Issue #1 (Volume 22 2025)

This research paper aims to explore the market efficiency of stock exchanges in of ASEAN-5 countries, Indonesia, Malaysia, Singapore, Thailand, and the Philippines, during the COVID-19 pandemic. Stock market efficiency is the degree to which stock prices reflect all available relevant information. In an efficient market, stock prices will immediately rise or fall to reflect new information released by a company. This study uses the Stochastic Frontier Analysis (SFA) method to determine the efficient value over time. Market efficiency generally refers to how well financial markets in these selected countries reflect all available information, particularly in the context of the COVID-19 pandemic. SFA is useful here as it can separate random errors from inefficiencies, allowing us to isolate the impact of COVID-19 on market efficiency levels across these countries. The results show that the stock markets of ASEAN-5 countries (Indonesia, Malaysia, Thailand, Singapore, and Philippines) are efficient during the COVID-19 pandemic. Based on the hypothesis test, for the overall period of 2021 and 2023, the average efficiency ranges from 0.68 to 0.72, and for the time period/per year the average efficiency ranges from 0.66 to 0.74. The efficiency of the Philippine stock market based on time period/per year shows the average maximum efficiency in 2021 (0.74) and 2023 (0.73). While the average efficiency of the Malaysian stock market shows the minimum level of efficiency in 2020 (0.66) and 2021 (0.68).

Acknowledgment
The authors would like to thank the Research and Innovation Institute (LRI), Universitas Muhammadiyah Surakarta, for the enormous financial support in writing this study.

This study aims to investigate the mediating effect of digital financial inclusion on the relationship between digital financial literacy and the financial well-being of Malaysian households, focusing on gender differences. Using quantitative research, a total of 210 responses, which contained 105 samples for each gender, were collected from households across Malaysia using a self-administered questionnaire. The research model was analyzed using Partial Least Square-Structural Equation Modelling techniques. The findings revealed significant relationships between digital financial literacy and digital financial inclusion, as well as between digital financial inclusion and financial well-being. Additionally, digital financial inclusion was found to significantly mediate the relationship between digital financial literacy and financial well-being, underscoring the importance of digital financial inclusion. The MICOM analysis results show that all constructs have good configural invariance, indicating the measures are consistent across groups. High correlations between males and females suggest similarities, but permutation tests indicate these similarities might be due to chance. Variance differences for digital financial literacy and digital financial inclusion are not significant. However, financial well-being shows a significant variance difference, suggesting less variability among males, supported by higher reliability scores for the financial well-being of males, indicating more consistent responses. Notably, the standardized beta for the digital financial inclusion – financial well-being path is higher among females, indicating a stronger influence of digital financial inclusion on financial well-being for this group. However, the direct relationship between digital financial literacy and financial well-being is insignificant for both genders.

Acknowledgment
This research was supported by the Ministry of Higher Education (MoHE) of Malaysia through the Fundamental Research Grant Scheme (FRGS/1/2022/SS01/UUM/02/10).

Rapid technological advances have made financial markets more accessible and encouraged individual investors to engage in investment decision-making actively. Generation Z, or Gen Z, characterized by higher levels of digital literacy, a high sense of curiosity, and acceptance of innovation, tends to make investment decisions quickly. This study aimed to analyze the effect of technological progress, financial literacy, and financial attitudes on investors’ investment decisions. There are 125 Gen Z investors in Jakarta, Indonesia, selected as research samples using the non-probability sampling method. The survey method was employed to collect data, and the study instrument was a questionnaire. For data analysis, Partial Least Squares version 4.0 was used. The study’s findings revealed that financial literacy and financial attitude positively influence Gen Z investment decisions. Technological progress does not affect Gen Z in determining investment in the financial market. Financial literacy and financial attitude are more dominant for Gen Z investors than technological progress in determining investment allocation. This finding implies that Gen Z must improve their understanding of correct financial literacy and financial attitudes that align with individual investors’ character. Further investigation needs to reveal the insignificance of technological progress in determining investment decisions. Technological progress and financial literacy likely have the same factor characteristics related to three dimensions: knowledge, skills, and attitudes. The attitude of Gen Z investors towards the progress of financial technology by investors is preceded by good financial literacy. Therefore, it is necessary to test the relationship between variables, both mediation and moderation, in investment decisions.

This paper investigates the multifractal behavior of the six largest African stock markets, including the Johannesburg, Casablanca, Botswana, Nigerian, Egyptian, and Regional Stock Exchanges. Despite the growing significance of these markets in the global economy, there is limited understanding of their underlying dynamics, particularly regarding their multifractal properties. This lack of knowledge raises concerns about the informational efficiency of these markets, as traditional models may not adequately capture the complexities of price movements. To achieve the goals of the study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method is applied to capture the multifractal dynamics, and shuffling and phase randomization techniques are performed to identify the sources of the multifractality of the six African stock markets. The empirical results, derived from the generalized Hurst exponents, Rényi exponents, and Singularity spectrum, show that all six stock markets display multifractal behavior, characterized by irregular and complex price movements that vary across different scales and timeframes. Additionally, the study finds that both long-term correlations and heavy-tailed distributions contribute to the observed multifractality. Long-term correlations lead to persistent price trends, challenging the Efficient Market Hypothesis (EMH), while heavy tails increase market unpredictability by raising the likelihood of extreme events like crashes or booms. The findings have significant practical implications for stakeholders in African stock markets, enabling investors and portfolio managers to enhance risk assessment and develop effective trading strategies while helping market regulators improve efficiency and stability through appropriate policies. Financial institutions can also refine risk management frameworks to better account for extreme events.

The study provides empirical evidence on the cost implications of socially responsible investing (SRI) in relation to Environmental, Social, and Governance (ESG) preferences. Specifically, it examines whether socially responsible investors incur higher costs to meet non-pecuniary goals and how government involvement can offer rewards to socially responsible investors in supporting the realization of the United Nations’ Sustainable Development Goals (SDGs). Using panel data regression, this study analyzes ESG scores and financial and return data of 1,450 firm-year observations in ASEAN-5 countries over the period 2015–2022. The findings reveal that firms implementing ESG practices experience an increase in their cost of capital (CoC), supporting the notion that ESG investment requires a sacrificial cost. Even firms with low operational risks face rising CoC when implementing ESG principles. However, the study also finds that firms located in countries with better government effectiveness and stronger control of corruption benefit from a reduction in CoC, despite ESG implementation. Conversely, country risks, particularly those related to environmental pollution, exacerbate the CoC for firms adhering to ESG criteria. Overall, the results suggest that while country-level governance can reward socially responsible investors by mitigating CoC, country risks such as pollution pose additional burdens, highlighting the need for government intervention to incentivize SRI and align it with global sustainability goals.

Acknowledgment
This research was funded by the Indonesian Ministry of Education, Research, and Technology (DRTPM), Fundamental Research Grant in 2024 [0609.10/LL5-INT/AL.04/2024,359/D.01/LPPM/2024].

Understanding the conditions leading to business failure and predicting them earlier is the best way for companies to overcome and minimize their harm, improve their performance, and avoid financial distress and bankruptcy. This paper aims to measure the level and trends of factors affecting financial distress in Vietnam – an emerging Southeast Asian economy, along with the managerial implications drawn from the research results. Research data were collected from 606 firms listed on the Vietnam Stock Exchange from 2018 to 2022. The Altman Z-score is used to determine the financial distress of these firms. The factors researched and tested in this study are all internal factors divided into two groups with distinct features. Non-financial factors belong to management characteristics; financial factors are typical indicators of a firm’s financial statements. The study uses OLS, FEM, and REM models to analyze the influence of financial factors (Total liability to Total assets, Sales growth, Firm size, and Firm age) and non-financial factors (Board size, CEO duality, Institutional ownership level, Independent member, and Foreign CEOs) on financial distress and GLS regression to overcome the model’s shortcomings. The results show that the factors in the research model significantly impact financial distress, of which six factors (Board size, CEO duality, Institutional ownership level, Foreign CEOs, Sales growth, and Firm age) are negatively correlated. Three other factors (Independent members, Total liability to Total assets, and Firm size) are positively correlated with financial distress.

Acknowledgment
The author thanks everyone who helped make this study possible.

This study examines the extent of environmental and community disclosures and evaluates how audit committee features influence such disclosures among listed firms in Bahrain and Kuwait, Gulf Cooperation Council (GCC) countries of emerging markets. The research employs an unweighted disclosure index comprising 18 items related to environmental and community disclosures, analyzing 432 firm-year observations across Bahrain and Kuwait covering a nine-year period (2015–2023). Three audit committee features (independence, number of meetings, and size) along with the number of other board committees are examined in this empirical investigation. Descriptive analysis indicates that the sampled firms offer 44.25% and 60.60% of environmental and community information, respectively, signaling a satisfactory disclosure level in Bahrain and Kuwait. This demonstrates progress compared to prior studies in GCC countries. Hierarchical Multiple Regression models demonstrate that all four models significantly describe the dependent variables. Regression model four exhibits the highest explanatory power in explaining community information. Audit committee independence and size emerge as determinants of community information, while only audit committee independence is associated with environmental information. The results of this study bear significant implications for governmental bodies and regulatory authorities aiming to strengthen disclosure regulations and promote corporate governance frameworks within GCC nations.

The study aims to analyze the co-movement between oil prices, BRICS nations’ exchange rates, and stock markets. Grasping these interrelationships is essential for understanding how global energy price shifts broadly affect the economies, particularly those of developing nations.
The study employs wavelet coherency analysis on daily data, examining the association between crude oil (Brent crude), exchange rates (Brazilian Real, Russian Rubble, Indian Rupee, Chinese Yuan, and South African Rand), and stock markets (BOVESPA of Brazil, Moscow Exchange of Russia, Nifty50 of India, Shanghai Composite of China, and JSE FTSE of South Africa) across both temporal and frequency domains.
This study reveals strong comovements, especially during periods of global economic instability, such as the impact of the COVID-19 pandemic and the Russia-Ukraine war. During such periods, oil prices and stock market indices tend to move in tandem, while oil prices and exchange rates show an inverse relationship. The study also reveals a decoupling of crude oil from both share markets and exchange rates during normal economic conditions. This decoupling suggests that outside of a chaotic period, the relationships weaken. However, the co-movements among the variables for China are notably weaker, even during economic upheavals, than in other BRICS nations. Understanding these relationships can aid in informed decision making and strategies in the face of global economic turmoil.

Acknowledgment
This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2025/R/1446).

The global adoption of Central Bank Digital Currencies (CBDCs) represents a pivotal shift in monetary systems, driven by technological advancements and economic imperatives. While a small number of official digital currencies are in circulation, many nations are launching pilot programs to address financial inclusion challenges and enhance economic resilience. This study aims to identify the determinants of digital currency adoption across 116 countries, using logistic regression to analyze the effects of economic, technological, institutional, and financial factors.
The results show that higher GDP levels significantly increase the likelihood of active CBDC adoption by 332.1 percent and pilot adoption by 212.6 percent, reflecting the role of economic development. Greater internet usage improves the odds of active adoption by 12.7 percent and pilot adoption by 13.4 percent, while financial inclusion indicators, such as account ownership, increase the likelihood of adoption by 59 percent for active initiatives and 141 percent for pilot projects. Monetary freedom positively influences active adoption by 31.1 percent, and higher interest rates increase the odds by 20.8 percent. Conversely, business freedom negatively affects active adoption by 27.5 percent and pilot adoption by 29.1 percent, suggesting that countries with strong private-sector digital payment solutions may rely less on CBDCs.
These findings represent the transformative potential of digital currencies to improve financial inclusion and economic participation. Policymakers should prioritize investments in digital infrastructure and financial inclusion initiatives to facilitate the integration of digital currencies into national economies and empower underserved populations globally.

Investor behavioral factors determine the investment decisions of individual investors in the stock market. The study investigated behavioral factors driving investment decisions in Nepal’s stock market, contributing to existing literature. The behavioral factors comprise heuristics, prospects, and herding as predictors and investment decisions as a response variable. Thus, the study adopted a descriptive and analytical research design to test the research hypotheses and resolve the research questions and issues. A survey was conducted among individual investors registered with Nepal’s trading management system (TMS). A total of 526 structured questionnaires were distributed to targeted respondents, and only 350 useful questionnaires (66.54 percent) were received. The survey data of cross-sectional type were encompassed with a random clustering sampling method for this study. Further, the study employed descriptive statistics to depict the characteristics of respondents’ profiles, correlation analysis to assess the association between predictors and response variables, and linear regression analysis to investigate the impact of predictors on response variables. Similarly, Cronbach’s alpha was tested to observe reliability in the study. The survey findings showed a positive and significant association between heuristics and investment decisions (β = 0.088, p < 0.05). The prospect is positively linked with the individual’s investment decision but found insignificant (β = 0.011, p > 0.05). Finally, herding found a positive and significant association with investment decisions (β = 0.235, p < 0.05). The findings of this study contribute to existing theory and can be a benchmark for decision-makers and policymakers, investors, and others.

Transfer pricing practices remain a challenge for tax authorities in various countries because they can be used to reduce tax payments. This study aims to explore the impact of ownership structure on transfer pricing practices, focusing on how board experience and executive characteristics act as moderating factors. Additionally, the study considers three control variables: company size, debt to equity ratio, and ROE. The analysis encompasses all publicly listed companies on the Indonesia Stock Exchange, utilizing panel data analysis and moderated regression techniques. The dataset comprises 2,480 entries from 310 companies over an eight-year span from 2015 to 2022. The findings indicate that concentrated ownership positively influences transfer pricing, whereas managerial ownership exerts a negative influence. Meanwhile, foreign, institutional, and family ownership show no significant impact on transfer pricing activities. The experience of the board of directors only moderates the effect of ownership concentration on transfer pricing, with no other significant moderating effects observed. In contrast, executive characteristics successfully moderate the impact of foreign ownership, managerial ownership, and ownership concentration on transfer pricing but not institutional or family ownership.

The study reviews the application of machine learning tools in financial investment portfolio management, focusing on cluster analysis for asset allocation, diversification, and risk optimization. The paper aims to explore the use of clustering analysis to broaden the concept of portfolio diversification beyond traditional volatility metrics. An open dataset from Yahoo Finance includes a ten-year historical period (2014–2024) of 130 actively traded securities from international stock markets used. Dataset selection prioritizes top liquidity and trading activity. Python analytical tools were employed to clean, process, and analyze the data. The methodology combines classical Markowitz optimization with clustering analysis techniques, highlighting variance-return trade-offs. Various asset characteristics, including annualized return, standard deviation, Sharpe ratio, correlation with indices, skewness, and kurtosis, were incorporated into the clustering models to reveal hidden patterns and groupings among financial assets. Results show that while clustering enhances insights into asset diversity, classical approaches remain historically superior in optimizing risk-adjusted returns. This study concludes that clustering complements, rather than replaces, classical methods by broadening the understanding of diversification and addressing many diversity factors, such as metrics of the technical, graphical, and fundamental analysis. The paper also introduces the diversity rate based on clustering, which measures the variance balance by all features within and between clusters, providing a broader perspective on diversification beyond traditional metrics. Future research should investigate dynamic clustering techniques, integrate fundamental economic indicators, and develop adaptive models for effective portfolio management in evolving financial markets.

Corporate structure decisions are the foundation of a company’s legal, financial, and operational framework, influencing diverse issues, from liability and tax obligations to growth potential and public perception. The paper aims to analyze the effect of firms’ financial performance on capital structure decisions. Firm size and corporate governance were taken as moderators and mediators, respectively. The study is based on 23 non-banking public firms listed on the Nepal Stock Exchange, adapting a causal-comparative research design. The moderated mediation model was tested using the Process Macro to assess the impact of corporate governance scores on the relationship between firm performance and capital structure. The result shows that firm performance positively and significantly impacts capital structure decisions. The results revealed no effect of corporate governance on capital structure decisions; however, the moderated mediation impacts of corporate governance and firm size have been reflected in the financing decision. This study extends previous research with the moderated mediation effects of corporate governance and the size of non-banking firms on their financing decisions. The results encourage managers to raise debt funds for those firms that observe the firm’s size, providing practical insights into business decisions. The study also has policy and theoretical implications.

Acknowledgment
We are grateful to everyone who contributed directly or indirectly to the research. We also appreciate the anonymous reviewers’ insightful feedback, which helped enhance the quality of the paper.

This study examines how perceived security and privacy influence user ratings of cryptocurrency applications, which are critical for adoption and satisfaction amid the growing popularity of blockchain technologies and rising concerns over information security in online platforms and mobile apps. The study focuses on mobile applications from the Android app market. It used text mining methods to investigate over 64 thousand text-based user reviews and star ratings of over 140 cryptocurrency-related mobile applications available in the Google Play store. Using a partially supervised machine learning approach, this study first identified reviewer sentiment related to privacy and security, then employed ordinal regression analysis to examine the data to reveal the relationship between perceived security threats, privacy concerns, and app ratings. This study found that crypto apps average 3.84 out of 55 stars, which is higher than Productivity apps (3.46) but lower than FinTech (4.29) and Banking (4.25) apps. Ordinal regression analysis revealed security and privacy threats negatively impact ratings, while robust security measures improve them, with a model Pseudo R² of 0.25. These results have implications for both cryptocurrency app developers and platform managers, offering insights for enhancing user experiences and informing future research endeavors in this domain. It contributes to the literature by integrating the Protection Motivation Theory with sentiment analysis and provides a structured framework for developing an understanding of user behavior in the context of cryptocurrency apps.

This study investigates the investment preferences of institutional investors in Indonesia, focusing on the factors influencing stock selection. A comparative analysis is conducted between pressure-sensitive and pressure-insensitive investor groups to explore how different factors such as corporate social responsibility, corporate governance, shariah-compliant stocks, and financial indicators, including profitability, liquidity, and risk, affect their investment decisions. Data from 938 observations across 253 manufacturing companies listed on the Indonesia Stock Exchange were analyzed using panel data regression. The period was chosen because it captures a stable economic period in Indonesia, allowing for an accurate assessment of investment patterns without major external shocks. The results reveal that institutional investors favor stocks listed on the Indonesia Shariah Stock Index (ISSI), perceived as low-risk investments. Pressure-sensitive investors, such as banks and insurance companies, prefer companies with close business affiliations, while pressure-insensitive investors, such as mutual funds and pension funds, prioritize financial performance and corporate governance. Additionally, the study finds that the debt-to-asset ratio and inclusion in the Shariah index significantly affect institutional ownership, indicating a preference for leveraged companies with ethical investment profiles. This study provides a deeper understanding of the varying preferences between institutional investor groups, highlighting the significance of ethical considerations, financial stability, and corporate governance in emerging markets.

Social media significantly influences investor behavior, particularly in emerging markets like the Egyptian stock market. This study examines its impact on trading frequency, herding behavior, and overconfidence among Egyptian investors. Data were collected through a structured survey of 300 active investors, distributed via two prominent Facebook pages: “The Popular Union of Investors in the Egyptian Stock Market” and “Investment in the Egyptian Stock Market.” The sample was determined using Cochran’s formula for large, undefined populations, achieving a 78% response rate from the 385 recommended respondents. A descriptive quantitative approach was employed, utilizing correlation tests and regression analysis to assess relationships between social media engagement and investor behavior.
Findings indicate that social media usage significantly increases trading frequency, as investors make more reactive decisions based on rapidly available information. Herding behavior is also positively associated with social media engagement, demonstrating that investors tend to follow market trends and decisions discussed in online communities. Additionally, social media exposure fosters overconfidence, leading to increased risk-taking behavior. These insights highlight the critical role of social media in shaping investor behavior, with practical implications for regulators, financial advisors, and individual investors. Regulators should promote investor education on the cognitive biases linked to social media engagement, while financial advisors must address its influence on client decision-making. Future research should explore platform-specific features, such as visual content and influencer-driven financial advice, to better understand their effects on investment strategies.

Acknowledgment
The authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

The study aims to investigate the effect of research and development (R&D) capitalization on revenue-expense matching in the pharmaceutical and biotechnology industries, with particular attention to the moderating role of corporate governance and the influence of regulatory intervention. While capitalizing R&D expenditures enhances the relevance of financial information and positively impacts firm value, it also increases the risk of earnings management, potentially disrupting revenue-expense matching. Using a fixed-effects regression model, this study analyzes 1,350 firm-year observations from Korean listed firms in the bio-pharmaceutical sector from 2012 to 2022. The sample includes firms with financial statements, auditor data, and detailed disclosures on R&D expenditures, encompassing capitalized R&D costs, R&D expenses recognized in income statements, and those classified as manufacturing costs. The results indicate that R&D capitalization generally weakens revenue-expense matching in these industries. However, the adverse effects are mitigated by the effective implementation of corporate governance mechanisms. Additionally, the Financial Supervisory Service’s thematic supervision of R&D accounting practices has significantly improved revenue-expense matching. Prior to the supervision period (2012–2017), firms exhibited significant discretionary capitalization practices, undermining revenue-expense matching. Following the supervision (2018–2022), improved adherence to accounting standards has enhanced matching quality, underscoring the regulatory intervention’s effectiveness. These findings contribute to the literature by demonstrating that while discretionary R&D capitalization can impair revenue-expense alignment, strong corporate governance and adherence to accounting standards can offset these negative effects. The study provides valuable implications for future research and industry practices, particularly in navigating the trade-offs associated with R&D capitalization.

Acknowledgment
This study was supported by Chungnam National University.