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This study aims to organise and present the development of asset pricing models in the international environment. The stock market integration and cross-listing lead us to another objective of bibliometric analysis for “International Asset Pricing” to provide a complete overview and give scope and directions for future research.

Web of Science database is used to search with “International Asset Pricing.” Of 3,438 articles, 2,487 articles are selected for the final bibliometric analysis. Various research such as citation analysis, keyword analysis, author’s and corresponding author's analysis have been conducted.

The bibliometric analysis finds that the USA comes out to be the country where the maximum research was conducted on the topic. The keyword analysis was also analysed to evaluate the significant areas of the research. Risk, return and international asset pricing are the most frequently used keywords. The year 2020 has the maximum number of published research articles and citations due to the change in the market structure worldwide and the effect of Covid-19 across the world.

The present paper provides the collection, classification and comprehensive analysis of “International Asset pricing,” which may help the academicians, researchers and practitioners for future research for the relevant subject area.

Asset pricing revolves around the core aspects of risk and expected return. The main objective of the study is to test different asset pricing models for the Indian securities market. This paper aims to analyse whether leverage and liquidity augmented five-factor model performs better than Capital Asset Pricing Model (CAPM), Fama and French three-factor model, leverage augmented four-factor model and liquidity augmented four-factor model.

The data for the current study comprises records on prices of securities that are part of the Nifty 500 index for a time frame of 14 years, that is, from October 2004 to September 2017 consisting of 183 companies using time series regression.

The results indicate that the five-factor model performs better than CAPM and the three-factor model. The model outperforms leverage augmented and liquidity augmented four-factor models. The empirical evidence shows that the five-factor model has the highest explanatory power among the entire asset pricing models considered.

The present study bears certain useful implications for various stakeholders including fund managers, investors and academicians.

This study presents a five-factor model containing two additional factors, that is, leverage and liquidity risk along with the Fama-French three-factor model. These factors are expected to give more value to the model in comparison to the Fama-French three-factor model.

This paper aims to empirically indicate the factors influencing stock liquidity premium (i.e. the relationship between liquidity and stock returns) in one of the leading European emerging markets, namely, the Polish one.

Various firms’ characteristics and market states are analysed as potentially affecting liquidity premiums in the Polish stock market. Stock returns are regressed on liquidity measures and panel models are used. Liquidity premium has been estimated in various subsamples.

The findings vividly contradict the common sense that liquidity premium raises during the periods of stress. Liquidity premium does not increase during bear markets, as investors lengthen the investment horizon when market liquidity decreases. Liquidity premium varies with the firm’s size, book-to-market value and stock risk, but these patterns seem to vanish during a bear market.

This is one of the first empirical papers considering conditional stock liquidity premium in an emerging market. Using a unique methodological design it is presented that liquidity premium in emerging markets behaves differently than in developed markets.

This paper aims to test three parametric models in pricing and hedging higher-order moment swaps. Using vanilla option prices from the volatility surface of the Euro Stoxx 50 Index, the paper shows that the pricing accuracy of these models is very satisfactory under four different pricing error functions. The result is that taking a position in a third moment swap considerably improves the performance of the standard hedge of a variance swap based on a static position in the log-contract and a dynamic trading strategy. The position in the third moment swap is taken by running a Monte Carlo simulation.

This paper undertook empirical tests of three parametric models. The aim of the paper is twofold: assess the pricing accuracy of these models and show how the classical hedge of the variance swap in terms of a position in a log-contract and a dynamic trading strategy can be significantly enhanced by using third-order moment swaps. The pricing accuracy was measured under four different pricing error functions. A Monte Carlo simulation was run to take a position in the third moment swap.

The results of the paper are twofold: the pricing accuracy of the Heston (1993) model and that of two Levy models with stochastic time and stochastic volatility are satisfactory; taking a position in third-order moment swaps can significantly improve the performance of the standard hedge of a variance swap.

The limitation is that these empirical tests are conducted on existing three parametric models. Maybe more critical insights could have been revealed had these tests been conducted in a brand new derivatives pricing model.

This work is 100 per cent original, and it undertook empirical tests of the pricing and hedging accuracy of existing three parametric models.

The aim of this paper is to examine the role of liquidity in asset pricing in a tiny market, such as the Portuguese. The unique setting of the Lisbon Stock Exchange with regards to changes in classification from an emerging to a developed stock market, allows an original answer to whether changes in the development of the market affect the role of liquidity in asset pricing.

The authors propose and compare two alternative implications of liquidity in asset pricing: as a desirable characteristic of stocks and as a source of systematic risk. In contrast to prior research for major stock markets, they use the proportion of zero returns which is an appropriated measure of liquidity in tiny markets and propose the separated effects of illiquidity in a capital asset pricing model framework over the whole sample period as well as in two sub-samples, depending on the change in classification of the Portuguese market, from an emerging to a developed one.

The overall results of the study show that individual illiquidity affects Portuguese stock returns. However, in contrast to previous evidence from other markets, they show that the most traded stocks (hence the most liquid stocks) exhibit larger returns. In addition, they show that the illiquidity effects on stock returns were higher and more significant in the period from January 1988 to November 1997, during which the Portuguese stock market was still an emerging market.

These findings are relevant for investors when they make their investment decisions and for market regulators because they reflect the need of improving the competitiveness of the Portuguese stock market. Additionally, these findings are a challenge for academics because they exhibit the need for providing alternative theories for tiny markets such as the Portuguese one.

The results have important implications for individual and institutional investors who can take into account the peculiar effect of liquidity in stock returns to make proper investment decision.

The Portuguese market provides a natural experimental area to analyse the role of liquidity in asset pricing, because it is a tiny market and during the period studied it changed from an emerging to a developed stock market. Moreover, the authors have to highlight that previous evidence almost exclusively focuses on the US and major European stock markets, whereas studies for the Portuguese one are scarce. In this context, the study provides an alternative methodological approach with results that differ from those theoretically expected. Thus, these findings are a challenge for academics and open a theoretical and a practical debate.

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 paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This research surveys the recent surge of empirical studies on transfer pricing manipulation by multinational enterprises (MNEs), tax-motivated transfer pricing, particularly from the year 1990 to present. The review tackles transfer pricing income shifting behavior of MNEs from three different perspectives: taxation relationship with profitability, intrafirm trade, and foreign direct investment (FDI). There have been significant developments and contributions in this field, despite many limitations, mainly concerning the availability of micro-data in general, (specifically intrafirm trade data which allows capturing much of the heterogeneity which is dangling within inter-sectors), and the tax measurement issue. Yet, this area of study is still developing and promises more achievements.

This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model.

This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy.

The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market.

The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner.

Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.