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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.

The purpose of this paper is to examine socially responsible investment (SRI) fund performance and investigate the factors influencing fund performance.

The study uses return data from the Morningstar database for 152 SRI funds from January 1995 to May 2015. The initial analysis includes the use of various risk-adjusted performance measures, including Sharpe ratio, Treynor ratio, Information ratio, Sortino ratio and M². The study also uses four factor models, including Jensen single-factor model, Fama–French three-factor model, Carhart four-factor model and Fama–French five-factor model to explain SRI fund returns. Finally, a cross-sectional regression analysis is applied to investigate the determinants of SRI fund returns.

The results show that, on average, the SRI funds provide comparable risk-adjusted returns relative to various benchmark market indices. Market factor is significant in explaining SRI fund returns. Examining each factor model, the results do not support Fama–French’s three-factor model as neither size nor value factor is significant. The author finds weak support for Carhart’s momentum factor along with the market factor. Finally, the Fama–French five-factor model shows market, size and operating profit factors explain SRI fund returns. The study also finds the fund performance is stronger for funds with the higher turnover ratio, the larger fund size and more managerial experience and lower for funds with higher expense ratio. Also, funds formed with negative screening perform better than positive or mixed screened funds.

SRI funds have received considerable attention from investors. This study contributes to the literature by examining SRI fund performance and investigating factors influencing their performance using multiple factor models and cross-sectional regression analysis. The findings are relevant for investors who demand responsible investment opportunities without sacrificing returns for nonfinancial screenings. Findings also suggest that investors should consider fund characteristics when selecting SRI funds.

The purpose of this paper is to demonstrate that common method variance, specifically single-source bias, threatens the validity of a university-created student assessment of instructor instrument, suggesting that decisions made from these assessments are inherently flawed or skewed. Single-source bias leads to generalizations about assessments that might influence the ability of raters to separate multiple behaviors of an instructor.

Exploratory factor analysis, nested confirmatory factor analysis and within-and-between analysis are used to assess a university-developed, proprietary student assessment of instructor instrument to determine whether a hypothesized factor structure is identifiable. The instrument was developed over a three-year period by a university-mandated committee.

Findings suggest that common method variance, specifically single-source bias, resulted in the inability to identify hypothesized constructs statistically. Additional information is needed to identify valid instruments and an effective collection method for assessment.

Institutions are not guaranteed valid or useful instruments even if they invest significant time and resources to produce one. Without accurate instrumentation, there is insufficient information to assess constructs for teaching excellence. More valid measurement criteria can result from using multiple methods, altering collection times and educating students to distinguish multiple traits and behaviors of individual instructors more accurately.

This paper documents the three-year development of a university-wide student assessment of instructor instrument and carries development through to examining the psychometric properties and appropriateness of using this instrument to evaluate instructors.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

This study aims to develop a model for readiness measurement and to study readiness levels for online testing of undergraduate students in Thailand’s distance education programs.

In total, 870 undergraduate students enrolled in the 2022 academic year of a Thai university were sampled for the study. The samples were divided into two groups: Group 1 comprised 432 students who underwent exploratory factor analysis (EFA) and Group 2 comprised 438 students who underwent second-order confirmatory factor analysis (CFA). Both were multi-stage random samples. Descriptive statistics, item-total correlations (ITCs), coefficient correlations, EFA and second-order CFA were used.

The readiness for the online testing model comprised 5 factors and 33 indicators. These included self-efficacy (SE) in utilizing technology (nine indicators), self-directed learning (SL) for readiness testing (six indicators), adequacy of technology (AT) for testing (five indicators), acceptance of online testing (AC) (seven indicators) and readiness training for testing (six indicators). The model was congruent with empirical data, and the survey results indicated that students were highly prepared at the “high” level.

This study disclosed several factors and indicators involved in the readiness for online testing. The university may use these findings in preparing its students for online testing for better achievement.

These findings may serve as a framework for the analysis of the readiness issues for online testing of undergraduate students and also offer guidance to the universities preparing to offer online testing.

Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance.

This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix.

The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications.

This paper provides an insight into the current state and the future changes in carbon emissions.

The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe.

To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model.

The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion.

Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios.

This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As different public-sector (reform) models imply different ideal typical personality traits for civil servants, the purpose of this paper is to ask the question to what extent personality requirements that governments demand from their employees have evolved over time in line with these models.

The authors analyzed the use of big-five traits in a sample of 21,003 job advertisements for local government jobs published between 1980 and 2017, applying tools for computer-assisted text analysis.

Using multilevel regression analyses, the authors conclude that, over time, there is a significant increase in the use of personality descriptors related to all big-five factors.

The authors postulate that governments nowadays are actively looking for the “renaissance bureaucrat” in line with the neo-Weberian bureaucracy paradigm. The authors end with a discussion of both positive and negative consequences of this development.

First, the authors explicitly link personality, public administration, and public management using the Abridged Big-Five-Dimensional Circumflex model of personality. Second, by linking observed trends in civil servant personality requirements to larger theories of public-sector reform models, the authors narrow the gap between public administration theories and practice. Third, the software tools that the authors use to digitalize and analyze a large number of documents (the job ads) are new to the discipline of public administration. The research can therefore serve as a guideline for scholars who want to use software tools to study large amounts of unstructured, qualitative data.

This paper aims to present a model to survey if effective destination management can manage (unsustainable) overtourism from the perspective of residents’ quality of life (QOL).

A constructivist approach, based on factors taken from conceptual overtourism model (Mihalic, 2020), was used to propose an overtourism QOL management model. Relationships among the factors were analysed with a path analyses model with two second-order latent factors. The model was tested in a real setting, the city of Ljubljana.

The proposed theoretical model is comprised of five factors: positive tourism impacts, negative tourism impacts, irritation with overtourism, residents’ QOL and destination management. Empirical tests confirmed the model. Positive tourism impacts positively affected residents’ QOL via destination management. Negative tourism impacts created overtourism-based resident irritation and negatively impacted their QOL.

The model was limited to one group of sustainable tourism stakeholders: residents of a destination. The sustainability performance of tourism was only assessed based on residents’ QOL.

The proposed model adds to the conceptual knowledge of tourism and may be useful for (sustainable) destination managers to monitor the existence and causes of overtourism and may help to focus efforts to manage the causes of overtourism irritation and improve residents’ QOL.

Overtourism is a concern for residents of tourism destinations who become irritated by unsustainable tourism impacts on community resources and their QOL. The suggested model is the first to address destination management’s ability to manage unsustainable overtourism.

本文采用建构主义的方法, 基于概念性的过度旅游模型（Mihalic, 2020年）中的因素, 提出了过度旅游中居民生活质量（QOL）管理模型。这些因子之间的关系是通过对包含两个二阶潜在因子的模型的路径分析得到的。该模型在卢布尔雅那市的真实情况中进行了测试。

本文提出了这样一个模型, 从居民的生活质量（QOL）角度出发, 调查有效的目的地管理是否可以管理（不可持续的）过度旅游。

理论模型由五部分组成：正面的旅游影响, 负面的旅游影响, 过度旅游带来的恼怒, 居民的生活质量和目的地管理。实证检验证实了该模型。积极的旅游业通过目的地管理对居民的生活质量产生了积极影响。负面的旅游影响造成了基于过度旅游的居民恼怒情绪, 并对其生活质量产生了负面影响。

该模型仅基于一个可持续的旅游业利益相关者：目的地居民。旅游业的可持续发展绩效仅根据居民的生活质量来评估。

社会和实际意义： 提出的模型增加了旅游的概念性知识, 并且可能有助于（可持续）目的地管理者监督过度旅游的存在和原因, 并且集中精力管理过度旅游引起的居民恼怒情绪, 并改善居民的生活质量。

对于旅游目的地的居民来说, 过度旅游是一个令人担忧的问题, 他们因不可持续的旅游业对社区资源及其生活质量的影响而感到不快。本模型是第一个解决目的地管理机构管理不可持续的过度旅游的能力的模型。

Para proponer un modelo de gestión del sobreturismo QOL, se utilizó un enfoque constructivista, basado en factores tomados del modelo conceptual de sobreturismo (Mihalic, 2020). Las relaciones entre los factores se analizaron con un modelo de análisis de rutas con dos factores latentes de segundo orden. El modelo se probó en un escenario real, la ciudad de Ljubljana.

En este documento se presenta un modelo para estudiar si una gestión eficaz del destino puede gestionar el exceso de turismo (insostenible) desde la perspectiva de la calidad de vida de los residentes (QOL).

El modelo teórico propuesto comprende cinco factores: impactos positivos del turismo, impactos negativos del turismo, irritación por el exceso de turismo, calidad de vida de los residentes y gestión del destino. Las pruebas empíricas confirmaron el modelo. Los impactos positivos del turismo afectaron positivamente la calidad de vida de los residentes a través de la gestión del destino. Los impactos negativos del turismo crearon una irritación de los residentes basada en el exceso de turismo y tuvieron un impacto negativo en su calidad de vida.

El modelo se limitaba a un grupo de interesados en el turismo sostenible: los residentes de un destino. El desempeño de la sostenibilidad del turismo sólo se evaluó en base a la calidad de vida de los residentes.

Implicaciones sociales y prácticas: El modelo propuesto contribuye al conocimiento conceptual del turismo y puede ser útil para que los gestores de destinos (sostenibles) vigilen la existencia y las causas del exceso de turismo y pueda ayudar a centrar los esfuerzos en la gestión de las causas de la irritación del exceso de turismo y mejorar la calidad de vida de los residentes.

El exceso de turismo es una preocupación para los residentes de los destinos turísticos que se irritan por los impactos insostenibles del turismo en los recursos de la comunidad y su QOL. El modelo sugerido es el primero que aborda la capacidad de la gestión del destino para gestionar el sobreturismo insostenible.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.