Murugesan Selvam

Exploring the potential of financial technology (FinTech) to promote financial inclusion is the aim of this research. This study concentrated on understanding why people use FinTech and how it affects their access to financial services by taking into account the mediating role of digital financial literacy and the moderating effect of perceived regulatory support. This study used partial least squares structural equation modeling (PLS-SEM) for testing the research model by collecting data from 608 FinTech users in India. The results revealed the role of trust, service quality, and perceived security are essential in promoting the utilization of FinTech services. This study also demonstrated that FinTech positively impacts financial inclusion, making it easier for individuals to get into formal financial services. Furthermore, digital financial literacy emerged as an important mediator between FinTech use and financial inclusion. The research also confirmed that perceived regulatory support has a significant moderation influence on the relationship between FinTech and financial inclusion. This research would contribute to advancing theoretical frameworks and offer practical advice for policymakers and FinTech companies to make financial services more inclusive.

Financial technology (FinTech) is transforming the financial services industry by offering innovative, convenient solutions for businesses and individuals. This study examines the factors influencing FinTech adoption, with a special focus on trust. By integrating insights from both the unified theory of acceptance and use of technology (UTAUT2), and the trust theoretic model (TTM), this research uncovers critical determinants of FinTech adoption. Utilizing survey responses obtained from 399 participants, this research employs the partial least squares structural equation modelling method. The findings reveal that performance expectancy, effort expectancy, social influence, habit, price value, and facilitating conditions significantly influence users’ intentions to use FinTech services. In addition, the study shows that trust plays a crucial role in FinTech use, as it influences both the intentions to use and the actual use of FinTech. Surprisingly, hedonic motivation was found not to affect users’ intentions, implying that people see FinTech as a practical, rather than enjoyable, endeavor. These insights provide valuable guidance for service providers and policymakers seeking to enhance FinTech adoption and utilization while ensuring the security and trustworthiness of these digital platforms.

The goal of this study is to look into how changes in crude oil prices affect GDP per capita and exchange rate fluctuations.to investigate the influence of crude oil price shocks on GDP per capita and exchange rate movements. This research employed yearly time series data for the price of crude oil, exchange rate (USD/INR), and GDP per capita, from 1990 to 2020. Arithmetical tools such as Descriptive, Unit Root, Granger Causality Test, and OLS Model were applied. The present study discovered a strong bi-directional Granger causality effect of Dubai crude oil prices on exchange rates, as well as a bi-directional Granger influence of exchange rates on WTI crude oil prices. The diagnostic tests were successfully passed by the estimated models. According to the OLS model, the exchange rate was driven only by the price of Dubai crude oil, although the price of WTI crude oil influenced both the GDP per capita and the exchange rate over the research period. The key policy recommendation derived from this analysis is that the Reserve Bank of India (RBI) must depreciate the rupee, first to restore much-needed exchange rate stability, then to stimulate domestic manufacturers, and finally, to attract foreign capital inflows.

The paper presents a systematic review of the research work, published on the topic of weather effects and stock market behavior. The objectives of the study were to examine the current status of research, by collecting the literature, in the area of weather effects and stock market behavior. In the process, the study would reveal the current status to the budding researchers. In this study, the authors critically assessed and examined fifty one research studies, published from 1993 and 2019, in different regions across the globe. A systematic literature review for this study has been made using Google Scholar. The present study found that number of research works on the weather effects and stock market behavior had increased marginally during the recent time period, especially from the beginning of Twenty First Century. Among the different weather factors, temperature was wildly used for research. Finally, this paper reveals some significant research gap to advance the research agenda for future research.

The paper examined the impact of converging with IFRS, on stock market performance of 12 pharmaceuticals companies listed in S&P BSE 100. The time frame of six years from 2013 to 2018 was selected and Ordinary Least Square (OLS) regression model was used for the analysis. The result revealed that the value relevance of financial statements was high on converging with IFRS. The impact of financial statement variables on Indian Stock Market was significantly positive with reference to PAT. However, all financial variables did show significant association with stock market indicators.

This research paper proposes to analyse the impact of International Financial Reporting Standards (IFRS), on pharmaceutical companies. A graphical representation of its impact on the key performance indicators, after converging with IFRS, is presented in this paper. This study compared the financial ratios, prepared by using IGAAP and IFRS, on the date of transition, i.e. 1st April 2016, using Wilcoxon Signed Ranked Test. The study has exhibited significantly positive impact on most of the ratios under IFRS, when compared to those derived under Indian GAAP.

The present study investigates the impact of temperature, on the returns of the Carbon Index, otherwise called BSE GREENEX in India. The study employed the secondary data of temperature in five sample cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), and the BSE GREENEX in India. Statistical tools like Descriptive Statistics, Unit Root, OLS Regression Test, and Granger Causality Test, were used. This study would be useful for the investors to make green investment in India.

The world of today is high frequency data driven and characterized by the application and use of information technology for better business development and decision making. The price movements of stock markets are mainly influenced by micro and macro economic variables, legal framework and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical or past prices. Achieving the accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behavior in the stock market and volatility in the stock prices. This paper analyses the stochasticity of movement pattern of the most volatile, fifty company stocks (in terms of market capitalization) of NSE-Nifty, using ensemble machine learning method. The findings of the study would help the investors, to make rational and well informed investment decisions, to optimize the stock returns by investing in the most valuable stocks.

Air pollution issue has become an important environmental problem in India. This paper proposes to examine the influence of Delhi Air Pollution on the two Indian stock indices, using Descriptive Statistics, Unit Root, and OLS regression. The analysis of the study found that Delhi Air Pollution did create a statistically significant effect on Nifty. This is the first study of this type to look into the effects of air quality issue on stock market indices in India