Journal Press India^®

In increasingly complex and dynamic financial ecosystems, scholarly discourse informs decision-making, policy creation, and strategic planning. The current issue of the Mudra Journal of Finance and Accounting features a rich tapestry of empirical and theoretical studies on finance and economics, focusing primarily on India but also including global perspectives.

This issue showcases finance research’s growth, from capital structure in commercial banking to AI in credit evaluation.  We begin with the important article, “Does Capital Structure Influence Profitability in Indian Commercial Banks?” “An Empirical Approach using Panel Data Analysis” by Yaipharen Punsiba Meetei Potsangbam and Ch. Ibohal Meitei. The paper examines whether and how capital structure affects Indian commercial bank profitability, notably post-COVID-19. The authors use panel data from 32 Bombay Stock Exchange-listed banks from 2016 to 2023 to find that total debt decreases profitability, while long-term debt does not. These findings support the pecking order theory, which is a key concept in financial economics and suggests that firms prefer to use internal financing rather than external loans. This study clarifies emerging economy and banking capital structure issues. Sourav Chakraborty, Bhaskar Goswami, Mukulika Roy, and Ashima Karmakar study currency interdependencies in Brazil, Russia, India, China, and South Africa in “Exchange Rate Dynamics and Its Spillover Effect among the BRICS Nations. The analysis finds negligible BRICS currency spillover using Diebold and Yilmaz’s technique and weekly data from 2004 to 2023. The Indian Rupee transmits volatility, while the South African Rand receives it. The study’s detailed findings—such as the absence of Granger causation between the Indian Rupee and Chinese Yuan—impact currency risk management, investment strategy, and regional financial integration.

Latika Sharma, Jyoti Parjapati, and Jai Kishan Chandel’s “Unraveling the Nexus between Financial Inclusion and Tax Revenue: Insights from Global Findex Survey” on fiscal health and development addresses a timely issue. This research shows that when more people have access to financial services, it leads to higher direct and indirect tax collection, based on a strong fixed-effect panel model using data from four Global Findex survey years (2011, 2014, 2017, and 2021). It’s particularly intriguing that commercial operations improve tax receipts, but indirect taxes have a more complicated, occasionally inverse relationship. These findings indicate a policy path: improving financial literacy and accessibility could empower the economy and strengthen the state. The study, “Unveiling the Dimensions of Digital Financial Inclusion: An Exploratory Factor Analysis through Howard’s Five Decisions Framework and Demographic Insights,” by Mohd Shafeeq, Sana Beg, and Mansoor Ahmad, takes a novel approach to gender equality in digital financial services by applying Howard’s Five Decisions Framework. The Delhi-NCR study uses T-tests and exploratory factor analysis to investigate if gender disparity continues in digital financial inclusion. Their findings reveal negligible gender inequalities in access, usage, and quality, supporting inclusive digital transformation. These findings show progress as governments and financial institutions pursue universal digital access.

In “Pragmatic Trading Behaviour of Retail Investors in the Derivative Market,” S. S. Mageswari and P. Sasirekha make another intriguing contribution. This study examines investor psychology, specifically retail derivatives market players. The authors show that risk and money attitudes strongly influence trading behavior using snowball sampling, factor analysis, and structural equation modelling. Financial literacy and self-efficacy moderate market dynamics, emphasizing the importance of behavioural finance. The lesson for regulators and educators is that investor education can improve trade rationality. Manisha Choudhary and K. V. Bhanumurthy’s research, “Informational Inefficiency and Behavioural Biases: Evidence from the Indian Life Insurance Market,” applies the behavioral economics framework to the opaque insurance market. The study uses unique analytical methods, including Murthy’s Indexes of Rank Dominance and Heuristic z-tests, to identify availability, inattention, and trust heuristics as behavioral biases that distort buyer behavior. It also shows intermediaries as key informational inefficiencies. In a sector where trust and information symmetry are key, this study provides the framework for regulatory change and customer empowerment in India’s life insurance market.

Sant Kumar’s “The Empirical Performance of the CAPM on NSE Nifty 50: A Study from April 2008 to June 2024 by Using the BJS Methodology” examines the CAPM in Indian equities. The Black, Jensen, and Scholes time-series test of 50 Nifty stocks reveals inconsistent support for CAPM’s central proposition—that all alphas should be zero. Beta’s systemic risk importance is confirmed. These findings contribute to the debate over CAPM’s empirical validity and may inspire asset pricing models tailored to Indian financial markets. Shagun Sharma, K. R. Gola, Nishtha Ujjawal, and Vinod Kumar Bagar examine how financial literacy affects technology adoption in a complementary digital topic. The study uses Smart PLS4 for structural modeling and finds that small merchants with higher financial literacy adopt digital payment systems more enthusiastically. However, low-literacy populations are apprehensive, underscoring digital transformation’s potential and limitations. Fintech developers, government organizations, and NGOs working to create grassroots cashless economies need these insights.

Ejaj Mardan and Karamala Padmasree’s “Impact of Ind AS on Share Price Determinants: Evidence from Listed NBFCs” examines accounting information and share price behavior. The study uses the Ohlson model and panel data from over 200 NBFCs to assess market perceptions after India’s transition to Ind AS (Indian Accounting Standards, harmonized with IFRS). Interestingly, pre-Ind AS value relevance is higher, showing a gap between standards and market interpretability. Regulators and accountants should reflect on new accounting norm implementation and market comprehension after reading this article. This issue concludes with Somesh Kumar Shukla, Ramakant Singh, and Amit Mishra’s forward-thinking “A Comparative Analysis of Conventional Creditworthiness Assessment Methods and AI-Based Assessment Methods.” Traditional credit assessment depends on financial history and collateral, whereas AI-driven models employ behavioral data and machine learning, according to the report. Algorithmic bias and regulatory deficiencies are considered when evaluating each technique. This paper highlights the ethical, operational, and regulatory issues facing financial organizations as they adopt AI. Overall, this issue showcases the range and dynamism of modern financial research. These contributions—from institutional reforms and behavioral insights to technological shifts and global linkages—reaffirm finance’s centrality in economic outcomes. These works should encourage more research, informed policy decisions, and a better knowledge of the ever-changing financial scene.

Editor-in-Chief
Prof. Prashant Sharma

Associate Editor
Dr. Sushma Verma