This study investigates the effect of various firm-level corporate governance mechanisms on the likelihood of financial distress in India. We analyze the competing hypotheses of interest alignment and agency theory, examining how controlling shareholders affect the probability of financial distress. Additionally, we assess the potential impact of institutional investor shareholding and bank-appointed directors on the likelihood of financial distress. Our findings suggest that increasing controlling shareholder ownership decreases the probability of distress. However, the effects of institutional investors and bank-appointed directors vary and also depend on the quantum of shareholding of institutional investors and affiliation with business groups. Our results offer insights into improving governance in economies with higher ownership concentration, weaker institutional frameworks, and greater bank participation in credit facilitation. JEL Codes: G32, G33, G34

Student engagement is critical to creating a collaborative and involved learning environment. The pandemic brought the class room engagement between the faculty and the students to a halt. We tried to find different avenues for the students to reach out to the faculty and to have an open forum to facilitate discussions among themselves. This approach helped us in increasing engagement and also allowed students who would normally not speak during the class hours to contribute to the overall learning during the course. JEL Codes: I20, I23, I29

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem.