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The purpose of this study is to provide a new way to optimize a portfolio and to show that combining the Hurst exponent and wavelet analysis may help to increase portfolio returns.

The authors use the Hurst exponent and wavelet analysis to study the long-term dependencies between sovereign bonds and sectoral indices of India. The authors further construct and evaluate the performance of three portfolios constructed on the basis of Hurst standard deviation (SD) – global minimum variance (GMV), most diversified portfolio (MDP) and equal risk contribution (ERC).

The authors find that an ERC portfolio generates positive superior return as compared other two. Since our sample includes periods of two crisis – post-2007 financial crisis and the ongoing pandemic, this study reveals that combining government bond with equities and gold provides a higher returns when the portfolios are constructed using the risk exposures of each asset in the overall portfolio risk.

The findings provide guidance to portfolio managers by helping them to select assets using the Hurst approach and wavelet analysis thereby increasing the portfolio returns.

In this study, the authors use a combination of Hurst exponent and wavelet analysis to understand the long-term dependencies among various assets and provide a new methodology to optimize a portfolio. As far as the authors’ knowledge, no study in the past has attempted to provide a joint framework for portfolio optimization and therefore this study is the first to apply this methodology.

The purpose of this study is to empirically investigate the variables having an impact on profitability of public private partnerships (PPPs) in India using a balanced panel data of 171 unlisted PPPs from different infrastructure sectors such as road, power generation, real estate and ports.

Estimations were done using Arellano–Bond dynamic panel data estimation and seemingly unrelated regression models on a balanced panel data of 855 firm-years for 171 unlisted PPPs in India. To further test the estimation robustness, panel-corrected standard errors model was used.

The study findings indicate that in firm-specific factors, leverage, size, non-debt tax shield, growth and risk have significant positive impact on PPPs’ profitability, whereas in macroeconomic factors, only inflation has significant positive relationship. Although the relationship of all determinants is in sync with various theories and approaches, but these are not significant. Using the robustness test, the results are found to be robust and consistent with resource-based view and strategy-structure-performance approaches.

As PPPs are gaining prominence in the development of infrastructural resources, their profitability is of significant importance to drive private investments in infrastructure development, the identification of factors which determine profitability is critical for researchers, practitioners, policymakers and fund providers such as equity investors and debt providers.

The empirical literature on profitability determinants is focused on various sectors including small and mid-size enterprises (SMEs) and micro firms, but to the best of the authors’ knowledge, this is the first study, in both developed and developing economies, to empirically investigate the determinants of profitability for PPPs.

The International Journal of Organizational Analysis (IJOA) is a leading journal that has published high-quality research focused on various facets of organizational analysis since 1993. This paper aims to conduct a retrospective analysis of the IJOA journey from 2005 to 2020.

The data used in this study was extracted using the Scopus database. The bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of the journal. The mapping of bibliographic data is carried using VOSviewer and Biblioshiny.

The study findings indicate that IJOA has grown for publications and citations since its inception. Five significant research directions emerged, i.e. organizational diagnostics, organization citizenship behaviour, organizational commitment to employee retention, psychological capital and firm performance, based on cluster analysis of IJOA’s publications.

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJOA. The study presents the key themes and trends emerging from a leading journal, considered a high-quality journal, for researching various facets of organizational functioning by academicians, scholars and practitioners.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

The objective of this systematic literature review (SLR) is to outline the existing research in the field of infrastructure project finance (IPF). This paper aims to summarise the academic and practitioner research to highlight the benefits of adopting IPF structures in uncertain environments. By highlighting all conceptual and applied implications of IPF, the study identifies future research directions to develop a holistic understanding of IPF.

The SLR is based on 125 articles published in peer-reviewed journals during 1975–2019. After providing a brief overview of IPF, research methodology and citation, publication and author analysis, the SLR presents the various domains around which existing research in IPF is focussed and provides future research propositions in each domain.

The study found that despite the increased usage of IPF, academic and practitioner research in the field is lagging. Also, with increased usage of IPF in emerging and under-developed economies, IPF structure presents a perfect setting to understand how investment and financing are interlinked and how to overcome the institutional voids, socio-economic risks and inter-partner differences by IPF structures.

This literature review paper is based on the research in IPF between 1975 and 2019. To the best of the authors’ understanding, the SLR is the first focussed study detailing a methodical and thorough compendium of existing studies in the IPF domain. By focussing on various domains of IPF research, this paper presents future research avenues in the field.

This study aims to explore peer performance as the motivation behind gross profit manipulation through two different channels, namely, cost of goods sold (COGS) misclassification and revenue misclassification.

Gross profit expectation model (Poonawala and Nagar, 2019) and operating revenue expectation model (Malikov et al., 2018) are used to measure COGS and revenue misclassification, respectively. The panel data regression models are used to analyze the data for this study.

The study results show that firms engage in gross profit manipulation to meet the industry’s average gross margin, implying that peer performance is an important benchmark that firms strive to achieve through misclassification strategies. Further results exhibit that firms prefer COGS misclassification over revenue misclassification for manipulating gross profit, implying that firms choose the shifting strategy based on the relative advantage of each shifting tool.

The findings suggest that firms that just meet or slightly beat industry-average profitability levels are highly likely to engage in classification shifting (CS). Thus, investors and analysts should be careful when evaluating such firms by comparing them with other firms in the same industry.

First, this study is among earlier attempts to investigate CS motivated by peer performance. Second, this study investigates both tools of gross profit manipulation by taking a uniform sample of firms over the same period and provides compelling evidence that firms prefer one shifting tool over another depending on the relative advantage of each shifting tool.

The authors aim at investigating different forms of classification shifting (CS). CS is a novel form of earnings management under which managers misclassify income statement line items and cash flow statement line items with an intent to report favorable operating performance of firms. In particular, the authors check the existence of revenue misclassification, expense misclassification and cash flows misclassification among Indian firms by taking the uniform sample of firms over a single period.

Operating revenue model (Malikov et al., 2018), core earnings expectation model (McVay, 2006) and operating cash flows model (Roychowdhury, 2006) are employed for measuring revenue misclassification, expense misclassification and cash flows misclassification, respectively. The panel data regression models are used to analyze the data for this study.

Based on the sample of 12,870 Bombay Stock Exchange (BSE) listed firm-years observations between 2010 and 2018, we find that, on average, Indian firms are engaged in revenue misclassification rather than expense misclassification to report inflated core earnings. Firms are found to be engaged in cash flows misclassification too. Besides, we find that magnitude of shifting is greater among larger firms. Results also establish that adoption of Ind AS increases the scope of shifting practices. These results are based on several robustness checks.

The results suggest that investors conduct a comprehensive review of the items of financial statements before using them in their portfolio valuation. It suggests auditors check the basis of revenue classification and standard-setting authorities, like ICAI in India, to make more mandatory disclosure requirements for classification of revenues and cash flows. It suggests lenders not to make lending decisions by looking at the operating performance metrics, as CS is the most preferred tool to positively influence the perception of lenders toward operating performance.

It is the first study that investigates different forms of classification shifting jointly for a sample of firms. Most of the earlier studies have examined one kind of classification shifting at a time. This study adds to the existing literature on earnings management by documenting that some firm-specific factors pressurize firms to prefer one form of shifting over another to report inflated core earnings.

The study aims to investigate the psychological mechanism that motivates consumers to pay more for a preferred brand that reflects their actual or ideal self-concept, by examining the shift in attention between consumer’s present, future, and past moments.

First, in a survey setting, the study identifies the relationship between temporal focus and self-congruence. Subsequently, we conduct three experiments to capture the effects of temporal focus on brand preference and willingness to pay (WTP). In these experiments, we manipulate consumers’ self-congruence and temporal focus.

The findings show that consumers with a present focus (distant future and distant past foci) tend to evaluate a brand more preferably when the brand serves to reflect their actual (ideal) selves. However, in the absence of present focus consumers’ WTP is more for a brand that reflects their ideal selves.

The study does not have an actual measure on consumers’ WTP; instead we use single-item measure.

This study sheds new light on branding strategy. The results suggest that authentic and aspirational branding strategies are relevant to publicly consumed products. Brand managers could incorporate consumers’ temporal focus into branding strategy that could significantly influence consumer preference and WTP for their brands.

This study expands our understanding of brand usage imagery congruity by showing that temporal focus is an important determinant of self-congruence. In this regard, this study empirically investigates the relationship of temporal focus, self-congruence, brand preference, and WTP. It further reveals that mere brand preference does not necessarily lead consumers to pay more for symbolic brands.

This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

Amid the COVID-19 contamination, people are bound to use contactless FinTech payment services. Because of restrictions on physical movement and avoidance of touching physical money, people willingly choose mobile payment, resulting in enormous growth in FinTech payment service industries. Because of this, this study aims to examine the effect of factors affecting Gen X and Millennials users to use FinTech payment services.

The authors used 328 responses collected through convenience sampling of Indian users aged between 26 and 57 years in the Delhi-NCR region who are users of FinTech payment services.

The authors’ findings verified that in India, perceived COVID-19 risk, perceived severity for COVID, individual mobility, subjective norms, perceived ease of use and perceived usefulness have statistically significant impacts on FinTech payment services during the COVID-19 pandemic. Structural equation modelling was used to study the proposed research model. Overall, the model predicted 76.9 % of the variation in intention to use FinTech payment services by the abovesaid variables by Indian users during a pandemic.

This study will provide valuable insight to all FinTech service providers and stakeholders in planning and designing the concerned policy. It will be able to draw the attention of users more.

This research added a valuable theory to the existing technology adoption model (TAM) theory. It demonstrated the utility of the above variables in adopting and using FinTech payment services, which will help service providers to develop future strategies because of the COVID-19 pandemic.