Elena Fedorova, Pavel Chertsov and Anna Kuzmina
The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government…
Abstract
Purpose
The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.
Design/methodology/approach
The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.
Findings
The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.
Originality/value
At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.
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During the COVID-19 pandemic, transitory unemployment insurance (UI) policies substantially increased unemployment benefits (UBs) and the number of eligible groups in Russia. The…
Abstract
Purpose
During the COVID-19 pandemic, transitory unemployment insurance (UI) policies substantially increased unemployment benefits (UBs) and the number of eligible groups in Russia. The procedure for registering as unemployed was moved to an online platform. The present paper aims to distinguish the effect of anti-COVID-19 restrictions on unemployment from that of the transitory unemployment insurance policies.
Design/methodology/approach
Using 553,627 approved claims for unemployment benefits from the Russian Public Employment Service (PES) administrative records (June 2019–December 2020), monthly data on the number of individuals registered as unemployed are aggregated in a pseudo panel. A double-difference approach is employed to identify the effects of the social interaction intensity and teleworkability (TW) of the latest occupation on unemployment. The first is associated with a direct effect of anti-COVID-19 restrictions and the latter with the simplified application procedure.
Findings
The face-to-face (F2F) intensity of the latest occupation did not lead to any increase in the number of unemployed persons as could be expected in response to anti-COVID-19 restrictions. Job TW had two opposite effects on unemployment: it decreased individuals' risk of job loss and increased their likelihood of claiming unemployment benefits. Surprisingly, however, in line with the typical response of the Russian labour market to downturns, the latter dominated. The largest response was found among men and individuals with primary education.
Originality/value
This study is the first to attempt to distinguish the effect of anti-COVID-19 restrictions from that of the transitory UI policies on unemployment in Russia.
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Parminder Varma, Shivinder Nijjer, Kiran Sood and Simon Grima
Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and…
Abstract
Purpose
Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and opportunities for incumbent banks, and it can be valuable to investigate its effects on banking performance. Therefore, the aim of this study is to assess whether investment in FinTech is associated with better performance of Indian banks during 2012–2018.
Methodology
To do this, a sample of Indian banks was investigated between 2012 and 2018 using k-means and hierarchical cluster analysis, ANOVA, and pairwise comparison tests.
Findings
Results of the analysis strongly suggest that investment in FinTech is associated with better banking performance. Higher FinTech investments, represented by mobile transaction volume, are associated with higher efficiency scores and accounting-based performance. In particular, banks that invest in FinTech and have relatively low non-performing loans have a 7.7% higher Return on Employment (ROE) than banks with exceptionally low FinTech use and no significant investment in smart branches.
Practical Implications
Therefore, it can be recommended that Indian banks adopt a forward-looking strategic approach when making investment decisions regarding new technologies. Failing to adapt to the FinTech disruption may result in poor value creation prospects in the long run.
Originality
To the best of the authors' knowledge, this is the first study that analyses. We are not aware of any similar study on whether investment in FinTech is associated with better performance of the Indian banks during 2012–2018.
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Aleksei Gorgadze, Anastasia Sinitsyna, Julia Trabskaya and To'neill Bala
The main purpose of this study is to examine the relationship between ranges of affective components that have an impact on the revisit intention of museum visitors, in the…
Abstract
Purpose
The main purpose of this study is to examine the relationship between ranges of affective components that have an impact on the revisit intention of museum visitors, in the context of a major city event. The study reveals the most significant factors that affect decision-making by applying the findings to a structural equation modelling (SEM) and conditional inference tree (CTree).
Design/methodology/approach
The paper utilises face-to-face survey research at the “Long Night of Museums” event in Saint Petersburg, 298 questionnaires were completed on the night of the event. The empirical part of the research is based on the SEM and interpreted by using the CTree. The SEM model measures the direct and indirect influence of the cognitive and affective components; the CTree enables the testing of both component and the joint effect they both produce.
Findings
This study shows a strong indirect correlation between the cognitive component of the major city event and the revisit intention of museum visitors. When focussing on affective components, both the SEM and the CTree demonstrated that attractiveness and atmosphere are revealed to be the most impactful elements regarding visitor retention and repeat custom. The research allows for a deeper understanding of visitor behaviours, intentions and their decision-making processes.
Practical implications
The results of the study allow museum managers to understand how to create repeat custom amongst visitors, by appreciating the importance of participation in major city events and the role that attraction and atmosphere play when creating intention for repeat visit. The research has uncovered which dimensions are the most important to visitors, and as a result, these particular dimensions should be thoroughly developed by museums in future in order to attract and repeat visits. This study has demonstrated the practical implications for museums participating in city events. When considering policy makers, this particular research provides an opportunity to develop recommendations for future city events, as well as using the CTree to assess and predict the effectiveness of visitor behaviour.
Originality/value
This is an original study which aims to integrate the impact of the perceived value of the cognitive component and a new range of affective elements regarding museum retention in the context of a major city event. The study includes newly developed dimensions of perceived value, as well as a unique focus on affective dimensions such as – atmosphere and attraction. Another point of originality is provided by using a CTree, which captures an in depth understanding of the intention formation process. This study provides an opportunity to advance our understanding of visitor decision-making processes.
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Itisha Jain and Rachita Gulati
This study analyzes the disparities and growth of higher education by constructing a composite index of higher education development (HEDI) across 31 Indian states and union…
Abstract
Purpose
This study analyzes the disparities and growth of higher education by constructing a composite index of higher education development (HEDI) across 31 Indian states and union territories (UTs) for the period from 2012 to 2021.
Design/methodology/approach
We develop an all-encompassing multidimensional index of higher education development (HEDI) by using a constrained “benefit-of-the-doubt” (BoD) model based on data envelopment analysis (DEA). The states and UTs are then ranked according to their respective HEDI scores.
Findings
The empirical findings reveal significant disparities in higher education development across states and UTs in India. States like Tamil Nadu, Chandigarh, and Puducherry exhibit higher performance. In contrast, Bihar, Odisha, and Tripura relatively show underdevelopment and need greater focus. The dimensions of outcome and infrastructure and financial resources are the most neglected and require greater attention in higher education.
Originality/value
The study is perhaps a pioneer in proposing a composite index to map the development of higher education across the Indian states and UTs using an innovative approach of DEA-based BoD methodology. The index provides educationists and policymakers with the current state of the spatial development of higher education, enabling the government to make informed decisions.