- Market Capitalization Dynamics: A Dual Approach Using Econometric and Machine Learning Models to Assess the Role of Market Liquidity and Macroeconomic Fundamentals
This study examines the complex relationship between market capitalization, market liquidity, and macroeconomic fundamentals in South-Asian economies, using both econometric and machine learning models. It explains the essentiality of understanding the dynamic interplay between market liquidity and economic fundamentals in determining market capitalization to provide understanding for strengthening capital market growth and policy formation in emerging South-Asian economies, where capital markets are quickly expanding but still confront structural constraints such as low liquidity and regulatory fragmentation. Our data showed no cross-sectional dependence, significant cointegration, and the parameters are stationary on the mixed level, so we have chosen panel ARDL regression. Further, the robustness of results is tested through alternative model specifications, non-linear econometric methods (Fixed Effect, Random Effect, and MMQR), and machine learning models (Random Forest, SVR, LASSO, and Ridge) to ensure empirical reliability. Moreover, the SHAP analysis enhances the interpretability of our machine learning models by quantifying the contribution of each predictor to market capitalization. Key findings of our study reveal that while trading volume and domestic credit positively influence market capitalization, equity turnover and inflation exert consistent negative effects, highlighting structural inefficiencies and speculative trading tendencies. Policy recommendations for South-Asia include regulatory reforms to reduce speculative trading, digitalized trading infrastructure for liquidity, SMEs credit access, macroeconomic stabilization, and tax-incentive schemes.; June 2025
- Testing the modeling relevance for growth, inflation, and unemployment: analysis using VAR, ARDL, and DOLS
Managing inflation and unemployment is challenging in Bangladesh, due to their opposing effects on the economy. So, this study aims to examine the dynamic interrelationship between inflation, unemployment, and economic growth in Bangladesh by utilizing annual time series data from 1981 to 2024, we employ three econometric models, Vector Autoregressive (VAR), and Autoregressive Distributed Lag (ARDL) to investigate both the long-run and short-run linkages among key macroeconomic variables. Initially, VAR and ARDL models are applied to assess the relationship between inflation, unemployment, and growth in isolation. Subsequently, ARDL regressions are employed for three distinct models, growth, inflation, and unemployment, by incorporating additional macroeconomic control variables including inflation, unemployment, government expenditure, private capital formation, money supply, foreign direct investment, and governance indicators. Our findings reveal a positive long-run relationship between inflation, unemployment, and economic growth, challenging traditional theories such as Okun’s Law while confirming the relevance of the Phillips Curve in the Bangladeshi context. The results also emphasize the differentiated impacts of public expenditure and private investment on growth and inflation, highlighting the role of fiscal quality and investment allocation in macroeconomic management. Robustness checks using Dynamic OLS validate our ARDL findings. The study offers practical policy implications for balancing growth, inflation, and employment, emphasizing the importance of sound governance, strategic fiscal interventions, and sector-specific investment to ensure macroeconomic stability and support Bangladesh’s sustainable development.; November 2025
- Unveiling the Drivers of NPLs: The Role of Banking Factors, Macroeconomic Conditions, and Institutional Quality of Asian and African Economies Using Econometric and Machine Learning Approach
Non-performing loans (NPLs) represent a critical challenge to financial stability across nine emerging economies in Asia and Africa, where rapid credit growth, macroeconomic, and institutional volatility reinforce systemic risks. This study investigates the determinants of NPLs by employing a novel hybrid methodology integrating Panel ARDL estimation to identify long-run equilibrium, short-run relationships along with checking robustness through Fixed Effect and Random effect, and SHAP (SHapley Additive exPlanations) machine learning analysis for predictive feature importance and non-linear insights, moving beyond traditional approaches. Our findings reveal a complex interplay of factors, with distinct regional patterns. In Asia, NPLs are primarily driven by trend-following credit expansion and inflationary pressures, consistent with the Financial Accelerator mechanism. In Africa, macroeconomic instability, particularly high interest rates, and weak institutional frameworks are the dominant predictors, aligning with Institutional and Credit Rationing theories. The SHAP analysis corroborates these results, identifying bank credit and domestic credit as top global predictors, while highlighting regional asymmetries: inflation and regulatory quality are paramount in Asia, whereas interest rates and macroeconomic shocks generate higher predictive variance in Africa. Based on these insights, we propose distinct policy frameworks:Asia requires countercyclical credit regulations and sector-sensitive capital distribution, while Africa needs institutional reforms focused on collateral registries and interest rate stabilization, with simulations indicating potential NPL reductions. Our research emphasizes that NPLs are a macro-institutional challenge, necessitating integrated, region-specific strategies for financial stability.; December 2025
- Unveiling the nexus of financial inclusion and political stability for capital market participation in South Asian regions
Frequent political uncertainty, governance challenges, financial inaccessibility, and inconsistent policy environments in South Asian countries undermine investor confidence and obstruct the development of capital markets. Thus, this study is motivated to investigate the long-term and short-term consequences of financial inclusion and political stability on capital market participation in South Asian countries. We used a panel ARDL model as the estimates demonstrated no cross-sectional dependence, considerable cointegration, and variables with mixed-level stationarity. The results highlighted financial inclusion, literacy, and savings as the most significant determinants influencing capital market participation in South Asian economies. Interestingly, while political stability did not show a direct long-term effect, its indirect influence through variables such as investor confidence and institutional trust warrants further exploration. The findings suggest that policymakers should prioritize expanding financial inclusion initiatives, improving financial and general literacy rates, and adopting policies that encourage a balanced-saving-investment behavior among citizens to strengthen capital markets.; June 2025
