Nvm Rao

This study examines the dynamic interrelationships between financial technology (fintech), natural resource rents, economic growth, urbanization, and environmental sustainability, using the Load Capacity Factor (LCF) as a composite measure of ecological balance. Unlike prior studies that rely solely on demand-side indicators such as carbon emissions or ecological footprint, this research employs LCF to capture both environmental supply and demand dimensions. Utilizing annual data spanning from 2005 to 2022, we construct fintech index using variables, namely, automated teller machine, mobile cellular subscription, fixed broadband subscription, and internet usage, by employing Principal Component Analysis approach. For preliminary testing, current study considers cross-sectional dependency test, slope homogeneity tests, pedroni and westerlund tests for cointegration and pairwise dumitrescu hurlin panel granger causality tests, and common correlated effects mean group and driscoll-kraay estimation for robustness. For result findings, we utilized the panel Vector Autoregression (Panel-VAR) method to illustrate the dynamic relationships among these variables. Our findings from Panel VAR approach indicate that fintech shocks initially have a positive impact on natural resource rent and load capacity factor but this effect weakens over later horizon, suggesting the need for cautious policy design. Furthermore, economic growth responds positively to fintech shocks, while the influence of fintech on natural resource rent and urbanization appears to be negative. From a policy standpoint, our research suggests that promoting fintech could mitigate environmental degradation and contribute to sustainable development. Graphical abstract

Financial literacy plays a crucial role in shaping individual investment decisions by influencing susceptibility to behavioural biases such as heuristics, framing effects, cognitive illusions, and herding mentality. While most existing studies have examined financial literacy as a mediating factor, our study is among the first in the literature to analyse the role of behavioural biases as mediating factors in the relationship between financial literacy and investment decisions. Specifically, we investigate key biases, including overconfidence, herding, disposition effect, self-attribution, anchoring, availability, representativeness, and familiarity. Using purposive sampling, we collected 482 responses through a structured Likert scale questionnaire. The dataset underwent rigorous validation and reliability tests to ensure robustness. We employed Python-based statistical analysis and used Pearson’s correlation and mediation analysis to explore the relationships between financial literacy, behavioural biases, and investment decisions. With the help of these methods, we were able to uncover relationships and causal pathways which further our understanding of the role of behavioural biases in determining the impact of financial literacy on investment behaviour. The findings illustrate a notable positive correlation between investment decisions and financial literacy, implying that people with higher financial literacy levels possess greater and more rational financial decision-making capabilities. Other analyses have revealed that biases have a moderating effect on this relationship, showing another path through which financial literacy impacts behaviour at the level of the investor. By placing behavioural biases as mediating constructs, this research broadens the scope of investor psychology and the body of knowledge in behavioural finance, highlighting the need to change the approach to how financial literacy programs aimed at investors are structured and implemented.

Asset-Liability Management is critical for the successful working of banks. The Indian Financial system is very dynamic and growing rapidly. Banks focus on both the assets and liabilities due to liquidity risk, interest rate risk and foreign exchange risk. The implementation and understanding of ALM policies, and procedures will provide insight into the approach of banks towards ALM. A primary survey is conducted among employees of twelve public and twelve private banks to collect data related to employees' experience, understanding, and knowledge about the ALM. The questionnaire used in the study carries Yes/No type questions, multiple-choice questions, checklist questions, scaled questions. The data is analyzed using SPSS. The results suggest that public and private banks' employees have a similar understanding and knowledge of the ALM. Both types of banks follow identical practices and policies in the implementation of ALM with few deviations. The employees of the banks were hesitant to answer complex questions, and therefore questionnaire has to be kept generic and straightforward. This paper studies ALM in banks using primary data. All other studies have been conducted using secondary data or literature surveys. These studies did not feature the banks' approach in ALM policies, practices, and procedures. JEL Classification Codes: E58, E43, G33, G32, D81.

In earlier years, there was abundance of funds in banks in the form of demand and savings deposits. Hence, the focus of banks was mainly on asset management. But intense competition and volatility of interest rate due to banking reforms reduced the availability of low-cost funds and therefore, banks focused on liability management as well. These pressures call for structured and comprehensive measures and not just ad hoc action. This is how banks started to concentrate more on the management of both sides of the balance sheet. As a result, the concept of asset-liability management originated in India and introduced in the Indian banking industry since 1st April 1999 to administer the risk management aspects. This paper attempts to optimize assets and liabilities of banks using goal programming technique. Secondary data is collected from annual reports of Allahabad bank from 2010-2019 and RBI website is used for modelling. The findings show that in Allahabad bank, goal programming help in achieving optimization and increase profitability. The model incorporating constraints and set objectives. It model can support banks in decision making process, planning, budgeting, and forecasting. An attempt is made to use realistic goals and constraints after discussing with bank officials. JEL Classification Codes: C61, G21, G32.

Asset Liability Management has gained popularity in the banking sector. Earlier banks focused on asset allocation, but now the management of assets and liabilities is equally essential. Asset liability management targets the optimum distribution of funds in assets and managing liabilities so that banks can earn higher profits and minimize risk. In this paper, the optimization of assets and liabilities of Indian banks has been concentrated using mathematical models. Combining the Analytical Hierarchy Process (AHP) and Goal Programming (GP) model has been used to solve the optimization problem. AHP is a multi-criteria decision-making approach for deriving priority weights. Goal Programming is a linear programming model to solve complex issues having multiple objectives. In this paper, the primary data gathered from Bank senior managers have been analyzed using the AHP approach to derive weights for criteria. These weights are assigned to goals in goal programming to prioritize the goals. Secondary data on OBC bank is used in goal programming from 2010-2019 collected from OBC bank's annual reports and RBI websites. The findings show that OBC bank has the scope of improving its assets and liabilities position to increase its profit and minimize the risk. The model generates an optimum balance sheet that achieves the set goals and satisfies all the statutory and planning constraints. The same model can be useful for scheduled commercial banks in India with modifications concerning banks' targets and controls. The model developed in this paper is helpful for bank managers in planning and forecasting. AHP and GP's combined approach is unique in this paper, which uses experts' knowledge and applies it in the model. The model is created on the bank's realistic goals and constraints after carefully considering the issues faced by bank officials. The paper is limited to the Indian Banking system as other countries have different balance sheet structures and constraints.

This study examines the efficiency of the overall Indian banking industry using Data Envelopment Analysis (DEA) and to perform a comparative efficiency analysis of public, private, and foreign banks using six varied forms. Also, providing ranks to the banks based on their efficiency. The study incorporates BCC output-oriented DEA model using a sample of 50 Indian banks (public banks = 17, private banks = 18, foreign banks = 15) for a period ranging from 2009-10 to 2018-19, hence incorporating the after-effects of the financial crisis and demonetization, this study uses panel data from 2009-10 to 2018-19. The results showed that most of the Indian banks fall on the efficient side or are near to full efficiency. However, public banks outperform private and foreign banks in terms of their average efficiency. Results also specify that the performance of banks is sensitive to input-output variables, units under evaluation, and choice of the model. The current study has just focused on the internal factors for analyzing the efficiency of Indian banks; however, certain external factors might also impact the banks’ efficiency.

Purpose: This paper aims to suggest the preferred mode of financing for major sub-sectors of infrastructure: roads, seaports, telecommunication and energy by examining which mode of infrastructure financing – public, private or public–private partnership (PPP) – has the maximum positive impact on the overall GDP of India. The same exercise was carried out for the overall infrastructure sector by integrating data from all the four sub-sectors.Design/methodology/approach: The structural vector autoregressive approach was used with the period of analysis taken from 1995 to 2014. The stationary properties of the variables were checked by the Phillips–Perron unit root.Findings: The PPP mode of financing was found to make the maximum positive impact on the GDP of India. Considering the four sub-sectors individually, it was concluded that the private mode of financing in roads, energy and telecom sectors has the maximum positive impact on the GDP, while the PPP gives optimal benefit to the seaports sector.Practical implications: Results will aid the Indian Government and policymakers to efficiently design and develop their economic policies accordingly.Originality/value: The study is novel in a sense that it helps to address the lack of research into the area of infrastructure financing in India.