Search Results (2,334)

The current study investigates the impact of the liquidity coverage ratio (LCR) on the efficiency of Indian banks for the period 2010 to 2019. The study examines the effect of internal bank elements like ownership structure, transparency and disclosure, and technological advancement on the relationship between the LCR and efficiency. Bank efficiency proxied as technical efficiency is evaluated by applying the data envelope analysis approach. Applying the panel data regression technique, the authors discover that the LCR has a positive impact on the technical efficiency at a constant return to scale of banks. The relationship between the LCR and the technical efficiency at a variable return to scale is non-linear. Initially, as liquidity increases, the efficiency of banks improves, after reaching its optimum level, efficiency starts to decline. Furthermore, liquidity tends to improve efficiency of banks with higher promoter stakes, whereas opposing results are evidenced for institutional investors and technological advancement. Full article

This study employs a Monte Carlo simulation to see whether accounting identity problems are present in the Fazzari, Hubbard, and Petersen model (1988). The Monte Carlo simulation generates 50,000 random cash flows, Tobin’s Q, and error term variables, which shape an investment variable that is dependent on them. Cash flows and investments are linked by a partial accounting identity, also known as an accounting semi-identity (ASI). An accounting identity is, for example, an equality between the left and right sides of a balance sheet. An ASI is not a complete one since one or more components of the accounting identity are missing. The estimated coefficients of an ASI do not represent reality, according to the OLS estimations. The regression tells us less about causality the closer the data are to the accounting identity. This is the first time that the biases of OLS estimations in an ASI-based model have been demonstrated. Full article

The current study explores the problem of illiquidity in the corporate bond market globally and proposes a solution to enhance liquidity by studying various dimensions of liquidity. Purpose: The purpose of this paper is to propose a solution to the global issue of illiquidity in the corporate bond market. The problem has been identified by many researchers and this paper attempts to find a viable solution in the “fungibility route” as an alternative to the “liquidity route”. Design/Methodology: An analysis of a sample size of 234,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is performed to identify the problem. Findings/Solution proposed: A mathematical model based on the credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be exchanged with a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated the risk-adjusted yield (RAY) using the modified duration and a credit risk differential. Originality: This research is a pioneering effort in addressing the worldwide issue of corporate bond illiquidity by proposing a novel solution. The proposed strategy aims to improve the liquidity of the bond indirectly, by utilizing the fungibility route. Full article

This paper proposes an optimal trading algorithm based on a novel application of conventional control engineering (CE). We consider a fundamental CE concept, namely, the feedback control, and apply it to algorithmic trading (AT). The concrete feedback control strategy is designed in a form of the celebrated proportional–integral–derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realization of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above enables the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in financial engineering, namely, the “stock market energy” concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python-based prototype software. We finally apply the corresponding prototype software to a data set from the Binance BTC/USDT (Bitcoin/Tether) stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT. Full article

The intricate relationship between financial fraud and credit risk, and their combined impact on banking stability, is a vital and under-researched aspect of financial system integrity. To fill this knowledge gap, this study embarked on a thorough bibliometric analysis of the field, utilizing 2790 documents from various sources, including 1853 articles, 504 books, and 177 reviews, spanning the years 1990 to 2023. Utilizing advanced tools, like Biblioshiny and VOSviewer, this study illuminated key geographical, thematic, and intellectual trends, shedding light on an annual growth rate of 13.43% in the related literature and an average citation per document of 28.29. This detailed analysis offered valuable insights into the current research landscape, emphasizing areas such as author collaboration, with 20.32% international co-authorships, and the prevalence of single-authored documents, at 1100. Despite the existing body of research, the interconnected dynamics between financial fraud and credit risk and their implications for banking stability remain underexplored. Therefore, this study sought to unravel this complex relationship and examine its effects at both the micro (individual banks) and macro (banking sector and wider economy) levels. The findings carry significant practical implications, informing policy development, shaping risk management strategies, and contributing to regulatory measures. Despite its limitations, including the potential transformation of identified trends due to evolving financial systems and financial crimes, this study represents a significant contribution to scholarly discourse in the field. It lays the groundwork for future research and facilitates a more secure and resilient banking sector, reflecting the data-driven insights obtained from the research. Full article

The study presents the economic, managerial, and societal perspectives on corporate social responsibility (CSR) as a basis for adding value to enterprises. It investigates the interplay between the digitalisation of activities and the management of Bulgarian financial enterprises, with a focus on HRM and CSR initiatives in a pandemic situation. The study tests the hypothesis that, in pandemic conditions, the CSR of Bulgarian financial enterprises is positively correlated with the digitalisation of general and human resource management. To assess the level of engagement of financial enterprises with CSR causes during the pandemic, the study employs a methodology comprising descriptive statistics and ordinal regression. The main conclusion, based on a nationally representative survey of Bulgarian financial enterprises, is that banks and insurers that heavily invest in digitalisation have demonstrated a higher level of commitment to CSR causes during the COVID-19 pandemic, while conservative and less digitally advanced financial enterprises have had limited CSR activity. By adopting fintech and insurtech solutions directed towards societal needs, market demands, and customer satisfaction, financial enterprises increase their efficiency. Our analysis confirms the interplay between the digitalisation of financial enterprises and support for CSR causes. Full article

Management with high organization capital, which can be seen as an indicator of superior internal governance, can be expected to exhibit a preference for cash reserves to safeguard assets and mitigate the risk of underinvestment. However, external parties may see high cash reserves as a risk factor for the agency problem. Strong external governance can mitigate the preference of management with high organization capital for cash reserves. The empirical analyses show a positive association between the organization capital of U.S.-listed firms and their cash reserves when controlling for multiple variables. Furthermore, through employing the hostile takeover threat index, I reveal the disciplinary effects of strong external governance. This study adds to the existing literature investigating corporate governance that is useful in corporate decision making. Full article

The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights. Full article

During the last half century, the concept of shared ledger systems that offer a single source of truth has challenged traditional bookkeeping, leading to innovations such as the resource-event-agent (REA) accounting framework, triple-entry accounting (TEA), and blockchain. Despite these advancements, the historical development of shared ledger systems remains under-researched and unclear, with the influence of REA on TEA particularly overlooked. This study aims to fill this gap by conducting a genealogical analysis of shared ledger systems, with a focus on tracing the development of TEA and its historical byproduct of the REA framework designed by McCarthy. Through a comprehensive literature review and interviews with pioneers in REA, TEA, and blockchain, we uncover the missing link between REA and TEA. Our findings suggest that the current explosion of shared ledger systems results from the convergence of three parallel research streams, occasionally interacting with each other. We correct common misconceptions, acknowledge the influence of key individuals, and map out the overlapping paths of REA, TEA, and blockchain. By elucidating the historical evolution of shared ledger systems, this study contributes to the academic debate and fosters further discourse among researchers in REA, TEA, and blockchain, thereby enhancing the potential applications within these fields. Full article

Today most insurance is sold by over a million brokers and independent agents acting as intermediaries between the insurance companies and their customers. Digitalization and changing customer behavior have fostered the development of insurtech businesses, and, more recently, multi-sided platforms are emerging as new market forms for insurance intermediation. This paper aims to provide a better understanding of how the emergence of the platform economy, with a market dominated by multi-sided platforms, will potentially impact insurance intermediation in the future. Using inductive content analysis on the results of a systematic literature review of the body of research on insurance intermediation, we identify the key functional roles fulfilled by insurance intermediaries. Applying these roles to a literature review on multi-sided platforms allows us to compare how different market forms and players embody the functional roles of intermediaries. Our findings suggest that multi-sided platforms are better able to perform certain roles in terms of agility, scale and scope, and we discuss the future role of platforms in insurance intermediation. Full article

The global economy and the financial sector have suffered due to the COVID-19 epidemic. The banking industry has seen an increase in digital channels and payments, consumer behavior changes, regulatory and supervisory relief, and new operational resilience challenges due to the COVID-19 pandemic. Therefore, seniors have had to adopt new channels and technologies instead of traditional cash and traditional channels. However, older people in India are not tech-savvy and avoid e-banking. Thus, stakeholders (e.g., bank authorities, governments) must focus on variables affecting the older population’s use of e-banking to reduce financial isolation. Thus, this study uses an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework to examine senior citizens’ intentions to use e-banking. Data from “456” senior citizens from central India were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques. The study identified different significant predictors (e.g., performance expectancy, effort expectancy, perceived risk, self-efficacy, perceived trust, and anxiety) of older users’ intention to use e-banking post-COVID-19. This is the first study from central India to determine elderly people’s intention to use online banking during and after the COVID-19 pandemic. The findings will help bank authorities and other stakeholders increase senior citizens’ financial inclusion in India. Full article

The International Financial Reporting Standards (IFRS) do not make explicit provisions, in terms of a specifically dedicated standard, for the accounting treatment of cryptocurrencies. This creates uncertainty, and guidance is therefore required in terms of accounting for such investments. Accounting theory has the potential to provide the foundation for this guidance. This study aimed to determine the most appropriate accounting treatment for cryptocurrencies based on the International Accounting Standards Board’s (IASB) Conceptual Framework for Financial Reporting (as a form of accounting theory) that results in decision-useful information. The research further investigated the proposed accounting treatment in terms of IFRS and sought to determine whether this treatment was aligned with the IASB’s conceptual framework. This qualitative study conducted a non-empirical interpretative analysis of the literature (focusing specifically on accounting theory) to address the research aim. The conceptual framework indicated that the most appropriate way to account for cryptocurrencies was to recognise an asset at fair value. This accounting treatment aligns with accounting for assets under International Accounting Standard (IAS) 2 commodities held by broker-traders and the IAS 38 revaluation model. Addressing the problem of accounting for cryptocurrencies with reference to accounting theory makes this study novel. The guidance provided could reduce uncertainty among entities holding investments in cryptocurrencies and could increase the decision-usefulness of financial information. Full article

Regulations related to the disclosure of environmental, governance, and social (ESG) factors are evolving rapidly and are a major concern for financial compliance worldwide. Information technology has the potential to reduce the effort and cost of ESG disclosure compliance. However, comprehensive and accurate ESG data are necessary for disclosures. Currently, the availability and quality of underlying data for ESG disclosures vary widely and are often deficient. The process involved with obtaining ESG data is also often inefficient and prone to error. This paper compares the models used and the evolution of Fintech data infrastructure developed to support financial services with the requirements and trajectory of ESG disclosure compliance. Based on existing Fintech models, it presents a sustainability data infrastructure framework that aims to address current ESG data challenges, including data governance concerns, on a large scale. In conclusion, it highlights key considerations and recommendations for policymakers. Full article

The purpose of this paper is to study the generalized method of moments (GMM) estimation procedures of the realized stochastic volatility model; we give the moment conditions for this model and then obtain the estimation of parameters. Then, we apply these moment conditions to the realized stochastic volatility model to improve the volatility prediction effect. This paper selects the Shanghai Composite Index (SSE) as the original data of model research and completes the volatility prediction under a realized stochastic volatility model. Markov chain Monte Carlo (MCMC) estimation and quasi-maximum likelihood (QML) estimation are applied to the parameter estimation of the realized stochastic volatility model to compare with the GMM method. And the volatility prediction accuracy of these three different methods is compared. The results of empirical research show that the effect of model prediction using the parameters obtained by the GMM method is close to that of the MCMC method, and the effect is obviously better than that of the quasi-maximum likelihood estimation method. Full article

This paper contributes to the advancement of noise trader theory by examining the connection between aggregate news sentiment and stock market returns during days of significant stock market movement. In contrast to previous studies that solely focused on company-specific news sentiment, this research explores the impact of aggregate news sentiment. To draw conclusions, GARCH modeling, regression analysis, and dictionary-based sentiment analysis are employed. The findings, based on data from India, reveal that aggregate news sentiment has a short-lived influence, with notable effects stemming from the business and politics categories. Full article