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The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

The “metaverse” is the new buzzword. With the phenomenal growth of the metaverse comes accounting, taxation and jurisdictional challenges, which business and governments have yet to fully address. This paper aims to highlight and rationalise the lack of regulatory framework and multiplicity of jurisdictions on metaverse transactions. This paper addresses some of the complications with respect to accounting and taxation in virtual environments.

This study relies on secondary data and emerging literature to understand the multiplicity of jurisdiction and complexity of the accounting transactions. The concept of the metaverse is rapidly evolving, and this study uses extant literature to provide the foundation for understanding the key challenges relating to accounting and taxation.

Concepts of revenue recognition and deferment are challenged by the transactions in the metaverse. There are novel applications, underpinned by emerging technologies and blockchain supporting new crypto assets, such as non-fungible tokens and other decentralised finance (DeFi) tools; however, the caveats of anonymity and jurisdictional issues persist. The paper suggests that the industry must adapt to the unique reporting requirements of these assets and develop new standards for evaluating their value for financial reporting purposes. The paper emphasises the need for a case-based approach in the absence of standardised regulations for the accounting industry in the metaverse.

This paper adds original contributions to extant literature of the metaverse and advances ongoing debates into the accounting and taxation issues pertinent to the metaverse and DeFi.

In the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

Using data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

The authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.

Literature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process.

The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk.

Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines.

It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices.

Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services.

Social implications may include improvement in residential design.

An innovative approach combines a thorough understanding of both design and economic principles.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

The case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.

This case is used in Darden's required first-year course, “Strategic Thinking and Action.”

In 2015, Steve and Heidi Crandall, the founders of Devils Backbone Brewing, LLC (DBB), were looking back on eight years of unanticipated success and significant growth. DBB had created a destination, a brand, and beer that drew people from all over, and it was the largest craft brewery in its region. The entire community, not just loyal beer drinkers, had supported DBB. In addition to funding and zoning accommodations, so many local residents had built their own economic lives around what had been their “little brewery that could.”

But the success had brought challenges, specifically in terms of growth. DBB was consistently not meeting demand in its existing markets and was receiving complaints about out-of-stocks. The Crandalls and their team had to figure out how to grow with, or preferably ahead of, demand for DBB's product. Should DBB build further capacity despite an already exhausted line of credit? Should it employ a contract brewer despite the local authenticity concerns such a move might stir up? Or should it just keep trying to manage business within its existing footprint, comfortably serving its loyal customer base?