Advance Journal of Econometrics and Finance
The Advance Journal of Econometrics and Finance is a valuable resource for economists, finance professionals, and students alike. This journal provides an up-to-date analysis of the latest economic trends, data, and research from around the world. It also covers topics such as asset pricing, financial engineering, macroeconomics, and microeconomics. With its comprehensive coverage of the field of econometrics and finance, this journal is an invaluable source for anyone looking to stay informed on the latest developments in this field.
Impact of Financial Development and Stock Market Performance on Natural Resources: A Financial Resource-Curse Analysis
This study focuses on exploring the effect of natural resources on the commercial and financial development of the nation though Financial Resource Curse analysis in BRICS economics. The coefficients for financial development are mostly negative or insignificant, policies should focus on improving the efficiency of financial systems to better channel funds into sustainable resource development. While, stock market performance does not directly influence natural resource outcomes. GDP growth has a positive and significant effect on natural resources. Policies should aim at harnessing economic growth to promote sustainable management of natural resources. Improved transparency, accountability and governance mechanisms can support sustainable governance. Governments should foster balanced economic policies that support growth without compromising resource sustainability, encouraging the integration of environmental regulations with economic planning. Additionally, policies should encourage investment in green technologies economic growth with environmental protection goals. JEL Code: F36, G14, Q5, O16, F15 Keywords: Financial development, Stock market performance, Natural resources, Financial Resource Curse, BRICS
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Heterogeneous Returns to Education and Gender Wage Inequality in China: An IV-Quantile Regression Analysis
The paper explored the heterogeneous returns to education and gender wage inequality within wage dispersal in China by utilizing East Asian Social Survey (EASS) micro-level data from 2006 and 2016. Education is found to increase wages by about 7.2% on average, though the actual impact appears to be higher, around 15%, once underlying biases are considered. The returns remain fairly stable, between 7.2% and 7.5%, across most of the wage distribution. However, evidence shows clear heterogeneous returns to schooling rise from 12.2% at the 25th percentile to 15.7% at the 50th percentile, and further to 16.6% at the 75th percentile, indicating that individuals at higher wage levels benefit more from additional education. In terms of gender differences, results suggest a greater wage disparity at the lower end of the distribution, with men earning about 14.8% compared to 9.1% for women. The investigated study contributes unique visions by jointly observing both temporal and distributional magnitudes of educational returns in China, going beyond average effects. The implications recommend that while schooling stays to be a critical driver of wage growth, its paybacks are randomly dispersed and reinforce current disparities. Representatives should consider targeted interventions to balance labor market outcomes, especially for females.
Heterogeneous Returns, Gender Disparities, IVQR, Year Fixed Effect
The Impact of Financial Stability on Financial Inclusion: Evidence from Pakistan
This study explores the major determinants of financial inclusion in Pakistan based on the annual data from 2005 to 2024, which is obtained by using the World Bank Global Financial Development Indicator (GFDI), the World Development Indicator (WDI), and the State Bank of Pakistan. The unbalanced panel data set was created to enable determining not only the long-term trends but also the effect of such world shocks as the 2007 2009 financial crisis and the COVID-19 pandemic (2020 2022). Using a regression analysis, the paper examines the relationship between financial stability (FS), economic development (ED), corruption perception index (CPI), and financial literacy (FL) and financial inclusion. The findings reveal that financial literacy has the greatest and most positive influence on financial inclusion, followed by economic development, which does not indicate a significant effect. A low to medium impact is seen in areas of financial stability and inflation. The evidence suggests that financial inclusion in Pakistan is influenced by demand-side factors, which necessitate more targeted efforts to enhance financial literacy beyond macroeconomic growth alone. Such realisations indicate that policy measures that encourage monetary education, better accessibility of financial resources, and inclusive economic systems are needed. Keywords : Financial Inclusion; Pakistan; Financial Stability; Economic Development; Consumer Price Index; Financial Literacy
Crash and Jump: The Impact of Social Media Coverage on Stock Prices in the Pakistan Stock Exchange
This quantile regression model and event research show that social media sentiment influences the volatility and movement of stock prices on the Pakistan Stock Exchange (PSX). In order to gauge investor attitude regarding market movement during significant financial events, the study builds a daily sentiment index using Facebook posts and Twitter tweets. Stock prices are more impacted by negative sentiment than by positive sentiment, and market events associated with crises result in significant volatility and significant losses. The quantile regression model indicates that low-volatility stocks are less responsive to investor sentiment, whereas highly volatile stocks are more vulnerable. OLS robustness tests show that sentiment movements on social media can forecast declines in the stock market. These results imply that risk management and sentiment-based trading may enhance the ability of investors, market analysts, and financial regulators to make sound financial decisions. The findings demonstrate the growing prevalence of behavioral finance in stock market operations, particularly in developing markets like the PSX, where speculative trading and social media forums impact investor behavior. Future studies could enhance financial forecasts and risk assessment models by utilizing machine learning and big data analytics in sentiment analysis. Keywords: Social Media Sentiment, Quantile Regression, Idiosyncratic Volatility, Behavioral Finance, Pakistan Stock Exchange (PSX). https://doi.org/10.5281/zenodo.17334299
The Impact Of Lottery Demand On the Stock Market Through Political Transitions: A Case Study Of Pakistan (PSX).
This study examines the influence of political shocks on stock market performance during four primary election cycles. Quantile regression and portfolio sorting methodologies are employed to examine the impact of political uncertainty on stock returns, market volatility, and investor sentiment. The Fama-MacBeth cross-sectional regression is used to check for robustness. The results indicate that election years are associated with lower stock returns and higher volatility. The adverse effects were especially pronounced in 2008, when the global financial crisis heightened people's concerns about elections, leading to a further decline in the market. The study reveals that stocks exhibiting idiosyncratic volatility (IVOLT) and idiosyncratic skewness (ISKEN), also referred to as lottery-like stocks, tend to perform worse when there is a change in political leadership. This suggests that investors tend to avoid riskier assets and shift toward safer ones when uncertainty prevails. Stocks with very high one-day and five-day returns (MAXRET(1) and MAXRET(5)) do poorly when there is political uncertainty. This supports the idea that volatile stocks are more affected by election cycles. Momentum-based trading strategies also falter during political transitions, indicating that elections disrupt standard market dynamics and result in short-term price reversals. The research also shows that investors should reduce their exposure to high-risk, speculative stocks during election cycles and switch to assets that are liquid and fundamentally sound. This study contributes to the existing literature on political uncertainty and financial markets, offering valuable insights for portfolio management, risk assessment, and investment strategy in contexts of political instability. Subsequent research could be extended to other geopolitical areas to investigate the enduring impacts of political risk on financial stability. https://doi.org/10.5281/zenodo.17334223
Gendered Socio-Economic Impacts of COVID-19Evidence from District Kotli, Azad Jammu and Kashmir
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COVID-19, Socio-Economic Impact, Gender Inequality, AJ&K, Kotli
Justice and Credit: Does Legal System Quality Enhance Financial Development?
A strong financial development depends on stable legal institutions. Most nations face challenges in developing deep credit markets which leads to questions about the role of inadequate legal systems. The research investigates the important relation between legal system quality and financial development by analyzing private sector credit as percentage of GDP. The objective of this study aims to identify which particular components within legal institutions enhance domestic credit levels for the private sector among rule of law, strength of legal rights, contract enforcement efficiency, judicial effectiveness, and corruption control. A fixed-effects regression analysis with 76 countries from 2013 to 2021 functioned to measure the influence of legal system elements on financial development. Stata software processed the data from World Bank resources along with their related credit and legal indicator collections. Experts assumed that institutional changes are genuinely reflected through improved legal indicators while a linear relationship properly describes the connection between law and finance. This research demonstrates that better legal rights for borrowers and lenders as well as faster judicial systems lead to major increases in private credit levels (Laeven & Levine, 2020; Djankov et al., 2021). Broad measures of rule of law and corruption perception fail to exhibit substantial direct impact on private credit once legal factors receive sufficient control. Previous studies confirm that financial credit growth stems from laws protecting both creditors and borrowers along with laws enforcing contractual agreements (Beck, Demirgüç-Kunt, & Levine, 2003; Haselmann, Pistor, & Vig, 2010). The panel results indicate that specific legal reforms create faster financial depth impacts than governance indicators which display slow changes (Berger & Udell, 2020). Data shows that particular legal framework improvements in creditor rights and judicial efficiency create significant financial development results. The research advocates for policymakers to direct their efforts toward these specific legal reforms because they represent a direct approach to increase credit market depth even though sustained rule-of-law development continues being an essential long-term objective. https://doi.org/10.5281/zenodo.17255215
PREDICTIVE ANALYTICS FOR CREDIT RISK ASSESSMENT: ENHANCING FAIRNESS, TRANSPARENCY, AND OVERSIGHT IN CREDIT DECISION OUTCOMES
In today’s business activities including financial services, credit risk assessments and decisions regarding credit outcomes have been transformed from traditional to data driven after the emerging and growing popularity of artificial intelligence (AI) and machine learning (ML) in the sector finance. With the growing admirations of both AI and ML in the sector of finance, there are concerning issues related to ethics and regulations. Many questions are being raises about data fairness, accountability and transparency. The paper attempts to identify responsible AI within the framework of credit risk assessment, and the impact of the situation on the change in the algorithmic bias, data quality, and interpretability technologies using explainable XAI methods to regulate compliance among other aspects. The problem under investigation is the discrimination ability of an artificial intelligence platform, when it is trained using biased or insufficient sample data, leading to some segments of the population being unable to access loan credit. According to the socio-technical systems theory, the technical, regulatory and human components are combined in this work in a way that makes a global vision combining independent variable (algorithmic bias), (data quality), (transparency mechanisms), (compliance practices)(human oversight) and dependent variable credit decisions outcomes. Possible results are to analyze the presence of algorithmic biases, facilitate openness, create accountability mechanisms to guarantee adherence to regulations, and to encourage financial inclusion among those who participate in the credit decision process. The approach is quantitative since survey-based data from financial institutions form the primary data source, augmented with secondary data on credit decision process. Data will be analyzed in Python, using Pandas for data manipulation, NumPy for numerical computing, Scikit-learn to apply machine learning. Advanced deep learning would be done with TensorFlow or PyTorch if required. In order to test hypotheses, and inference regarding interrelationships between variables, regression and structural equation modeling (SEM) are suggested as statistical tools. The implications of the study are both theoretical and practical. In theory, it also adds to the growing literature of responsible AI in finance decision-making. More generally, it provides a window for financial institutions and regulators to develop fair, transparent and compliant AI systems that do not introduce bias but build trust. The authors indicate that future work is needed to validate this framework in different contexts, examine long-term effects of responsible AI on financial inclusion, and compare cross regional regulatory responses for globally responsible use of AI in credit risk assessment. Keywords: Algorithmic Bias, Data Quality, Explainable AI (XAI), Regulatory Compliance, Human Oversight, Credit Decision Outcomes, Responsible AI, Financial Inclusion
Volatility in Fiscal Policy and Terms of Trade: An ARDL Analysis of Business Cycle Fluctuations in Pakistan
Volatility in fiscal policy and terms of trade has diverse macroeconomics implications. This study examines the impact of government spending volatility and terms of trade volatility on output volatility in Pakistan by utilizing time-series data from 1980 to 2021. After confirming the mixed order of integration of variables through unit root testing and the existence of a long-run relationship (cointegration) among variables, the long-run and short-run coefficients are estimated using the Autoregressive Distributed Lags (ARDL) estimation technique. The findings depict that volatility in fiscal spending and terms of trade increases volatility in the output, i.e., amplify fluctuations in the business cycle and leads to macroeconomic instability both in the short run and long run. Therefore, stable and flexible fiscal and trade policies should be designed to minimize the cost of output volatility. The long run empirical evidence from the control repressors indicates that trade openness, foreign direct investment, financial development, and capital formation stabilize output volatility. Keywords: Public Spending Volatility, Output Volatility, Terms of Trade Volatility, ARDL Modeling, Pakistan.
Unveiling the Economic Nexus: How Foreign Direct Investment, Gross Domestic Product, and Energy Consumption Shape Pakistan's Export Trajectory?
High population growth in developing countries is generating an extraordinary rise in demand for energy, urban development, and agricultural infrastructure. Global environmental obligations will be challenging when fulfilling growing demands. These issues promote unfavorable settings and major constraints in turning the economy into a green growth economy. This shows the countless stakeholders in the economy's accountability towards sustainability. This study investigates the dynamic nexus between FDI, GDP, energy consumption, and exports in Pakistan for the period 1991-2022 by using the NARDL approach. The findings demonstrate a statistically significant positive relationship between FDI and GDP, indicating that increased FDI drives GDP growth. By addressing gaps in cross-sectional analyses, this research enriches existing literature and highlights opportunities for comparative studies with other developing countries. Key Words: FDI, Energy Consumption, Exports, GDP, NARDL, Pakistan
The Impact of Corporate Social Responsibility on Financial Performance of Listed Companies on Pakistan Stock Exchange
This is reported that Corporate Social Responsibility (CSR) play a vital role in the performance of companies. Therefore, in this study examined that how financial performance of listed companies is affected by the CSR. For this purpose selected 26 companies from cement, chemical, and oil & gas sectors of Pakistan during 2014 to 2018. These industries are important for the economic development and sustainable business practices. For this purpose used CSR as independent variable, Return on assets (ROA), and Return on Equity (ROE) are used as dependent variables and firm size and ownership concentration are used as control variables. The results shows that CSR positively and insignificantly effect the firm performance. However, CSR negatively and insignificantly effect the firm performance. Therefore, suggested that performance of firms are not affected by the CSR activities of firms selected in this study. Keywords : Corporate Social Responsibility (CSR), Financial Performance, Ownership Concentration and Panel Data Analysis
Linking Paradoxical Leadership to Team Innovation: A Social Cognitive Perspective on the Role of Innovation-Supportive Climate
Team innovation is a lynchpin and play crucial role in delivering the high quality service which is indispensable for competitive advantages, economic prosperity and sustainability for the tourism and the hospitality industry in rapidly changing environment. Emerging research increasingly acknowledge that innovation inherently involve paradoxes often involves navigating conflicting demands and divergent perspectives. The inherent paradoxes in the process of innovation have turned the scholar intention towards paradoxical leadership behavior as potential driver for nurturing the innovation. Drawing upon the social cognitive theory, the study aims to explore the relationship between paradoxical leadership behavior and the team innovation particularly focusing on innovation supportive climate as mediator. Data assembled from 20 hotels and restaurants by adopting cross sectional approach through purposing sampling techniques from 720 employees including leader of the team and the employees working under their supervision. Using AMOS and SPSS for data analysis, it was found that paradoxical leadership behavior positively related to team innovation. Besides, supportive innovation climate was also observed, playing a significant role as mediator between paradoxical leadership behavior and the team innovation. The research is novel in the way that it investigates the relationship between paradoxical leadership behavior and team innovation with innovation supportive climate as mediator and provide theoretical and practical insights in to relationship between paradoxical leadership behavior and team innovation and revealed valuable implications for the practitioners in the hospitality industry. Keywords : Paradoxical leadership behavior (PLB), Innovation supportive climate (ISC), Team innovation (TI)
Impact of Work Rewards on Employee Performance: A Case Study of Pakistan's Higher Education Institutions (HEIs)
This study examines the impact of work rewards on employee performance within Pakistan’s Higher Education Institutions (HEIs). Employing a mixed-methods approach, we distributed 400 survey questionnaires to academic and administrative staff across public and private universities in Pakistan. The survey instrument incorporated structured scales to measure both intrinsic (e.g., recognition, career development) and extrinsic rewards (e.g., salary, benefits), alongside performance metrics such as productivity, job satisfaction, and retention. Data analysis was conducted using Smart-PLS 4 for Partial Least Squares Structural Equation Modeling (PLS-SEM) to validate construct relationships and IBM SPSS for descriptive statistics, reliability testing, and correlation analysis Preliminary findings indicate that reward satisfaction significantly predicts employee performance, with extrinsic rewards like competitive salaries and benefits being primary drivers in Pakistan’s socio-economic context. However, intrinsic rewards, particularly recognition and professional growth opportunities, emerged as critical for long-term retention and engagement . The study also identifies structural challenges unique to HEIs, including bureaucratic reward systems and inequitable distribution, which undermine efficacy. The research highlights the necessity of tailored reward strategies that align with cultural and institutional dynamics. Recommendations include implementing regular recognition surveys to gauge employee preferences, enhancing transparency in reward allocation, and leveraging data-driven tools like PLS-SEM to model optimal reward structures. This study contributes to both theoretical discourse and practical management in HEIs by providing a framework for reward systems that boost performance while addressing Pakistan’s specific challenges. Future research should explore longitudinal impacts and integrate broader socio-economic variables.
Work Rewards, Employee Performance, Higher Education Institutions, Pakistan, Smart-PLS, SPSS, Recognition Surveys, Structural Equation Modeling.
The Role of Leadership Effectiveness and HRM Policies in Enhancing Organizational Performance: Mediating Effect of Employee Motivation
The current competitive and fast changing business world has put more and more pressures on organizations due to the forces of globalization, technological change and shifting expectations of the workforce. The study analyzes the association between leadership performance, human resource management (HRM) and organizational outcome and specific to employee motivation as a mediator. The design employed was a quantitative, cross-sectional design, where 300 employees and managers in service and manufacturing companies were in Lahore in Pakistan were surveyed. Measures like the Multifactor Leadership Questionnaire (MLQ), which were tested and proved valid in European countries, were used to guarantee the validity and reliability. Structural Equation Modeling (SEM) was used to analyze data in AMOS with bootstrapping methods to test mediation effects. The findings suggest that the effectiveness of leadership is a significant and positive influence on the organizational performance, and visionary and inspiring leadership should be valued as the contributor to the innovation and success. The HRM policies were discovered to have a strong impact on the motivation of employees, which is in line with the Ability-Motivation-Opportunity (AMO)-theory. Moreover, employee motivation became one of the key mediators, as it connected leadership and HRM policies with some actual performance results. The model also showed superior fit indices proving it to be valid and theoretically sound. The research also adds to the theory by incorporating leadership, HRM, and motivation in the same framework to have a holistic picture of what drives organizational performance. In practice, it offers useful lessons to managers and HR experts on how to align leadership practices with HR systems to have a motivated and engaged workforce. Its cross-sectional nature and regional orientation are its limitations and future studies should be based on longitudinal and cross-cultural designs to be more general. Keywords : Leadership Effectiveness; HRM policies; Employee motivation; Organizational performance; Mediation; Structural Equation Modeling (SEM); High-Performance Work Systems (HPWS); AMO framework; JD-R model. https://doi.org/10.5281/zenodo.17231077
A Time Series Anaylsis Of Autoregressive Distributed Lag (ARDL) To Co Integration Technique: The Results Applications
This research examines Autoregressive Distributed Lag (ARDL) to co integration technique and its theoretical background, advantages, application, interpretation and problems of ARDL to co integration approach. ARDL approach to co integration technique has flexible behavior about variables, can be applied in the presence of same or different stationary order or mutually integrated. This approach has a compulsory condition that none of variable in the model should be at second difference. This technique is applied on time series dataset while time series dataset has to face unit root problems. In the presence of unit roots problems the forecasting model gives the spurious results. The bound test (Wald test) or F-statistics reflects the long run relationship of the variables. The value of F-statistics exceeds the lower and upper limits at 90% and 95% confidence interval represents the co integration among variables exists in long run. In case of low value of bound test shows absence of co integration. Error Correction Mechanism is used for the picture of short run results. The negative value of ecm (-1) represents the model is highly significant and convergence towards equilibrium. The Lagrange Multiplier test for serial correlation and Ramsey Reset test for correct functional form is used in diagnostic test scenario. The value of F-version and LM-version is more than 10% or 5% fulfills all the assumptions of Ordinary Least Square. Pesaran and Pesaran (1977) applied the stability test if the plot of CUSUM and CUSUM sum of square lies in between 5% critical bound limits showed model is stable without structural breaks. Keywords: ARDL Approach; Bound Test; Error correction Mechanism; Unit Root Tests; Stability
An Investigation of the Impact of Globalization on Financial Markets
The present study examined the effect of globalization on the financial markets of Pakistan based on an extensive mixed-method research project between 2000-2023. The study relied on secondary data provided by State Bank of Pakistan, Pakistan Stock Exchange, World Bank and IMF databases and was also complemented by structured interviews with 15 senior financial sector professionals. Important results demonstrated that there is a strong positive correlation between the measures of globalization and the measures of the development of financial markets in Pakistan. KOF Globalization Index was found to be strongly related to growth of stock market capitalization (r=0.74), inflows of foreign direct investment (r=0.68) and openness to trade (r=0.82). But globalization also raised the volatility and sensitivity of markets to external shocks. The qualitative analysis reflected that, although globalization made markets more efficient, liquid and more exposed to international capital, it also opened up Pakistani financial markets to more systemic risks and contagion effects. The paper found regulatory frameworks to be important mediating variables in the management of the two impacts of globalization. Time series analysis proved the enhancement of the financial sector growth rate in more globalized times, especially after 2008 when Pakistan introduced the extensive financial liberalization policy. These results can be added to the existing knowledge on the relationship between globalization and the emergence of new financial systems in the market and give a clue to policymakers who struggle to find their way in the world of global integration. Keywords : Effect, Globalization, Financial Markets, State Bank of Pakistan, Pakistan Stock Exchange.
Board’s Knowledge and Skills around Mergers and Acquisitions Deals in SAARC Region
The purpose of the study is to examine the board’s knowledge and skills before and after mergers and acquisitions (M&A) in emerging and frontier markets of the SAARC region. Sample of study is consisting of 104 domestic M&A deals from India, Pakistan and Bangladesh, occurred during 2000 to 2020. The sample includes only those M&A deals whose board members’ data is available for 3 years before and 3 years after M&A deals. Univariate analysis (paired sample t-test) is used to compute mean difference of variables related to board knowledge and skills in pre-and post-M&A period. Findings of the study show that board busyness decreased, and growth is observed in board size, independence and vigilance in post-M&A period. Board accountancy knowledge, business knowledge, managerial experience, technical knowledge, audit experience, gender diversity shows an increase in the post-M&A period compared to the pre-M&A period. The current study uses exclusively domestic M&A deals and ignore cross-border M&A deals. This study does not consider the influence of highly skilled board on post-M&A financial performance of acquiring entities and future researchers are encouraged to examine it. Future researchers are encouraged to include cross-border M&A deals to examine its impact on national firms’ governance standards. Keywords: Board’s Knowledge and Skills, Mergers and Acquisitions, Accountancy Knowledge, Board vigilance, Gender diversity
Corporate Governance in Action: The Interplay of Ownership Structure, Board Composition, and Firm Performance
This study investigates the impact of institutional ownership and board structure on firm performance in an emerging market context. Using a twelve-year panel (2013–2024) with a random effects model, the results show that institutional ownership and board size significantly enhance performance, measured by return on assets (ROA) and return on equity (ROE). Institutional investors improve monitoring and accountability, reducing agency costs, while larger boards provide diverse expertise and networks that support stronger outcomes. Firm size also demonstrates a consistent positive effect, reflecting economies of scale. In contrast, board independence and gender diversity show limited influence, suggesting their effectiveness depends on institutional and cultural factors. The study extends corporate governance literature by jointly considering ownership and board attributes, highlighting the value of institutional investors in improving governance. For regulators, the findings stress the need to foster institutional investment, while for firms, they point to board expansion as a strategic advantage.
Unearthing Local Realities: A Micro-Level Investigation of Public Education and Healthcare Access in Pakistan
This study examines provincial variations in user satisfaction with healthcare and education services across Pakistan, drawing on data from the Pakistan Social and Living Standards Measurement Survey (PSLM) 2019–2020. The dataset, which includes more than 160,000 observations for certain variables, provides a robust foundation for quantitative analysis. Descriptive findings suggest that access to services is relatively widespread, with short travel distances and generally high levels of satisfaction reported. Yet, significant regional disparities emerge, reflecting uneven distribution and quality of services. To test these differences, Pearson’s Chi-square test of independence was employed. Results reveal strong statistical associations (p < 0.05) between province and satisfaction, with Punjab consistently recording higher satisfaction levels, while Baluchistan shows markedly lower outcomes across several indicators. These findings underscore persistent geographic inequalities in service delivery. Addressing such gaps requires targeted, region-specific policies designed to enhance both accessibility and quality, thereby fostering more equitable development and balanced socio-economic progress across the country. Keywords : Pakistan Education and Health care, Pakistan Social and Living Standards Measurement Survey, Chi-square, Micro-Level Quantitative Analysis
The Role of Financial Literacy in Personal Finance Decision-Making
The proposed study examines the complicated association between the financial literacy level and the decisions about personal finance depending on different demographic groups in a mixed-methods study. The study explores the linkage between financial knowledge and financial behavioral outcomes such as savings levels, investment decisions, debt decision processes, and retirement plans among 500 sample participants whose age group, educational level and geographic spread is well distributed. Quantitative analyses show that financial literacy has a high positive correlation with both positive financial behaviors and overall financial decision-making quality and that after controlling demographics, over 48 percent of variance is due to financial literacy alone. The paper finds that there is a considerable difference in financial literacy according to various demographic groups such that middle aged patients (46-60) have the highest scores of financial literacy, males are better than females on average by three points, and urban dwellers have a high success rate in financial literacy than the rural dwellers. Results of 30 semi-structured interviews lead to several identified mediating mechanisms: self-confidence and self-efficacy in financial decision-making, the ability to process information systematically and evaluate information, high sensitivity to risk perception and risk management capacity. The study identifies how financially literate individuals are drastically more likely to participate in more advanced financial products with 78.2% of highly literate individuals investing in the stock market versus 23.1% of people with low literacy levels, indicating a major role to play by financial education in improving economic prosperity and informed financial management. Keywords : Association, Financial, Literacy Level, Personal Finance, Different Demographic Groups, Financial Knowledge, Financial Behavioral. .
Impact of Dividend Policy on Firm Performance of Shariah-Compliant Firms: Evidence from Pakistan
This research aims to explore the relationship between dividend policy and firm performance in Shariah-compliant (SC), non-financial firms listed on the Pakistan Stock Exchange from 2016 to 2021. A dynamic panel of 66 SC firms was identified using Karachi Meezan index screening criteria, and fixed-effects regression models were selected based on BPLM and Hausman tests. Firm performance is measured through return on assets (ROA), and return on equity (ROE), whereas dividend policy is represented by the dividend payout ratio (DPO), dividend per share (DPS), and dividend yield (DY). The empirical findings indicate that DPO has a positive and significant impact on both ROA and ROE, supporting signaling and agency cost theories. In contrast, DPS showed a weak positive association with ROA, and an insignificant relationship with ROE, while DY remains insignificant in both models. The F-statistics and moderate R² values confirm the overall robustness and explanatory strength of the models. These results provide practical knowledge to corporate managers and investors who aim to ensure that their financial decisions are aligned with profitability and ethical observance. This study fills the gap in the literature on Islamic corporate finance, as it has empirically validated the significance of dividend policy as a key determinant of performance under the SC framework. The findings may not be generalizable to other Islamic markets or conventional firms; however, they contribute fresh, statistically reliable evidence on how dividend behavior influences firm performance in an emerging Islamic market.
The Impact of Macroeconomic Indicators on the Performance of Islamic Stocks: Evidence from KMI-30 Index
The study examines the impact of three principal macroeconomic factors (GDP growth, interest rates, and inflation) on the performance of Shariah-compliant stocks in Pakistan. KMI-30 Index is used which monitors Islamic stocks, utilizing yearly data from 2010 to 2024. The study seeks to determine the impact of macroeconomic factors on Islamic stock performance in both the short and long term. Initially, we evaluated the stationarity of the data with the Augmented Dickey-Fuller (ADF) test. The initial data exhibited non-stationarity, but achieved stationarity at first difference. Subsequently, the study conducted the ARDL cointegration test, which indicated the presence of a long-term equilibrium association among the variables. This indicates that these variables exhibit correlated over time. Our long-run estimates showed that GDP growth has a positive and significant impact on Shariah stock returns. In other words, as the economy grows, Islamic stocks tend to do well. On the flip side, interest rates had a negative and significant effect. Even though Islamic finance doesn’t deal directly with interest, higher rates still reduce investor desire for stocks overall. Inflation also has a negative impact but it is weakly significant. We employed an Error Correction Model (ECM) to analyze the adjustment of market self-correction following abrupt fluctuations. The findings indicated that around 65% of any short-term disturbance is rectified annually, signifying a rather rapid return to long-run equilibrium. This indicates a robust and adaptive market structure. The findings indicate that Shariah-compliant stocks are influenced by economic conditions; they prosper during robust economic development but face difficulties due to increasing interest rates and inflation. This study is beneficial for investors, analysts, and policymakers engaged in Islamic finance, since it provides insights into how macroeconomic indicators might inform wiser investment decisions. It is essential to comprehend the operation of these connections in order to effectively manage risks and optimize your investment returns, in an ethical, faith-based investment framework. Keywords : Shariah-compliant stocks, macroeconomic indicators, Stock return, KMI-30 index, GDP, Interest rates, Inflation, Autoregressive Distributed Lag (ARDL), Error correction term, Islamic finance.
Developing an Integrated AML Risk Management Framework for Commercial Banks Based on Customer Risk Profiling and Enhanced Due Diligence
This study examined the effectiveness of integrated Anti-Money Laundering (AML) risk management frameworks in commercial banks, focusing on customer risk profiling, technological adoption, and regulatory compliance mechanisms. Using both quantitative and comparative analysis, the research explored how transaction volume, source of funds transparency, geographical exposure, and politically exposed persons (PEPs) criteria influence risk categorization across low, medium, and high-risk groups. Results revealed that transparency in source of funds and monitoring of PEPs emerged as critical determinants in classifying customers into higher risk categories. Furthermore, the adoption of technology significantly enhanced AML efficiency, with automated transaction monitoring and data analytics dashboards demonstrating higher adoption rates and effectiveness scores compared to blockchain and machine learning tools. Overall, the findings suggested that an integrated approach combining advanced technology, risk-based profiling, and regulatory compliance improved detection accuracy, reduced fraudulent activity, and strengthened operational efficiency. However, gaps remained in addressing high-risk categories and ensuring the explainability of AI-driven tools for regulatory acceptance. The study emphasized the importance of balancing technological innovation with regulatory adaptability, particularly in cross-border contexts where financial crime risks are more complex. Future research should explore emerging technologies such as privacy-preserving AI and decentralized finance (DeFi) frameworks to strengthen global AML strategies while ensuring compliance and operational sustainability.
Aml Compliance, Customer Profiling, Financial Crime, Machine Learning, Regulatory Frameworks, Risk Management
From Stability to Collapse: Investigating the Determinants of Bank Crises in Upper Middle-Income Nations
This study examines the factors leading to bank crises in upper-middle-income countries by analyzing macroeconomic banking factors and financial indicators unique to individual banks. Using data from the WDI, it employs panel data regression analysis from 2001 to 2020 to assess how key macroeconomic factors, such as financial system deposits to GDP, deposit money banks' assets to GDP, and central bank assets to GDP, influence bank crises (NPL). Additionally, the study investigates how bank-specific attributes like net interest margin, noninterest income to total income, overhead costs to total assets, and return on equity (ROE) impact loan performance and credit risk management. By examining these factors collectively, the research offers insights into how financial institutions and policymakers can develop strategies to reduce credit risk, improve regulatory frameworks, and bolster banking sector resilience, ultimately contributing to the prevention of crises and sustainable economic growth. The results indicate that deposit money banks' assets to GDP, higher net interest margins, and higher ROE contribute to lower NPL, suggesting that well-capitalized banks manage credit risk more effectively. However, the increase in deposit money banks' assets to GDP has led to greater market concentration and higher non-performing loans, pointing to riskier lending practices. Meanwhile, central bank assets, net interest income, and bank deposits do not significantly impact the level of non-performing loans. These findings can assist policymakers and banking institutions in developing effective risk-management strategies to prevent future financial crises. Keywords: NPL, ROE, Financial System Deposit to GDP, Bank Crisis
INTERNATIONAL REGULATIONS AND NON-PERFORMING ASSETS: MODERATING ROLE OF MARKET CONENTRATIONS
Banks in Pakistan are vital and essential for economic stability, growth, and financial inclusion. However, efficient credit management significantly impacts the overall success of banks. Considering the fact, the central banks have taken a number of initiatives in this regard and implement different international regulations in Pakistan. Hence, the basic aim of this research is to find the relationship between the international regulations and nonperforming assets with market concentrations as a moderator variable. This study has obtained data from the annual reports of selected banks. The data have been taken from SBP’s website from the period of 2007 to 2016 with 235 observations are considered. The current study results show that stricter or more comprehensive international regulations may lead to a reduction in non-performing loans (NPLs) in Pakistan’s banking sector. However, the moderating role of market concentration particularly in a financial landscape dominated by a few major banks remains ambiguous. The current study intends to provide deeper insights for important stakeholders such as bank managers, policymakers and regulatory authorities to formulate more effective strategies for operational efficiency as well as financial performance of Pakistan Banking Sector.
International Regulations, Non-performing Assets, Market Concentration, Pakistan Banking Sector, Credit Management
Analyzing the Contribution of Sustainable Development Goals in the Economic Growth of Pakistan
Sustainable development goals have played a significant role in economic growth of Pakistan. Developing countries like Pakistan have a major issue of sustainable development and economic growth. The main objective of the study to determine the factors of sustainable development goals effect the economic growth of Pakistan. The nature of the data was quantitative and secondary. The data for the study variables was conducted from World Bank. A time series data from 2014 to 2024 was used for analysis of the study. It has ensuring that all the variables were stationary by employing unit root tests, namely Augmented Dickey Fuller (ADF) and Phillips Perron tests. Vector Error Correction model was used based on unit root test results. The results of the Unit Root and Phillips-Perron test showed that all the variables were stationary at first difference I (1). This study based on Endogenous Growth Theory and sustainable development theory. The results of the study shows that the poverty rate has a statistically significant and negative influence on Economic Growth (coefficient = –0.412, p = 0.001), while employment rate has a high positive influence (coefficient = 0.376, p = 0.002). The uses energy possesses the biggest positive coefficient (0.551, p = 0.001). Unemployment has a negative effect on Economic Growth in the long run (p = 0.026), indicating that increasing unemployment erodes potential output. Government debt, while marginally significant (p = 0.079), also has a modest negative impact, as it is with fear of fiscal unsustainability and debt overhang. The error correction term is negative and highly significant (–0.472, p = 0.001), reflecting that approximately 47.2% of disequilibrium in Economic Growth is corrected each period. Among short-term effects: Poverty has a significant negative effect on Economic Growth (–0.198, p = 0.048), proving the short-run drag of poverty increases. Energy Consumption has a positive effect on Economic Growth (0.194, p = 0.021). The study suggested that broaden the energy basket to promote investments in hydroelectricity, wind, and solar energy to enable sustainable development and reduce dependence on imports. Enhance Energy Efficiency: Introduce incentives to industries and household consumers toward the use of energy-efficient technologies and practices. The negative impact of government borrowing on GDP highlights the need for sound fiscal management. Halt unproductive borrowing and concentrate on spending in growth-inducing sectors like infrastructure, healthcare, and education. Keywords: Sustainable Development, Economic Growth, Unit Root Test, Vector Error, Correction Model.
The Role of Artificial Intelligence in Enhancing Financial Forecasting Accuracy
This study investigates the role of artificial intelligence (AI) in enhancing the financial forecasting accuracy of listed firms in Pakistan, with a specific focus on the mediating effect of financial performance. Using data collected from 100 firms listed on the Pakistan Stock Exchange, the research examines how AI-driven tools and techniques influence the precision of financial forecasts related to revenues, costs, and profitability. The findings reveal that AI significantly improves forecasting accuracy by enabling firms to process large and complex datasets, detect non-linear patterns, and adjust predictions in response to dynamic market conditions. Furthermore, the study demonstrates that financial performance partially mediates this relationship, as firms with stronger profitability and operational efficiency are more capable of investing in advanced AI technologies, thereby achieving even more accurate forecasts. Sectorial differences indicate that banking and manufacturing firms have higher AI adoption levels compared to service-oriented firms. The study contributes to financial management literature by empirically validating the direct and indirect effects of AI on forecasting accuracy and offers practical insights for corporate managers and policymakers seeking to enhance decision-making, investor confidence, and market competitiveness through technological integration.
Artificial Intelligence, Financial Forecasting, Machine Learning, Pakistan Stock Market
Board Diversity and Green Innovation: A Systematic Literature Review
As the world continues to face mounting environmental crises, the prospects for green innovation have emerged as a strategic priority for firms striving to develop sustainably. This paper presents a systematic literature review on board diversity, emphasizing the characteristics of board diversity (gender, age, education, nationality, and professional background) on environmentally responsible innovation activities. Using the PRISMA methodology, 50 empirical and conceptual studies from 2010-2025 across Scopus, Web of Science, and Science Direct were identified. In general, the analysis of the articles shows a positive and significant relationship between board diversity and green innovation, with gender diversity and international diversity being especially impactful. By creating more diverse boards, organizations automatically expand the potential for innovation relative to environmental awareness, ethics, and engaging with stakeholders in strategic dialogue. Furthermore, this relationship may also be moderated and mediated by alt variables such as environmental disclosure, corporate social responsibility (CSR), and ESG performance. The review enhances the theoretical landscape by combining the Resource-Based View (RBV), Stakeholder Theory, and Upper Echelons Theory to explain the diversity and innovation relationship. It provides evidence of contextual variation across industries and nations, pointing to the relevance of institutional dimensions. It also offers policy and practice implications for policymakers, company managers, and investors on the possible benefits of diversity of board governance for innovation. The review also notes the absence of longitudinal studies and posits gaps, as well as highlighting the need for more complex, contextually-sensitive and multi-theoretical approaches to study the interaction of boardroom diversity with environmental innovation. Keywords: Board Diversity, Green Innovation, Gender Diversity, Environmental Sustainability, PRISMA, Corporate Governance, ESG, CSR.
Impact Of Job Stress On Turnover Intention, Moderating Role Of Workplace Incivility, In Banking Sector Of Mirpur Division
The study investigates the effect of job stress on employee turnover intention in the banking sector of Mirpur Division, with workplace incivility examined as a moderating factor. Data were collected from 150 male and female employees through questionnaires, achieving a 75% response rate from 200 distributed forms. Findings indicate that higher job stress is strongly associated with increased turnover intention, while workplace incivility intensifies this relationship. This highlights the necessity for banks to implement stress management strategies and cultivate a supportive environment to reduce employee attrition. https://doi.org/10.5281/zenodo.17362147
FinTech and Financial Inclusion in SMEs of Afghanistan: Exploring the Moderating influences of Perceived Regulatory Support and Continued Usage Intention
This study focuses into the factors that influence FinTech adoption and its impact on financial inclusion (FI) in Afghanistan's small and medium-sized enterprises (SMEs). Employing the Technology Acceptance Model (TAM) and related trust-based frameworks, the study investigates the impact of perceived ease of use (PEOU), trust (TRS), service quality (SEQ), and perceived security (PES) on FinTech adoption. Additionally, it explores how FinTech usage improves financial inclusion (FI) and how this relationship is influenced by continuing usage intention (CUI) and perceived regulatory support (PRS). The data collected from SMEs participants was examined using structural equation modeling (SEM) in SmartPLS. The results reveal that PEOU (β = 0.212, t = 4.817, p <.001), TRS (β = 0.199, t = 3.303, p =.001), SEQ (β = 0.248, t = 4.274, p <.001), and PES (β = 0.149, t = 2.359, p =.018) have significant effects on FinTech usage. FinTech usage significantly predicts FI (β = 0.608, t = 13.226, p <.001). CUI (β = 0.125, t = 3.070, p =.002) did not moderate the relationship and while PRS (β = 0.106, t = 2.610, p =.009) positively moderated the association between FinTech usage and financial inclusion. These findings indicate that enhancing usability, trust, quality, and security perceptions can greatly increase FinTech adoption, improving access to financial services. Further, maintaining continuing interaction and supporting regulatory settings increases this impact. The study complements to the expanding literature on FinTech in emerging economies and has practical implications for policymakers, FinTech developers, and SMEs in Afghanistan. Key words : FinTech adoption, financial inclusion, SMEs, continued usage intention, regulatory support