather than partial or strong integration in both domestic portfolios and interlisted stocks. Second, interlisted stocks appear at first glance to be more integrated than the domestic portfolios, but this result can be attributed to the poor explanatory power of the models applied to interlisted stocks. Once the authors rule out the case where the model does not generate statistically important risk premiums for both countries, the evidence of integration is similar in both domestic and interlisted stocks. Third, the domestic and international models have similar explanatory power, although the domestic model performs better with the Canadian interlisted stocks are found.

The results suggest that, in an international context, a portfolio manager is better off using the four factor model as a benchmark in cross sections rather than the single market. Furthermore, if the agency problem described in Karolyi is ignored, Canadian interlisted stocks and Canadian domestic portfolios have the same diversification potential.

The authors aim to assess whether herding in GCC stock markets is more responsive to global dynamics than its response to regional developments. To do so, they use the largest equity market in the region which is Saudi Arabia as the benchmark, and then they examine if herding crosses from this large regional market to the rest of equities in the neighboring markets during various time periods. To compare the importance of global influences on herding, the authors investigate and compare the impact of the information flow from the US equity market on the herding of equities in the GCC markets.

To investigate herding in GCC markets the authors use the relationship between the squared market return and the cross-section absolute deviation that does not covary with market styles and/or fundamentals. In order to do that we follow Galariotis et al. (2015) and account for four styles: market-oriented, small-cap, value and momentum. As these factors have been shown to be associated with the economic fundamentals, filtering the covariance of deviation with these factors is expected to remove the style and the fundamental herding influence from the value of the dispersion.

The results show significant herding behavior that persists across various independent periods. This evidence stands even when the authors control for the well- known factor structures in stock returns. Importantly, the authors find that the few herding crossovers that occurred during the sample period are more likely to originate from the Saudi market rather than from the US. Therefore, the authors conclude that behavioral inefficiencies in the GCC equity markets are likely to be regional and that the sentiment-based trading in the US has essentially a minimal role to play.

The empirical findings are useful for policymakers who aim at preventing market manipulation in order to preserve the integrity of financial markets. Policymakers in the GCC should disclose more information to aid investors so they do not rely on other investors' trades. The portfolio managers should be aware that the correlation of GCC equities can be higher in the short term due to common market herding in these countries. As the US market does not play an important role in triggering behavioral irrationalities in these markets, investing in GCC equities is a good hedge in a US portfolio. Finally, the results have also important implications for active funds that aim to exploit short-term trending in markets in order to enhance performance.

The authors’ contribution in this paper is to investigate herding in GCC markets by using the relationship between the squared market return and the cross-section absolute deviation that does not covary with market styles and/or fundamentals. Another contribution of our paper is to investigate any cross herding from the Saudi market to the rest of the markets in the area. The previous literature on GCC equity market herding is silent on this issue and it is typically restricted to the level of the single market.

This paper aims to investigate whether empirical evidence for scale economies can be found across countries and if so, whether this evidence varies across the stage of development.

The paper uses statistical methods to make comparisons between countries.

The empirical results suggest overall evidence towards aggregate increasing returns across all samples. Within the Cobb‐Douglas framework, stronger evidence for aggregate increasing returns is found among samples depicting economies in the early stages of development. The CES framework in turn supports aggregate scale economies for advanced economies, while unitary elasticity of substitution cannot be rejected for less developed economies, giving further support for the Cobb‐Douglas estimates.

Given that evidence for scale economies is found within different estimation frameworks for different groups of economies, comparative judgment is prevented. The results nevertheless provide evidence on the overall relevance of scale economies within and across groups of economies, while also giving a clear indication of the relevance of stage of development in economic growth and development analysis.

The most fundamental insight of the empirical results presented in this paper is that there is no reason to assume that the determinants of growth or the parameters guiding economies' adjustments towards their steady states or growth paths will be similar for economies at different stages of development, given their significant structural differences, whether in terms of production structures and characteristics or consumption patterns.