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The current study proposes an integrative framework for examination of determinants of stock returns in US market based on the five-factor Fama and French (FF) model, macroeconomic variables and investor sentimental factors. The study is based on both value weighted and equally weighted monthly portfolio returns of all CRSP firms which are incorporated in the United States and listed on the NYSE, AMEX or NASDAQ.

The study applies PLS-SEM methodology to examine the major determinants of portfolio return.

The study suggests that investor sentiments are the major driving forces which positively influence the portfolio stock returns. The macroeconomic factors, the FF Factors and Momentum factor have negative influences on portfolio stock returns.

The study is the first of its kind which aim to determine the determinants of portfolio returns using the PLS-SEM methodology.

This chapter re-examines the Fama–French (FF) five-factor asset pricing model proposed by Fama and French (2015), since this model has a failure to capture the lower average returns on small stocks and its performance could not fully satisfy the original definitions of those considered factors. From the viewpoint of the econometrics analysis, we consider the inferior performance could be potentially caused by the spurious effect in the five-factor model, which could mislead the statistical inference and yield biased empirical results. We thus employ the CO-AR estimation by Wang and Hafner (2018) to prove the usefulness of the FF five-factor model. Empirical results demonstrate with the CO-AR estimation, the five-factor model indeed properly captures the lower average returns on small stocks and illustrate the sustainability of efficiency of the market, which is in contrast to the findings of Fama and French (2015). However, we propose a new perspective on the seminal five-factor model.

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 authors study the explanatory power of investor rationality and irrationality for value and momentum portfolios. We also examine the relationships during financial crisis events, namely, the US subprime mortgage crisis (2007–2009) and the European debt crisis (2011–2013).

This study examines the influence of investors’ rationality and irrationality on the US stock market, using the multiple linear regression model and the stepwise regression model. Technically, the stepwise regression uses the machine-learning technique, with specific testing methods — forward selection, backward selection and stepwise selection — to find the best-fit model, according to Akaike’s Information Criterion (AIC). Thus, in this study, we will show the best model, as tested by the stepwise regression model.

Our empirical results contribute to the importance of reasons and emotions for stock-market returns and conclude that rationality and irrationality simultaneously explain the value and momentum portfolios, as well as the ETF portfolios. Also, the rational and irrational explanatory powers differ, depending on portfolios and different periods. Rational factors usually explain the volatility of the return to a greater extent than irrational factors. Moreover, during a financial crisis, the irrational factors remarkably increase their importance in explaining returns, especially for the ETF portfolios.

We expect this study’s contribution will show not only academic contribution but also benefit many stakeholders in the financial market. Investors and traders can identify various irrational factors of trading — for example, taking a long position during the panic in the market following the indicators in the models. Managers also reconsider the cost of the company by adding irrational factors when computing the equity’s expected return. Similarly, stock exchanges can adequately adjust their circuit breaker during a pessimistic-investor period. Finally, regulators can evaluate a complete picture of the stock market by adding irrational factors into their considerations.

This paper aims to expand foreign investors' understanding of potential return enhancement and risk diversification advantages offered by equity market of Pakistan through comparing its performance to performances in other markets and investigating what matters for investing in Pakistan's market.

Comparative analysis of Pakistan Stock Exchange is performed using data for 22 developed and 22 emerging markets over the period 1993–2019. Cross-sectional analysis is performed using data for 130 non-financial firms from Pakistan and Carhart (1997) and Fama and French (2015) models are applied. The role of liquidity with five-factor model is analyzed using turnover rate and Amihud (2002) illiquidity cost as liquidity measures.

Pakistan's equity offers substantial diversification benefits if added to developed market portfolios. However, observed large returns come together with inverted premia for most traditional factors indicating that investors may want to invest preferably in big stocks with low book-to-market and momentum. Finally, global investors can invest in high yielding stocks with low liquidity risk owing to positive connection between liquidity and returns.

This study will provide investment model for foreign investors to enhance their portfolio returns. Policy makers in Pakistan must identify regulatory steps to facilitate foreign investments.

To the best of the authors' knowledge, this is the first study which identifies efficiency gains offered by Pakistan's equity for global investors.

In view of organizational inertia, with the occurrence of a major event, though resource rigidity minimizes, however simultaneously, it increases process rigidity, which creates difficulties in motivating managers and dealing with the agency problem. Therefore, keeping in mind the high demand created by the China–Pakistan Economic Corridor and Naya Pakistan Housing Scheme in the cement sector of Pakistan, the purpose of this paper is to investigate the impact of corporate governance (CG) on the cost of equity (COE) in the cement sector, to deal with the problems surging during and after the completion of these projects and highlight further opportunities for the cement sector of Pakistan.

CG is a qualitative concept therefore, eight proxies have been used to measure it along with the two control variables. This study uses balance panel data of six years from 2012 to 2017, collected from 18 companies of the cement sector of Pakistan. Descriptive statistics have been used to describe the data, correlation matrix to see the nature of the relationship, and Pooled OLS as the estimation technique, while to analyze the data a statistical package 13 has been used. To measure the COE, the Capital Asset Pricing Model (CAPM) has been used.

Regression results suggest that block ownership, insider ownership and the board size are insignificant, while CEO tenure is negatively and significantly associated with the COE. Non-executive directors, independence and CEO duality are insignificant; however, diversity is positively and significantly associated with the COE. Moreover, the mean value of the COE is 8.22 percent for the cement sector, while the coefficient of determination of the model under study is 74 percent.

This paper is based on the data from the cement sector of Pakistan only. Therefore, this is the reason that these results cannot be generalized on the whole economy of Pakistan.

This study helps in finding out the COE value specific to the cement sector, which will help this sector to evaluate the capital budgeting decision more precisely and accurately than before. Moreover, the association of diversity as positive, while independence as negative with the COE highlights a room for improvement in the implementation of CG codes by SECP. This study also helps to mitigate the impact of inertia, the after-effects of high demand, and managing the agency problem in the cement sector.

This is the first study using CG data collected just after the revised promulgation of CG codes in 2012, along with a wide range of eight proxies measuring CG and its impact on the COE in the cement sector.

By examining the impact of product innovation on abnormal financial returns following the launch of new products, this study aims to test the explanatory power of a new compound measure of product innovativeness (Ganbaatar and Douglas, 2019).

It is a longitudinal study in which the authors used the compound product innovativeness score (CPIS) for the first time to measure product innovativeness. The abnormal financial returns are estimated through the event study design, where four different models are used. Artificial neural network analysis is done to determine the impact of the CPIS on abnormal returns by utilising a hexic polynomial regression model.

The authors find effect sizes that substantially exceed practically significant levels and that the CPIS explain 65% of the variance in the firm’s abnormal returns in market valuation. Moreover, new-to-the-market novelty predicts 83% of the variation, while new-to-the-firm (catch-up) innovation insignificantly impacts firm value.

This paper demonstrates how the CPIS, an objective and direct measure of product innovativeness, can be used to gain more insight into the innovation effect.

Implications for the business practice of this study include the necessity of relentless innovation by firms in contested differentiated markets, particularly where technological advance is ongoing. Larger and mature firms must practice corporate entrepreneurship to renew their products on a continuous basis to avoid slipping backwards in their markets. Innovation leadership, rather than following the leader, is also important to increase competitive advantage, given the result that innovation followship does not produce abnormal financial returns.

In this study, the authors focused on the effect of product innovativeness on firm performance. While the literature affirms a positive relationship between innovation and firm performance, the effect size of this relationship varies, due largely to the authors contend to simplistic measures of innovativeness. In this study, the authors adopt the relatively novel “compound” measure of product innovativeness (Ganbaatar and Douglas, 2019) to better encapsulate the nuances of both technical novelty and market novelty. This measure of product innovativeness is applicable to firms of all sizes but is more easily applied to entrepreneurial new ventures and SMEs, and it avoids the shortcomings of prior firm-level and subjective measures of innovativeness for both smaller and larger firms. Using a more effective analytical method (Artificial Neural Network), the authors investigated whether there is a “practically” significant effect size due to product innovation, which could be valuable for entrepreneurs in practice. The authors show that the CPIS measure can very effectively explain abnormalities in the stock market, exhibiting a moderate effect size and explaining 65% of the variation in abnormal returns.

Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

Drawing on the work as calling theory, the purpose of this study is to explore how and when career calling promotes taking charge by focusing on the mediating effects of work meaningfulness and felt obligation and the moderating role of family-friendly human resource practice (FF-HRP).

The authors collected data from 293 supervisor–employee dyads at three time points in southeastern China. Path analysis and bootstrap method were used for hypothesis testing.

Employees' perceived career calling positively affected taking charge through work meaningfulness and felt obligation. The positive effects of career calling on work meaningfulness and felt obligation as well as the indirect effect of career calling on taking charge are stronger when employees perceive high levels of FF-HRP.

Organizational interventions should be designed to enhance employees' sense of calling, and the organization should inspire employees to take charge by awakening their perception of work meaningfulness and obligation. Moreover, FF-HRP should be implemented as a form of organizational support.

This research identifies work meaningfulness and felt obligation as mediators that link career calling to taking charge and reveals the role of FF-HRP in amplifying the positive impact of career calling.