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This paper aims to investigate the explanatory power of the Fama-French five-factor model and compares it to the other asset pricing models. In addition, the paper examines the contributions of two additional factors: profitability and investment factor. The authors test the alternative four-factor models.

The authors use stock returns data of BSE-500 listed firms for the Indian market, an emerging market, from 1999 to 2020, thus covering the post-Asian crisis and pre- and post-financial crisis (2007–2008) periods. The authors employ 75 and 96 portfolios based on different factors. To check the performance of asset pricing models, the authors also used the GRS F-statistics and factor spanning tests.

The authors find that the five-factor model and alternative four-factor model outperform the three-factor model. Contrary to the findings for the US, but similar to the Chinese stock market, the value factor is significant for the Indian stock market. Simultaneously, the authors also find that the investment factor has no explanatory power in the presence of the profitability factor in their sample.

To the best of the authors' knowledge, this is the most comprehensive study using data more than two decades. These results are based on 75 (25 × 3) portfolios based on size, value, profitability and investment. The authors also tested these results based on 96 (32 × 3) portfolios to check robustness, and these results still hold. Furthermore, the authors find that factors based on 2 × 3 sorting have higher explanatory power than those based on 2 × 2 and 2 × 2 × 2 × 2 sorting.

Although the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view.

From the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al., 2020).

While the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view.

There are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.

The purpose of this paper is to study correlations between the national real estate investment trusts (REIT) markets in the USA and the four Asia‐Pacific countries of Australia, Hong Kong, Japan and Singapore, and document the extent to which the time variation present in these correlations can be explained from a set of 11 economic and financial factors. Both US dollar and local currency returns are used.

Time‐varying correlations are estimated using a DCC‐GARCH model that allows for asymmetries in both the correlations and volatilities. The correlations are then regressed on a set of four economic and seven financial factors, and tests of statistical significance are conducted in order to discriminate between relevant and irrelevant explanatory variables. The authors estimate a fixed‐effects panel regression as well as individual regressions for each dynamic correlation.

Significant time variation is found in the four REIT correlation series. Panel regressions suggest that REIT correlations rise with increases in the interaction of national inflation rates and with higher global equity market uncertainty. It is also found that REIT correlations fall with increases in the US default risk premium and global equity market volume. Relaxing the structure imposed by the panel data model, individual regressions confirm most of the results, although there are some exceptions. It is also found that there are no substantial differences in the dynamics of the correlation coefficients when switching from the US dollar to local currency denominated returns.

Investors in real estate securities across national markets should take into account information about the credit spread, the volatility and volume of global equity markets, and inflation rates when modeling correlations. These variables may alert the investors to the possibility that, under a set of circumstances, investing in real estate across different markets may not provide the expected diversification benefits. Another implication relates to the impact of currency hedging. It appears that the impact of switching from US dollar to local currency denominated returns does not substantially change the time dynamics of the correlations, or the importance of explanatory variables.

Although considerable progress has been made in modelling time‐varying correlations between various REIT markets, to the authors' knowledge, this is one of the first papers to investigate the underlying causes of the co‐movement, especially between the US and Asia‐Pacific markets. The paper's results will help investors and risk managers make better choices by identifying those factors that have more systematic effects on the change in the REIT correlations, rather than more transient forces.

While climatic conditions are believed to have some influence on triggering conflicts, the existing empirical results on the nature and statistical significance of their explanatory role are not conclusive. We construct a dataset for a sample of 139 countries which records the occurrence of an armed conflict, the annual average temperature and precipitation levels, as well as the relevant socioeconomic, demographic, and geographic measures over the 1961–2011 period. Using this dataset and controlling for the effect of relevant nonclimate variables, our comprehensive econometric analyses support the influencing role of climatic factors. Our results are robust and consistent with the hypothesis that climate warming is instrumental in raising the probability of onset of internal armed conflicts and suggests that, along with regulating population size and promoting political stability, controlling climate change is an effective factor for inducing peace by way of curtailing the onset of armed conflicts.

Real estate is the last major asset class without liquid derivatives markets. The reasons for that are not fully known or understood. Therefore, the purpose of this paper is to better understand the main factors that influence the propensity of commercial real estate investors in the UK to employ property derivatives.

The research methodology that was chosen for this research is grounded theory which, in its original form, goes back to Glaser and Strauss (1967). A total of 43 interviews were conducted with 46 real estate professionals in the UK from property investment management firms (investing directly or indirectly in real estate), multi-asset management firms, real estate investment trusts, banks, and brokerage and advisory firms, among others.

The research results show 29 factors that influence the propensity of direct and indirect real estate investors in the UK to employ property derivatives. Out of the 29 factors, the current research identified 12 factors with high-explanatory power, 6 with a contributing role and 11 with low explanatory power. Moreover, factors previously discussed in the literature are tested and assessed as to their explanatory power. The focus of this paper is on those factors with high-explanatory power. From the research data, three main reasons have been identified as the sources of investor reluctance to trade in property derivatives. The first and main reason is related to a mismatch between motivations of property investment managers and what can be achieved with the instruments. The second reason, which ties in with the first one, is a general misunderstanding as to the right pricing technique of property derivatives. Finally, the third reason is a general lack of hedging demand from the investor base owing to the long investment horizons through market cycles.

The research contributes to the literature on property derivatives in various ways. First, it extends the literature on market hurdles in property derivatives markets by testing and extending the hurdles that were proposed previously. Second, the research shows that the existing pricing models need to be extended in order to account for the risk perception of practitioners and their concerns with regard to liquidity levels.

For both theory and practice, the research has shown some limitations in using property derivatives for purposes such as creating index exposure or hedging. Another contribution, in this case to practice, is that this study provides a clearer picture as to the reasons that keep property investment managers away from using property derivatives.

The research results indicate that liquidity per se is not a universal remedy for the problems in the market. In addition to the need for improving the understanding of the pricing mechanism, practitioners should give more thought to the notion of real estate market risk and the commensurate returns that can reasonably be expected when they take or reduce it. This implies that property index futures currently do not price like those on any other investable asset class.

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 paper is an extension of Blinder's (1973) and Oaxaca's (1973) famous decomposition. While they looked at the determinants of the wage gap between two groups, this paper not only considers any number of groups but it also proposes a decomposition technique that permits to analyze the determinants of the overall wage dispersion. The approach presented combines two techniques. The first one, popular in the field of income inequality measurement, concerns the breakdown of inequality by population subgroups. The second one, very common in the labor economics literature, uses Mincerian earnings functions to derive a decomposition of wage differences between two groups into components measuring, respectively, group differences in the average values of the explanatory variables, in the coefficients of these variables in the earnings functions and in the unobservable characteristics. This methodological novelty allows one to determine the exact impact of each of these three elements on the overall wage dispersion, on the dispersion within and between groups, and on the degree of overlap between the wage distributions of the various groups.

This paper goes, however, beyond a static analysis in so far as it succeeds in breaking down the change over time in the overall wage dispersion and its components (between- and within-groups dispersion and group overlapping) into elements related to changes in the value of the explanatory variables and the coefficients of these variables in the earnings functions, in the unobservable characteristics and in the relative size of the various groups.

The empirical illustration of this paper looks at data obtained from income surveys conducted in Israel in 1982, 1990, and 1998, special emphasis being put on the comparison between the earnings of new immigrants and those of natives or older immigrants.

This study aims to analyze the factors related to the failure of 535 Federal Deposit Insurance Corporation (FDIC)-Insured United States banks in conjunction with the 2008 financial crisis.

The research consists of an analysis of the following three five-year partitions: pre-crisis (2002–2006), crisis (2007–2011) and post-crisis (2012–2016). The main hypothesis is that the factors explaining bank failures vary by period. Using logistic regression analysis, the authors identify the desirable models by period based on three model selection strategies.

Liquidity and non-risk-based capital ratios are important explanatory factors in all three periods. As the authors can see from the results, when comparing the full period (2002–2016) and the three five-year period partitions (2002–2006, 2007–2011 and 2012–2016), the ratios change from period to period, but they measure the same financial areas of concern in different contexts as follows: liquidity, leverage/risk exposure and capital adequacy. Risk-based capital ratios are not effective predictors of bank failures.

Recent academic studies have analyzed bank failures during periods that cover the years before, during and after the crisis, but most of these studies discuss bank failures in the forecasting context only. This study includes an analysis of failure determinants during pre-crisis, crisis and post-crisis subperiods based on the FDIC monitoring system of bank failures and identifies what ratios are more relevant during each period and how they change from period to period.

This chapter introduces the Person-in-Environment (PIE) framework, a research design and a nationwide empirical study, developed by the author, to measure the relative impacts of socio-structural and personal factors on individual-level information behaviours (IB) and outcomes. The IB field needs to tackle two questions: (1) In a particular situation, how much of an individual's IB is influenced by personal characteristics? and (2) How much of this behaviour is shaped by one's environment, such as socio-structural barriers? PIE is a beginning effort to address this agency–structure debate, which is a topic that confronts many social scientists. This chapter first outlines IB research relevant to agency–structure integration. It then presents six principles of the PIE framework. Personal characteristics (e.g. cognitive and affective factors) and socio-structural factors (e.g. information resources distribution) are conceptualised as interrelated. Thus, these need to be tested simultaneously. Previously, it was difficult to link individual- and societal-level datasets because their units of observation often vary. To overcome these methodological challenges, this author purposed a research design that employs secondary analysis, geographic information systems techniques and structural equation modelling. An empirical study of the library usage by 13,000 American 12th graders is presented to demonstrate PIE's applicability. Discussions on the future directions of PIE studies conclude the chapter. The PIE framework can contribute to conceptual and methodological development in IB research. It also offers scholars and policymakers a way to empirically assess the contributions of information services on an individual's life, while taking personal differences into account.

We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for discrete returns generates new empirical “factors,” and these factors contribute significant explanatory power to the models. We provide a conditional performance evaluation for US fixed income mutual funds, conditioning on a variety of discrete ex-ante characterizations of the states of the economy. During 1985–1999 we find that fixed income funds return less on average than passive benchmarks that do not pay expenses, but not in all economic states. Fixed income funds typically do poorly when short-term interest rates or industrial capacity utilization rates are high, and offer higher returns when quality-related credit spreads are high. We find more heterogeneity across fund styles than across characteristics-based fund groups. Mortgage funds underperform a GNMA index in all economic states. These excess returns are reduced, and typically become insignificant, when we adjust for risk using the models.