Financial Markets and Corporate Finance

We analyze the impact of the business cycle and earnings diversification on the performance, credit risk, and capital buffer of Indian commercial banks spanning from 2008 to 2021. Employing fixed effects panel estimation, the study reveals a significant negative correlation between the capital buffer and the business cycle, while credit risk and bank performance demonstrate positive and significant associations with the business cycle. Consequently, our research supports the claim that the Basel III accords, specifically those addressing countercyclical capital buffer requirements, play a role in enhancing the stability of financial institutions, particularly during economic downturns. However, our findings do not align with the perceived benefits of diversifying into activities that generate non-interest income.

FinTech (financial technology) has disrupted the business model of traditional financial institutions. The implementation of innovative technologies in the financial sector is not new; it emerged in 1967, when the first ATM (automated teller machine) was introduced to the financial sector by Barclay’s Bank in the UK. There are various initiatives taken by the Indian government to promote digitalization, such as Pradhan Mantri Jan Dhan Yojna (PMJDY), Immediate Payment Service (IMPS) and Unified Payment Interface (UPI). FinTech has disrupted the traditional model of the banking sector and continues to create competition to introduce innovative products and services for customers, like FinTech firms are offering. Presently, the FinTech adoption rate in India is 87%, which indicates that Indian customers are expecting and accepting innovative FinTech products and services. The purpose of this paper is to survey the studies done on FinTech and identify the technologies behind the success of FinTech. What are the segments of FinTech in India and their key challenges? For this study, we have collected the most relevant articles from Scopus, Science Direct databases and white papers. The concluding remarks of the study provide insights into FinTech-related aspects from an Indian perspective.

This study analyzes the in-sample forecasting power of four univariate time-series models—Naive, Autoregressive Moving Average (ARMA), Exponential Smoothing, and Autoregressive Fractionally Integrated Moving Average (ARFIMA)—for predicting 5-day returns of Bitcoin, Ether, Tether tokens, and Binance Coin (BNB). Each model's suitability varies due to the unique consensus systems and market dynamics of each cryptocurrency, suggesting no single model is universally optimal. While the Naive model proved unsuitable for most cases, ARMA demonstrated better accuracy for several cryptocurrencies, highlighting the importance of tailored forecasting approaches for diverse cryptocurrencies.

The purpose of this paper is to give theoretical review on liquidity in asset pricing models for predicting expected stock return through bibliometric analysis to provide an overview of current research and future trends in this area. Excel, VOS Viewer, and Biblioshiny evaluated 933 Scopus-indexed journal articles from 2000 to 2022. Clustering documents for bibliographic coupling identifies themes and future scope in this field. The number of articles that have been published has grown over time. Thematic mapping has shed light on the specialized and new themes that are being explored in the area. Four key research streams have been identified in this area. These models must accurately reflect asset risk and return to make informed investing decisions. This research fills the gap in literature by bibliometrically analyzing liquidity factors in asset pricing models and evaluating empirical investigations in the form of empirical reviews to determine future research agendas.

The financial landscape has undergone a seismic shift from traditional floor trading to the digitized realm of screen-based transactions. Despite the foundational assumption in finance literature that investors act rationally, empirical evidence reveals the pervasive influence of behavioral biases on investment decisions. This study focuses on discerning and ranking these biases impacting individual stock investors in India. Among the identified biases, overconfidence, disposition effect, herding bias, and representativeness bias emerge as prominent factors shaping investment choices. Notably, a positive correlation exists between overconfidence and decision-making in investments. The study employed primary data collection through a pilot survey, utilizing random snowball sampling to gather insights. Analytical techniques such as regression analysis and structural equation modeling were leveraged to assess the impact of these biases on Indian investors and explore the influence of socio-demographic factors. Results underscore the prevalence of biases in investment decisions, with variables like age, gender, occupation, annual income, and trading experience exerting significant influence. Moreover, the study underscores the importance of recognizing and addressing these biases to foster more informed and rational investment behavior. By understanding the interplay of socio-demographic factors, investors can tailor their strategies to navigate the market effectively. This research contributes to the theoretical understanding of behavioral finance and offers actionable insights for individuals navigating the complexities of the stock market.

Many of the informed traders are preferring options market for their investment purposes not only for its hedging properties, but also due to the impacts of leveraging effects. Numerous studies have tried to use the derivative market, most especially option contracts for predicting future returns of underlying using informed trader’s behavior exhibited in the market. This study aims to find the efficacy of Call Put Ratio of Option variables, a very commonly used information measure for predicting underlying return. For this, Option volume as well as Open Interest has been taken over the period from January 2022 to January 2023 in the study. It was observed that while literature claims that these two ratios have the predicting capability in terms of individual stock option, the same does not hold true in terms of index option as considered in the study. For the index option, the traders and portfolio manager should not only depend on these two variables while making an informed decision as the relationship established cannot be concluded to be entirely predictive.

Rise in commodity prices as well as price volatility trigger several effects on the macroeconomic state via inflation, supply shortages, uncertainty about real incomes, etc. Studies have shown that crisis-induced price shocks affect the supply chain as a result of changed production patterns. In this study, we analyze the impact of major economic crises since the 1990s on global commodity prices. The four major crises explored in this study are Asian Financial Crisis, Global Financial Crisis, Covid Crisis, and Ukraine-Russia War. We do a comparative study of energy, food, metal, and minerals prices between August 1992 and August 2022, and how these commodity groups behaved through these shocks. The paper shows how each crisis disturbs prices of the same commodity differently, while why some are not affected due to crisis. Factors like seasonality, negative news, and economic disruptions are accounted for. We compare between pre-and post-crisis periods for different time spans, e.g., 5 years, 3 years, and 6 months with monthly data. This gives an idea of how the impact changes over different time spans. Weekly data has been studied separately as a robustness check. Conditional volatility modeling helps to show how the long-term and short-term impacts percolate in the commodity markets. Results show that, in general, prices show fluctuations across the crises. Food, Metal, and Mineral prices soared due to the crises.

Research on fund management aims to ascertain whether fund performance is attributed to fund resources or the skills of the manager. However, most of the earlier research makes assumptions about manager skills based on fund return as the return of the manager who manages multiple funds throughout their career. In this paper, we examine the impact of fund managers’ personal traits, such as age, gender, education, and experience, on their managerial abilities. We compile an extensive dataset of returns attributable to managers by sampling fund managers of Indian origin. This dataset encompasses returns accumulated over the career trajectories of managers, amalgamating performance from the diverse range of funds they have overseen throughout their tenure. The findings demonstrate that manager skills are enhanced by undergraduate technical degrees and CFA certification but are diminished by manager age and experience. This study presents findings on how fund managers’ individual traits in determining manager skills, which remains largely uncharted, particularly when viewed through the lens of emerging markets.

Figuring out the dividend payout ratio is one of the most difficult tasks for financial managers. In addition to serving the company's best interests, it should safeguard the interests of shareholders. Concepts of relevance and irrelevance exist in relation to dividend policies. Dividends are thus still an open matter as of this writing. Research is being conducted on this subject, but no one has a perfect answer for why dividends are important, why corporations pay dividends, or how to solve the dividend puzzle. This study is an attempt to synthesize academic literatures on corporate dividend policy and includes citation, co-citation and co-occurrence analysis, bibliographic coupling, etc. Data were extracted from scopus database for the period of 2000–2021 and analyzed using Vosviewer software. The study starts with an initial search of 780 papers and after applying proper filtering criteria it reduced to 211 papers. Finding of the study shows constant rise in the quantity of papers between 2000 and 2021. The United States ranks first because it has made the most contributions to this field (17 papers), and coupling analysis yields five clusters in this field.

This research aims to provide insights into Corporate social responsibility (CSR) performance and its impact on portfolio performance. The research would contribute to the broader understanding of how investors can achieve financial success and positive societal impact through the CSR performance of financial companies. This study uses 56 financial companies’ data from 2013–2014 to 2021–2022. Seemingly unrelated regression has been used to examine the impact of FAMA and French factors on the return of different portfolios. The findings of this research are significant for Banks and NBFCs, which shows that all the factors of the FAMA and French model are significant in showing the portfolios’ results. This study demonstrates that banks with better CSR performance yield higher expected returns than NBFC portfolios. This finding confirms that increased socially responsible activities yield better returns for banks. It showed that more socially responsible companies provide better financial returns than those not focusing on these issues. This suggests that when companies invest in being responsible and doing good for society, it can lead to better financial results for them and the investors.

Leaders exhibit exceptional behavior and significant expertise. The exploratory study examines the techniques for successful leadership using an exploratory qualitative methodology. A case study on Mr. R. S. Sharma, the former Chairman and Managing Director of a prominent Indian public sector oil and gas business, was conducted using semi-structured interviews. The case study findings disclosed 15 critical success factors for achieving effectiveness in board operations, classified under three resource categories: Physical, Structural/Operational, and Human resources. This research contributes to the existing body of knowledge by investigating the crucial matter of improving board effectiveness through valuable insights from industry professionals.

This study examines the effect of large owners’ identity and shareholding concentration on firm performance. The large owners examined in this study are classified as individuals, institutions, corporates, government, and others. The study uses a balanced panel dataset of non-financial firms listed on the National Stock Exchange (NSE) of India. It comprises 1072 listed firms from 2010 to 2022. The analysis uses the fixed effects method with cluster robust standard errors. The empirical findings indicate varying effects of ownership identities on firm performance. Further, the effect of ownership identity on accounting and market performance differs when considering their respective shareholding concentration. However, the study indicates that when corporates hold the largest ownership, firms report significantly better accounting and market performance than the rest of the ownership identities. The performance of the firms where corporates hold the largest stake tends to increase with a rise in shareholding by the corporate owners.

Zombie firms cause enormous harm to the economy; therefore, it is crucial to understand the phenomenon. The present study identifies zombie firms from a large sample of all non-financial firms in India. It analyses the prevalence of zombie firms over time and their distribution across various industries using a three-digit National Industrial Classification (NIC) classification. Furthermore, this study investigates the causes of the emergence of zombies and evaluates the effectiveness of various re-structuring measures for resolving zombie firms. The study empirically analyses the capital structure composition of zombie firms to understand the mere reason for their formation. Using data for 2000–20, our analysis reveals that around nineteen per cent of Indian firms show the characteristic of zombie firms, and their capital structure is highly skewed toward debt capital-which makes the already loss-making firm more vulnerable as they struggle to service their debt out of the revenue they earn. To a certain extent, the conclusion of the formation of zombie firms would be conducive to adjusting the direction and intensity of further research.

The surge in insolvency filings in India has garnered significant scholarly interest due to the substantial cost associated with financial distress. Firms facing financial downturns have strong incentives to manipulate financial reporting through various earnings management strategies. This study aims to assess the extent of earnings management in financially distressed companies, identify incentives for such practices, and explore the trade-off between accrual and real earnings management. Using the data from NSE-listed firms and those undergoing insolvency from 2008 to 2022, the findings indicate a prevalence of real earnings management over accrual earnings management among distressed firms, with overproduction and sales manipulations being favored over cutting discretionary expenses. Further, the study also underscores that the choice between accrual and real earnings management is influenced by the relative cost of each strategy. The findings offer valuable insights for regulators and auditors, shedding light on the earnings manipulation strategies employed by distressed firms and contributing to enhanced financial reporting quality.

This exploratory study investigates how emerging technologies impact the competitive landscape across industries, focusing mainly on Micro, Small, and Medium-Sized Enterprises (MSMEs) and Large Enterprises (LEs). Using data from five focus group discussions with academicians from management and technology, the researchers explore how MSMEs and LEs can leverage emerging technologies to gain a competitive edge while mitigating the existing disparity between them. The study predicts a significant impact of emerging technologies across various sectors, but the most significant impact was found in the healthcare sector. While the overall benefits are anticipated, some industries may also encounter challenges. In both the public and private sectors, these technologies can enhance service delivery, reduce costs, boost social inclusion, and drive innovation. For MSMEs, automation and operational streamlining through emerging technologies offer cost reduction and improved efficiency. LEs can leverage them for personalized offerings and exceptional customer service to stand out. This study further highlights the potential for MSMEs to bridge the disparity with LEs through cost reduction and productivity gains. Recommendations for organizations include formulating a technology strategy, investing in training and development, fostering partnerships, and implementing robust cybersecurity protocols to navigate the evolving landscape of emerging technologies.

The Indian utility sector is currently experiencing a phase of disinvestment, driven by various factors like advancements in technology, the ever-changing landscape of global energy markets, shifts in governmental policies, and evolving consumer preferences. In light of these circumstances, it becomes crucial to assess the utility sector’s condition and its existing efficiency level. The present study primarily examines the repercussions of disinvestment on the technical efficiency of the state-owned utility sector enterprises in India during the period spanning from 2003 to 2023. To gauge the technical efficiency, the study employs the stochastic frontier analysis (SFA). The study also investigates how factors such as the size of the firm, its age, and the disinvestment process influence inefficiencies. The findings of the study indicate that among the considered factors, the size of the firm holds the most substantial sway on inefficiency levels, while the age of the firm and the occurrence of disinvestment do not exhibit a significant impact. On average, the utility sector enterprises within the sample demonstrated a technical efficiency of around forty-five percent. This statistic underscores the need for more comprehensive and profound structural reforms to enhance economic outcomes in the sector.

Mobile payment services have emerged as an alternative to cash payments and have become very popular among the youth because of their convenience and ubiquity. The revolution in communication and technology has motivated the youth to perform digital payment transactions through smartphones. However, the youth’s intention to continue the use of smartphone-based payment services (SBPS) is determined by various factors. Customer satisfaction is one of the important factors as it sustains the use of any product or innovation. Therefore, the present study intends to analyse the mediating effect of satisfaction on the relationship between continuance intention to use SBPS and its determinants. Primary data collected from 683 young SBPS users were analysed using PLS-SEM. The findings revealed that satisfaction positively mediates the relationship between trust, confirmation, social influence, facilitating conditions, habit and continuance intention to use SBPS. As customer satisfaction positively influences the continuance intention to use SBPS, the service providers must ensure their customers’ satisfaction for long-term survival in the ever-changing market environment.