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This study enhances the financial modelling of companies undergoing an Initial Public Offering (IPO) by focusing on internal capability determinants and IPO proceeds.

A hybrid logistic regression and shallow-depth decision tree approach are employed to predict the initial three-year post-IPO performance of companies listed on the Stock Exchange of Thailand (SET) using data from 2002 to 2021.

The results demonstrate that these models not only perform competitively against complex machine learning algorithms but also surpass them in terms of interpretability, an essential feature in financial modelling. The proposed approach effectively captures the effects of each determinant, offering valuable insights into strategic resource allocation and investment decision-making during transition years.

This study introduces a novel application that integrates logistic regression with decision trees to predict multiclass financial performance, filling the gap between complex machine learning techniques and interpretable financial models. It offers practical tools for companies and investors to make informed decisions in challenging post-IPO environments.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).

The authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.

According to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

The current version of the Trans-Pacific Partnership (TPP), known as Comprehensive and Progressive Agreement for TPP (CPTPP) has opened itself to new membership. China formally applied to join the CPTPP Free Trade Agreement. With respect to China joining the CPTPP, some groups of researchers have argued that China’s accession would be a win-win situation for both China and CPTPP member countries. On the contrary, a group of researchers has argued that there would not be any significant gain to member countries of CPTPP. Analysis in this paper is restricted to examining the economic benefits to China by joining the CPTPP. The paper aims to make conjectures about what are the challenges China must face nationally and internationally to join CPTPP.

The methodology and the approach to do the empirical analysis concern using the stochastic frontier gravity model and the panel data covering the period 1995–2022 from the World Integrated Trade Solution developed by the World Bank.

The empirical results reveal that the CPTPP membership will facilitate China to improve its export efficiency in merchandise exports with the CPTPP member countries.

Though there are a few discussions on the accession of China to CPTPP, quantitative analysis of examining the impact of China joining the CPTPP on its export efficiency with the members of CPTPP has not been explicitly discussed in the literature. This study’s contribution is to fill this gap in the literature.

The growing concern for food safety and quality, especially after the COVID-19 pandemic and the new normal situation, motivates governments and private sectors to improve consumers’ confidence in food systems by adopting certifications and traceability systems. The recent emergence of diverse food labeling schemes in food systems in emerging countries has sparked questions about consumers’ valuation of such labels. Nonetheless, little is known about how familiarity with, trust in and knowledge of these food labels affect consumers’ utility. This study aims to reveal consumers’ preferences for agrifood assurance to accelerate food safety practices. Specifically, we examine in what ways agrifood attributes (traceability, certification, selling places and price) impact consumers’ selection.

Data were gathered from surveying 1,365 consumers and then discrete choice experiment methodology was applied to measure consumers’ willingness to pay for safety attributes displayed on food labels in different market outlets.

Empirical evidence shows that certification is the most preferred safety attribute, with the highest level of WTP hovering around 50% more for both USDA and VietGap certificates. The second rank belongs to the traceability system, where consumers express particular interest in farming and processing information rather than more complicated information. Meanwhile, the food purchasing venue has less effect on consumers’ WTP for a certain food label. Consumers’ demographic factors, familiarity, knowledge and trust also play an important role in explaining their heterogeneity.

The findings may not be generalizable because the current study only included data from Vietnamese consumers.

Our findings provide managerial implications for food policymakers and providers in governing the food market to restore consumer confidence.

This study aims to identify factors that affect the intention and actual adoption of cashless payment (ACP) among Vietnamese youths. Extending the unified theory of acceptance and use of technology (UTAUT) model with two impelling factors (perceived trust [PTR] and lifestyle compatibility [LCM]), this study also examined the mediating effect of intention to adopt cashless payment (ICP) on the relationships of UTAUT model components with the actual ACP.

All data were collected online from 422 Vietnamese youths through online survey, and partial least squares structural equation modelling was performed to analyse the data.

The study’s results illustrated the positive and significant effects of performance expectancy, effort expectancy, facilitating conditions, LCM and PTR on ICP. However, social influence was found to exhibit a negative effect on ICP. Furthermore, ICP was found to contribute no mediation effects on the relationships of any of the components with the actual ACP.

This study’s findings are widely useful for marketers and managers to plot their promotional and campaigning strategies, emphasising factors that motivate consumers to adopt cashless payment. The obtained findings also benefit architects and designers in designing products and services by consolidating lifestyle standards and other requirements of consumers. Policymakers should implement policies and strategies to enforce rules and educate the public to widely adopt cashless payment across various sectors.

This study extended the UTAUT model with two new variables, i.e. PTR and LCM.

With the rise of stakeholders' activism, integrating sustainability into business practices has become increasingly crucial for businesses. For such stakeholders, recognition from global sustainability indexes such as Dow Jones Sustainability Indexes (DJSI) allows them to evaluate organizations based on sustainable integration. Further, it helps companies influence their investors' opinions and investment decisions. In this light, this study examines the stock market response to the inclusion and exclusion of Asian companies in the Dow Jones Sustainability World Index (DJSI World) and the Dow Jones Sustainability Index Emerging Markets (DJSI EM).

The study has employed the event study methodology to understand the intermediate and immediate abnormal reaction of the inclusion and exclusion event from the DJSI World and DJSI EM indexes from September 30, 2012, to November 30, 2023.

Findings show that changes in the DJSI indexes have asymmetric reactions in different markets. The reaction to changes in DJSI indexes is more pronounced for DJSI EM than DJSI World. Inclusion in DJSI World showed mixed reactions, while exclusion typically resulted in adverse reactions around the event day. Inclusion in DJSI EM generated a positive reaction, while exclusion had a mixed reaction. The study found no significant difference in the intermediate reaction of inclusion/exclusion between DJSI World and DJSI EM. Additionally, investors' immediate reactions from developing versus developed countries differed, but the reaction variation disappeared in the whole event window.

With a considerable increase in sustainable practices in Asia, companies must determine whether investors recognize and reward the company’s sustainability efforts. No other study has previously investigated the impact on Asian companies that are rapidly rising in global sustainability rankings. Furthermore, no study has examined the comparative reaction of inclusion or exclusion in two independent sustainability indices – DJSI WORLD and DJSI EM.

This paper aims to conduct a systematic literature review of Socially Conscious Investment (SCI) articles published in premier journals. Its objective is to shed light on the publication trend, leading authors, journals, countries and themes in contemporary SCI research. The article also provides a conceptual model of SCI to enhance understanding of the knowledge structure and the future research direction.

A systematic review followed the PRISMA guidelines and encompasses 264 full-text articles indexed in A* and A category journals listed in ABDC is reviewed. The literature synthesis adopts the theories, contexts, characteristics and methodology (TCCM) framework.

The article has identified the research trends related to author impact, journal impact, article impact and the outcomes derived from the TCCM framework. Additionally, it highlights three key themes: Performance of SCI, Behavioural issues and SCI development literature.

The insight on various aspects of SCI was explored for a comprehensive understanding. The authors also developed a conceptual model for socially conscious investment.

Monetary policy has major impacts on macroeconomic indicators of the country. Accordingly, uncertainty regarding monetary policy shifts can cause challenges and risks for businesses, financial markets and investors. Thus, the purpose of this study is to investigate how real estate market volatility responds to monetary policy uncertainty.

The GARCH-MIDAS model is applied in this study to investigate the nexus between monetary policy uncertainty and real estate market volatility. This model was fundamentally instituted to accommodate low-frequency variables.

The results of this study reveal that increased monetary policy uncertainty highly affects the volatility in real estate market during the peak period of COVID-19 as compared to full sample period and COVID-19 recovery period; hence, a significant decline is evident in real estate market volatility during crisis.

This study is particularly focused on peak and recovery period of COVID-19 considering the geographical region of Greece, Japan and the USA. This study provides a complete perspective on the nexus between monetary policy uncertainty and real estate markets volatility in three distinct economic views.

The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.

We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.

The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.

This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.