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The purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.

Using the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.

First discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.

The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.

These findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.

This paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

Introduction: Data of companies in the Informatics index between 2008 and 2017 fiscal years were analyzed. In the analysis, the following ratios were used as traditional performance evaluation criteria: Return on assets, return on equity, earnings per share, price to earnings ratio, market to book value ratio, and return on sales rate. In addition, economic value added (EVA) was also used. The companies in the Informatics index are divided into sub-sectors according to by field of activity. These sub-sectors are software, communication, and hardware marketing.

Aim: To analyze the power of traditional performance evaluation methods to explain the market value added (MVA).

Method: Regression analysis and autocorrelation tests were used as research methods. These were done with IBM SPSS Statistics 25.

Findings: Models and rates that explain the MVA with the most meaningful results were calculated. These models and ratios are as follows: For the software sub-sector, a market to book value ratio was 77.8%; for the hardware marketing sub-sector, earnings per share was 65.4%; for the communication sub-sector, a market to book value ratio was 92.5%. Market to book value was the independent variable that describes the best MVA value of the two sub-sectors.

Originality of the Study: In this study, the average of the values of all companies in the BIST Informatics Index is not taken. By dividing into sub-sectors, value deterioration from the sector difference is prevented. It is stronger in terms of statistical science.

Implications: Researchers can look for and model the relationship between traditional evaluation criteria and EVA or other value-based evaluation criteria in the same index or other indices.

This study aims to explore the connectedness of price return index spillovers across eight economic sectors in the Malaysian stock market (Bursa Malaysia).

The analysis uses daily data of sectoral price index from 10 May 2005 to 24 February 2021. The study uses Bayesian time-varying parameter vector autoregressive.

The degree of price return index spillovers varies over time, reaching unprecedented heights during the COVID-19 pandemic in 2020. The industrial economic sector is the main transmitter of price index return shock, whereas the utilities economic sector is the dominant receiver of index return spillovers.

The findings are critical for investors, market participants, businesses and policymakers in developing action plans for the vulnerable sectors. It further enhances investors’ confidence in making investment decisions.

This paper attempts to examine the impact of merger on the stock returns and economic value added (EVA) of acquiring firms to know if the mergers are successful corporate restructuring strategies for the firms.

In total, 108 Indian firms are studied using paired sample t-test and Wilcoxon signed rank test for comparing the EVA of acquiring firms in short, medium and long term after merger. The effect of merger announcements on stock returns is analyzed by way of event study. An event window of −20 to +20 is taken and an estimation window of 256 (-276 -20) days is used in the study.

The authors find that mergers lead to significant improvement in the EVA of acquiring firms. However, the increase in financial performance and EVA is witnessed only in long term. The authors did not find any significant impact of merger announcement on the stock returns of acquiring firms.

The study is a first of study's kind, which evaluates both short-term (using event study methodology) and long-term (using EVA) impact of value addition to an acquirer after Merger & Acquisition (M&A). The study contributes to existing literature on the signaling theory of announcement of M&As and synergy gain theory of completed M&As by providing evidence from the context of an emerging market like India.

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

The purpose of this paper is to examine the impact of financial reporting quality (FRQ) on dividend policy. This paper also examines the moderating role of corporate liquidity on the FRQ–dividend policy relationship.

The sample of this paper contains 113 non-financial companies listed on the Saudi Stock Exchange from 2003 to 2019 (1,675 firm-year observations). The authors use OLS regressions to test the hypotheses.

The authors find a positive relationship between FRQ and dividend policy. They also find that the positive effect of FRQ on dividend policy is not strengthened by the presence of corporate liquidity.

The findings of this study offer implications for stakeholders, including investors and others in Saudi Arabia and other developing countries with comparable business environments. This is because of the significant impact of the dividend policy on a company’s value, as it is a crucial decision that involves distributing substantial amounts of money to shareholders on a regular basis and interacts with other critical decisions within the company. Therefore, the dividend policy has a crucial role in determining the company’s value, which is reflected in its stock prices.

To the best of the authors’ knowledge, this is the first study in Saudi Arabia that provides new empirical evidence on the impact of FRQ on dividend policy and the moderating role of corporate liquidity on this relationship.

This research has been conducted to determine the core reasons for gender inequality and to evaluate the effectiveness of higher education as a sole solution to ensure gender equality.

This qualitative case study research explores the causes behind gender inequality, and interviews were conducted with 20 male and female graduates and five stakeholders. Liberal feminism theory has been employed to understand the phenomena.

The findings show that receiving a university degree cannot ensure gender equality and empowerment of women in Bangladesh. The key factors contributing to gender disparity are societal and a general deficiency of technical knowledge.

More policies should support women in every aspect of life, and the existing policies should be carefully followed. The government must ensure high-quality education, and people must show more significant concern by acquiring knowledge from quality education seeking gender equality.

This research fills the gap in the current literature by providing knowledge about the practical effectiveness of quality higher education in solving the problem of gender inequality, its core reasons and possible solutions in Bangladesh's context.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

At the age of 92, Mahathir Mohamad served his country as the Prime Minister for the second time. But unlike his first stint as the head of the government, which lasted 22 years, this time, his tenure was less than two years. His resignation, which was linked to some internal issues within the Pakatan Harapan (PH) allied parties, came at a time when many countries in the world were in the early stages of the coronavirus outbreak. Mahathir was aware of the power struggle that was adversely affecting the government's response to combat the virus transmission and reduce the rising number of COVID-19 infection cases. With his long experience in governance and as a former medical officer, Mahathir offered some advice and recommendations to the existing government on ways to address the public health crisis. Since the coronavirus outbreak, the domestic political and economic developments also drew his direct criticism and critical commentaries.