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Without precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.

In their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.

Authors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.

This study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

This paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days.

The study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices.

All of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study.

The outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality.

This study aims to address gaps and limitations in the literature on corporate governance and stock liquidity. It explores the potential benefits of increasing female representation in corporate leadership, which has been a subject of debate and policy intervention in recent years.

Based on prior empirical studies and by integrating the insights of different theories, this study links gender diversity to stock liquidity and uses a multivariate panel regression approach.

The results show that gender diversity, both on the board and in executive positions, positively and consistently affects stock liquidity across different business cycles. The findings reinforce the notion that diverse executive leadership is crucial and influential irrespective of the prevailing economic conditions.

This study has practical implications for investors, managers and policymakers who are interested in the benefits of gender diversity in corporate leadership. It suggests that increasing the percentage of female executives and board members can improve stock market liquidity, which is a key indicator of market efficiency and firm value.

This study advocates for gender equality and diversity in corporate leadership, which can benefit society. It demonstrates that the presence of women directors can enhance financial stability and thus benefit the stakeholders and the community.

This study contributes to the academic literature by examining the impact of gender diversity on board and executive levels on stock liquidity in the US market. Previous research on this topic has mainly relied on French or Australian data. Moreover, this study extends previous work through examining the case of executives’ gender diversity. To the best of the authors’ knowledge, this study is the first to analyze the relationship between gender diversity and stock liquidity across different business cycles, providing a nuanced understanding of how economic contexts affect this relationship.

We investigate connections between the development of Fintech and the blue economy from September 14th, 2020, to August 11th, 2023.

In this research, we use a cutting-edge model-free connectedness approach to investigate the relationships between FinTech and blue bond volatility. Our work is the first to investigate the effects of unknown events, such as the COVID-19 pandemic and Ukraine–Russia conflicts, on the interconnection of volatility derived from FinTech development and blue bond volatility.

Our results highlight the two-way relationship between the development of Fintech and the blue economy during our sample period. The net total connectedness shows that the blue economy index is a net shock receiver, especially in late 2021 and the second half of 2022, while most of the fintech indexes in our sample are mainly net shock transmitters. The Ukraine–Russia tension threatens the development of a sustainable blue economy. The development of Fintech plays an important role in promoting the blue economy.

Our results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies.

Our paper contributes to the literature in at least four ways. First, as previously stated, our article is the first to investigate the relationship between FinTech and blue bond volatility. Second, this study presented a framework for studying volatility interconnections between distinct variables that is more suited to analyzing these interconnections. In this research, we use a cutting-edge model-free connectedness approach to investigate the relationships between FinTech and blue bond volatility. Third, our work is the first to investigate the effects of unknown events such as the COVID-19 pandemic and Ukraine–Russia conflicts on the interconnection of volatility deriving from FinTech development and blue bond volatility. Lastly, our research provides a daily dataset for the BNP Paribas Easy ECPI Global ESG Blue Economy UCITS ETF to analyze 50 businesses from various markets that are at the forefront of the responsible application of ocean resources and other ESG standards. The Global X FinTech ETF (FINX) and the ARK FinTech Innovation ETF (ARKF) seek exposure to companies developing financial technology innovations. The development sectors include insurance, investment, fundraising and third-party lending by utilizing cutting-edge mobile and digital technologies. Our time series runs from September 14th, 2020, to August 11th, 2023. By using this database, we provide a comprehensive analysis of the link between the volatilities arising from various markets.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

Capital structure is vital to every company because it has a huge impact on the company’s financial decisions. The ultimate goal of the company is to effectively mix the debt-to-equity ratio (DER) to maximize the shareholder value. When the Covid-19 pandemic was officially announced in early March 2020, widespread negative effects started to affect almost all industries in Indonesia. The hotel, restaurant, and tourism industry is considered to be one of the most severely affected industry categories. It is important to pay attention to the role of this industry in Indonesia’s overall economy as it contributes to Indonesia’s gross domestic product at 6.1% in 2019. The objective of this study was to address the effects on the formation of capital structure of firm-specific characteristics among a sample of 26 active hotels, restaurants, and tourism companies listed on the Indonesia Stock Exchange. The authors used the data from the second and third quarters of 2019 to represent the period before the pandemic. Meanwhile, the period during the pandemic is represented by the data from the second and third quarters of 2020. Using the random-effects model to test the hypotheses, the authors found that asset tangibility, tax shield, and earnings volatility had significant positive correlations with book leverage. Furthermore, tax shield and earnings volatility had significantly positive relationships with DER. The authors also detected that size and earnings volatility had significant negative correlations with net equity. However, the authors found no significant relationship between capital structure and the pandemic dummy. It was inferred from the results that the pandemic had no effect on capital structure within the research period.

Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.

In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.

We show that our extended model yields a Pareto efficient outcome.

The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.

Long-term modelling and sustainability can be modelled in our setting.

Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

Introduction: The effect of environmental regulations or green policies on the financial health of businesses is still up for debate. The Prime Minister of India presented a bold plan to achieve net-zero emissions by 2070 at the COP26 climate summit in Glasgow (UK). Following this announcement, numerous Indian companies voluntarily committed to becoming carbon neutral to support the ambitious emission reduction targets. A growing body of research examines the link between environmental standards compliance and businesses’ sustainability measures, and how they affect their overall performance (profitability, stock returns, or output generation).

Purpose: The research assesses the effect of these voluntary announcements on the stock performance of Indian companies in the context of voluntary commitments to reduce carbon emissions.

Methodology: Concentrating on the announcement impact of carbon neutrality commitments/carbon emissions reductions of 52 Indian companies, the study considers carbon neutrality pledges/carbon emissions reduction from 2018 to 2022. The sample companies list was taken from various indices on the National Stock Exchange. A standard event study methodology is applied to compute abnormal returns during the event window of (−10, 10).

Findings: The results show that companies announcing the carbon neutrality pledges/carbon emissions reduction received significantly negative abnormal returns of 0.49% on announcement day. The cumulative average abnormal returns for different windows are also negative. It signifies that investors don’t value the environmentally sustainable actions of firms. It may also be because of investors’ ignorance of carbon neutrality pledges and their importance, highlighting the need to educate investors about the significance of corporate sustainability initiatives.