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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.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

The purpose of this article is to assess managers' evaluation of risk associated with applicable uncontrollable forces when developing pricing strategies.

The present study is based on attribution theory. An experiment using more than 100 business managers was conducted to assess the perceived risk of uncontrollable environmental factors.

The findings suggest that when uncontrollable environmental factors dominate pricing managers tend to select pricing strategies with external orientations to deflect risk away from themselves personally.

This research is limited to pricing strategies and not a broader selection of marketing strategies. The present research provides greater insight as to why managers make certain strategic pricing decisions.

This paper suggests management should frame decision‐making contexts so that minimizing personal exposure is consistent with corporate goals and objectives.

This paper is an extension of previous research examining the managers' perception of risk. In particular, this paper focuses on how managers examine/evaluate risk and how that impacts their decision‐making process when selecting pricing strategies.

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.

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.

To achieve a full understanding of the role of marketing from plan to profit requires a knowledge of the basic building blocks. This textbook introduces the key concepts in the art or science of marketing to practising managers. Understanding your customers and consumers, the 4 Ps (Product, Place, Price and Promotion) provides the basic tools for effective marketing. Deploying your resources and informing your managerial decision making is dealt with in Unit VII introducing marketing intelligence, competition, budgeting and organisational issues. The logical conclusion of this effort is achieving sales and the particular techniques involved are explored in the final section.

Understanding the pricing of real estate equities is a central objective of real estate research. This paper aims to investigate the impact of liquidity on European real estate equity returns, after accounting for well-documented systematic risk factors.

Based on risk factors derived from general equity data, the authors extend the Fama-French time-series regression approach by a liquidity factor, using a pan-European sample of 272 real estate equities.

The empirical results indicate that liquidity is a significant pricing factor in real estate stock returns, even after controlling for market, size and book-to-market factors. In addition, the authors detect that real estate stock returns load predominantly positively on the liquidity risk factor, suggesting that real estate equities tend to behave like illiquid common equities. These findings are underpinned by a series of robustness checks. Running a comparative analysis with alternative factor models, the authors further demonstrate that the liquidity-augmented asset-pricing model is most appropriate for explaining European real estate stock returns.

The inclusion of sentiment and downside risk factors could provide further insights into real estate asset pricing in European capital markets.

This is the first study to examine the role of liquidity as a systematic risk factor in a pan-European setting.

This paper aims to examine the nexus between the pricing of market-wide volatility risk and distress risk in the cross-section of portfolio returns for the 1990-2011 time period. The author expands upon prior research by constructing an ex post factor that mimics aggregate volatility risk based on the new VIX index of the Chicago Board Options Exchange, termed FVIX, as well as focuses on volatility risk in crisis versus non-crisis time periods.

The author investigates the relationship between volatility and distress risk using several techniques in the empirical finance literature. Specifically, the author investigates the behavior of correlations between risk factors as well as the correlations between factor loadings when using the Fama and French research portfolios as our test assets for different time periods. Additionally, the author examines the variation in the volatility factor loadings across the size- and value-sorted portfolios and assesses whether augmenting conventional pricing models with a volatility factor leads to a higher goodness-of-fit in pricing the 25 size- and value-sorted portfolios.

The author’s results suggest that factor volatilities are high during periods of market turmoil. In addition, the author presents evidence indicating that a factor mimicking innovation in volatility (based on the new VIX) is correlated with the market and momentum factors, while exhibiting the uncorrelated behavior with respect to the size, value and liquidity factors when using data from 1990 through 2011. In this paper, the author finds that the aggregate volatility factor’s correlation with the market and momentum factors increases during crisis periods. In periods of relative market tranquility, correlations decrease significantly. In examining multivariate factor loadings for the test assets, the results provide no clear pattern with regard to the variation of the volatility loadings across the book-to-market and size dimensions. Furthermore, the author finds that conventional pricing models are comparable to FVIX-augmented pricing models, in terms of goodness-of-fit, when pricing the 25 Fama-French size- and value-sorted portfolios. Additionally, when using the FVIX volatility factor to proxy for aggregate volatility risk, the coefficients are never significant statistically, thus revealing that innovations in aggregate volatility based on the new VIX index do not constitute a priced risk factor in the cross-section of returns.

The author’ finding indicates an absence of strong variation of the volatility factor loadings across the Fama-French research portfolios. In particular, the asset pricing results cast doubt on whether a factor mimicking innovations in aggregate volatility based on the new VIX index is priced. In agreement with prior research, the author believes that the inseparability of volatility and jump risk in the VIX can be a possible explanation of the current findings in this paper.

China's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.

This study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.

The results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.

The research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.

This study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

The purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.

Six liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.

The result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.

In the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.