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A closer look at industry-associated value premium: evidence from India
This paper examines whether the academic literature-promised value premium has any industry association in the Indian equity market and the relationship between stock returns, value, and size within and across industries. We examine all listed firms trading at BSE India between 1999-2020, using CAPM and Fama-French three-factor models on each firm-levels and industry-level portfolio. The positive and significant value effect was found to exist in 17 out of 21 industry groups. Both industry and firm-level value effects are identified; however, the firm-level effect seems more prominent. Furthermore, the value effect is most substantial in small-cap value stocks of value- and growth-oriented industries, large-cap value stocks of value-oriented industry groups, then small-cap growth stocks of value- and growth-oriented industries and large-cap growth stocks of value- and growth-oriented industries. We also show evidence confirming the claim that value premium results from investors challenging higher returns from firms and industries operating in higher risk and distressing constraints. Copyright 2022 Inderscience Enterprises Ltd. -
Size, Value Effects and the Explanatory Power of Pricing Models: Evidence from BSE listed Indian Industries
The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. This paper aims to examine the size, value effects and explanatory power of three well-known pricing models - CAPM, three-and five-factor- across and within 15 Indian industries. The study considers all firms listed on Indian largest stock exchange, BSE (Bombay stock exchange), between 1999-2021 by developing portfolios using firm size/value, size/investment and size/profitability risk characteristics. The study employs both univariate and multivariate methods, including time series, GRS statistics, and cross-sectional models within and across industries portfolios. Results indicated that size and value effects exist in almost all industries, presenting that size and value anomalies are the most prominent determinants for industry-level equity returns. In addition, the profitability and investment effects were also investigated; however, the results are mixed from industry to industry. In the case of the explanatory power of pricing models, the five-factor performs much better within and across industry portfolios than other pricing models; however, the models' effectiveness varies by industry. We also reported that investors who seek to allocate funds within and across industries tend to be expected reasonably stable returns and conceivably predictable; the findings of this study contribute to the existing literature on asset pricing and portfolio management in emerging markets. The Author(s) 2022. -
Is Industry-Specific Value Premium Declining? Evidence from India
This article examines whether the literature promised value effect exists and the changing nature of value premium at the industry level. It also determines the value premiums strength by controlling the January effect within and across the regulated industry groups. This is done by utilizing the two most prominent pricing models: FamaFrench three- and five-factor, considering all listed firms trading at BSE India between 1999 and 2020. The results show that a significant value effect exists in 15 of the 17 regulated industry groups over 21.5 years, while sub-period analysis revealed variation in the value effect at industry-based portfolio returns. We developed quintile and multivariate portfolios within and across the industries. Results show that the industry-specific value premium has been relatively low in the current decade due to decreasing industry portfolio returns and increasing P/B ratios within industry groups. The study also used the GRS test to explore the explanatory power of models. Results indicated that the explanatory power of models has declined in post-crisis periods. While controlling the January effect, the value premium has slightly diminished within and across the industry groups in the recent decade. We also observed that investors who seek to allocate assets within and across industries are likely to have potentially predictable and pretty stable returns. While other countries have found industry-specific value premiums, no such study has been conducted in India. As a first attempt, these findings are relevant for investors and academia. 2022 Management Development Institute. -
Reaction of Indian Stock Market to Outbreak of COVID-19: An Empirical Analysis of Extreme Inter-day Movements
The contagious COVID-19 pandemic has been considered a massive global crisis since World War II and has disturbed business and economic activities across the globe. The current study examined the reaction of the stock markets to the outbreak of COVID-19, considering the extreme inter-day movements in the Indian stock market. The extreme inter-day movements in S&P CNX Nifty-50 have been identified during the study period from January 2020 to December 2021 and further classified into decline and gain events based on positive and negative announcements related to COVID-19. The study utilized an event study approach and panel regression for empirical investigation. The results of the event study analysis illustrate that the significant abnormal loss ranges from 12.86% to 2.47% for the major decline events and significant abnormal return from 8.43% to 3.23% for the gain events. The regression analysis results showed that real return and Central Bank Policy rate have a considerable impact on the abnormal returns during COVID-19. The studys findings are helpful to policy implications that identified the need to focus on financial education and strengthen the health and finance-related policies to deal with such pandemics in the future. 2023 MDI. -
Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA): A developmental cohort study protocol
Background: Low and middle-income countries like India with a large youth population experience a different environment from that of high-income countries. The Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), based in India, aims to examine environmental influences on genomic variations, neurodevelopmental trajectories and vulnerability to psychopathology, with a focus on externalizing disorders. Methods: cVEDA is a longitudinal cohort study, with planned missingness design for yearly follow-up. Participants have been recruited from multi-site tertiary care mental health settings, local communities, schools and colleges. 10,000 individuals between 6 and 23 years of age, of all genders, representing five geographically, ethnically, and socio-culturally distinct regions in India, and exposures to variations in early life adversity (psychosocial, nutritional, toxic exposures, slum-habitats, socio-political conflicts, urban/rural living, mental illness in the family) have been assessed using age-appropriate instruments to capture socio-demographic information, temperament, environmental exposures, parenting, psychiatric morbidity, and neuropsychological functioning. Blood/saliva and urine samples have been collected for genetic, epigenetic and toxicological (heavy metals, volatile organic compounds) studies. Structural (T1, T2, DTI) and functional (resting state fMRI) MRI brain scans have been performed on approximately 15% of the individuals. All data and biological samples are maintained in a databank and biobank, respectively. Discussion: The cVEDA has established the largest neurodevelopmental database in India, comparable to global datasets, with detailed environmental characterization. This should permit identification of environmental and genetic vulnerabilities to psychopathology within a developmental framework. Neuroimaging and neuropsychological data from this study are already yielding insights on brain growth and maturation patterns. 2019 The Author(s). -
Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants
Cognitive abilities are markers of brain development and psychopathology. Abilities, across executive, and social domains need better characterization over development, including factors that influence developmental change. This study is based on the cVEDA [Consortium on Vulnerability to Externalizing Disorders and Addictions] study, an Indian population based developmental cohort. Verbal working memory, visuo-spatial working memory, response inhibition, set-shifting, and social cognition (faux pas recognition and emotion recognition) were cross-sectionally assessed in > 8000 individuals over the ages 623 years. There was adequate representation across sex, urban-rural background, psychosocial risk (psychopathology, childhood adversity and wealth index, i.e. socio-economic status). Quantile regression was used to model developmental change. Age-based trajectories were generated, along with examination of the impact of determinants (sex, childhood adversity, and wealth index). Development in both executive and social cognitive abilities continued into adulthood. Maturation and stabilization occurred in increasing order of complexity, from working memory to inhibitory control to cognitive flexibility. Age related change was more pronounced for low quantiles in response inhibition (??4 versus =2 for higher quantiles), but for higher quantiles in set-shifting (? > ?1 versus ?0.25 for lower quantiles). Wealth index had the largest influence on developmental change across cognitive abilities. Sex differences were prominent in response inhibition, set-shifting and emotion recognition. Childhood adversity had a negative influence on cognitive development. These findings add to the limited literature on patterns and determinants of cognitive development. They have implications for understanding developmental vulnerabilities in young persons, and the need for providing conducive socio-economic environments. 2023 Elsevier B.V. -
Breeding distrust during artificial intelligence (AI) era: howtechnological advancements, jobinsecurity and job stress fuel organizational cynicism?
Purpose: This study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees views on their work future and its probable influence on their job behaviors in this era of technology. Design/methodology/approach: A quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling. Findings: The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employees cynicism. Practical implications: The current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress. Originality/value: The study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements. 2024, Emerald Publishing Limited. -
A novel map matching algorithm for real-time location using low frequency floating trajectory data
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd. -
INVESTING IN WOMEN, INVESTING IN THE PLANET: QUANTIFYING THE IMPACT OF WOMEN'S EMPOWERMENT ON ENVIRONMENTAL SUSTAINABILITY; [INVESTIR NAS MULHERES, INVESTIR NO PLANETA: QUANTIFICAR O IMPACTO DO EMPODERAMENTO DAS MULHERES NA SUSTENTABILIDADE AMBIENTAL]; [INVERTIR EN LAS MUJERES, INVERTIR EN EL PLANETA: CUANTIFICAR EL IMPACTO DEL EMPODERAMIENTO DE LAS MUJERES EN LA SOSTENIBILIDAD AMBIENTAL]
Objective: This study finds out the correlation between the indicators of womens empowerment, including variables like gender parity index in tertiary education, female labour force participation and seats held by women in national parliament, and a variable of environmental sustainability such as CO2 emissions (metric tons per capita). The aim is to analyse existing datasets to know the impact of independent variables on dependent variable. Method: The study uses multiple linear regression to evaluate the effects of independent variables indicators of women's empowerment on the dependent variable, CO2 emissions, using secondary data from the World Bank covering the years 1990 to 2022. The Breusch-Pagan and Breusch-Godfrey LM tests are used to look at heteroskedasticity and autocorrelation, respectively, and VIF is used to find multicollinearity. Results and Conclusion: The study concludes that there is a statistically significant relationship between lower CO2 emissions and increases in the percentage of female seats in the national parliament (-3.73) and higher female labour force participation (-6.04). The gender parity index (GPI) in tertiary education, which is -0.2997, does not, however, appear to have a statistically significant impact on CO2 emissions. Implications: This research can serve as a cause for redesigning gender-responsive environmental initiatives and promoting a more sustainable and equitable future. Originality/Value: This study contributes empirical knowledge to the body of literature by showing the potential contribution of women's empowerment in addressing environmental issues and emphasising the significance of taking gender into account in environmental policy and decision-making processes. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
I Can Live Without Banks, but Not Without Banking: Role of Trust on Loyalty and Evangelism
The purpose of this paper is to examine the antecedents of e-banking loyalty and evangelism via threefold construct of WEQUAL (usability, information quality, and service interaction) of public sector banks operating in India. Moreover, it also investigates the mediating role of consumers' trust on the website quality of these banks and their impact on e-banking loyalty and evangelism. The data was collected from 243 respondents through online questionnaire. In order to develop the model and test the hypotheses, partial least square structural equation modeling (PLS-SEM) was done through Smart PLS version 3.2.9. Results assert that website quality of banks positively influences the trust of consumers via usability, information quality, and service interaction. Also, consumer trust plays a mediation role between WEBQUAL constructs and e-banking loyalty and evangelism. 2021 IGI Global. All rights reserved. -
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors -
Hazard identification of endocrine-disrupting carcinogens (EDCs) in relation to cancers in humans
Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens. 2024 Elsevier B.V. -
Being socially responsible: How green self-identity and locus of control impact green purchasing intentions?
This paper investigates the influence of green self-identity (GSI) and two attributes of locus of control, namely external environmental locus of control (ExLOC) and pro-environmental locus of control (PELOC), to predict perceived consumers effectiveness (PCE) on green purchase intentions (GPI) using attribution theory. For this study, data from 391 Indian consumers were analyzed using PLS-SEM via SMARTPLS version 3.2.9. Results show that GSI positively influences both ExLOC and PELOC. Furthermore, both aspects of locus of control are significant positive predictors of PCE and have partial mediation roles. The results not only imply comprehensively expound the process of green buying intentions of consumers through self-identity but also addresses the process of attribution. The study applied the Importance Performance Map Analysis (IPMA) to compare the relative importance and performance of three antecedents (i.e., ELOC, GSI, and PCE). The finding is of utmost importance for practitioners and public authorities to design more focused strategies to increase GPI among the masses to enhance the sales of green products. 2022 The Authors -
The impact of eco-innovation ongreen buying behaviour: the moderating effect of emotional loyalty and generation
Purpose: This study intends to contribute to the literature of eco-innovation by examining the pro-environmental intentions and behaviour among consumers through their understanding of eco-innovation. Thus, the relationship among eco-innovation, general pro-social attitude, generativity, environmental concern, purchasing intentions and buying environmentally friendly products and the differences of the relationship between high and low emotional loyalty and Generation Y and Z were investigated via structural equation modelling (SEM). Design/methodology/approach: Data were collected through an online questionnaire directed to Indian consumers, and analysis was done through partial least square structural equation modelling (PLS-SEM) in two stages, i.e. measurement model and structural model. Findings: Results confirm the relationships established in the proposed model, and some differences were found between the levels of emotional loyalty and the Generations Y and Z. The research shows that individualistic norms and perceived marketplace influence play a purposeful role in transforming environmental concerns into buying behaviour towards eco-innovation-driven products. Practical implications: From a policy and management perspective, the results not only imply the importance of continuous performance and environmental improvement but also those policies hindering diffusion and adoption need to be addressed. Green buying is an elusive task but can be opportunely attained by marketers by adding elements of eco-innovations and understanding mindsets of consumers to create winwin situations for themselves and consumers. Originality/value: The results reinforced that emotional loyalty and Generations Y and Z vitally impact consumers' green buying decision within the framework of eco-innovation and cognitive factors. 2022, Emerald Publishing Limited. -
Option or necessity: Role of environmental education as transformative change agent
There is a consensus around the importance of environmental education in mitigating the ill effects of environmental problems and preserving the natural environment and promoting green behaviours. The present paper studies the role of environmental education based on transformative learning theory. It intends to present and test a model proposal using sequential mediation analysis of several constructs as the Environmental Education Support (EES) and Volunteer Attitude (VA). A quantitative study was carried out by using data obtained through online questionnaires from several Indian and Brazilian Higher Education Institutions. A multivariate statistical method was employed to analyse the data by using partial least squares structural equation modelling. The results demonstrated that environmental education positively influences students environmental concern, willingness to be environmentally friendly, and volunteer attitude. As a novelty, it reports that environmental education beliefs, concern for the environment and willingness to be environmentally friendly sequentially mediate the relationship between environmental education support and volunteering attitude. 2023 Elsevier Ltd -
Relating the role of green self-concepts and identity on green purchasing behaviour: An empirical analysis
At present, consumers in emerging economies are becoming more conscious about environmental well-being. Therefore, organizations compete to make their products and practices more eco-friendly. Several studies have tried to explain the relationship between green consumerism and an individual's buying behaviour using traditional theories. However, there is quite a challenge in understanding the influence of green self-concept (GSC) and green self-identity (GSI) in predicting the green purchase intention (GPI) of consumers. Therefore, the authors developed six hypotheses to assess the relation between self-concept and the GPI. The survey was conducted, and the responses were evaluated through the partial least square (PLS) method. The authors analysed the measurement model results (n = 717) and the direct and indirect mediating effect of the latent variable contributing to GPI. The measurement model results show that a significant relationship exists in the proposed model, namely, GSCs ? green purchasing intentions, product self-concept (PSC) ? green purchasing intentions and GSI ? green purchasing intentions. Further, the GSI acted as a mediator for the measurement model. The implications of the study can be used to understand the green consumer behavior in developing new strategies and policies for the organizational practice in emerging economies. 2020 ERP Environment and John Wiley & Sons Ltd. -
Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems. 2023 Elsevier Ltd -
Broad-band mHz QPOs and spectral study of LMC X-4 with AstroSat
We report the results of broad-band timing and spectral analysis of data from an AstroSat observation of the high-mass X-ray binary LMC X-4. The Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) instruments onboard the AstroSat observed the source in 2016 August. A complete X-ray eclipse was detected with the LAXPC. The 340 keV power density spectrum showed the presence of coherent pulsations along with a ?26 mHz quasi-periodic oscillation feature. The spectral properties of LMC X-4 were derived from a joint analysis of the SXT and LAXPC spectral data. The 0.525 keV persistent spectrum comprised of an absorbed high-energy cut-off power law with photon index of ? ? 0.8 and cut-off at ?16 keV, a soft thermal component with kTBB ? 0.14 keV, and Gaussian components corresponding to Fe K?, Ne IX, and Ne X emission lines. Assuming a source distance of 50 kpc, we determined 0.525 keV luminosity to be ?2 1038 erg s?1 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
HTLML: Hybrid AI Based Model for Detection of Alzheimers Disease
Alzheimers disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brains ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Nae base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. 2022 by the authors. -
Hybrid HOG-SVM encrypted face detection and recognition model
Security plays a major role in an individuals life to win this world with highly secure and authentic lifestyle with the digital equipments. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time. 2022 Taru Publications.