Financial Sector Research Articles

Dynamic spillovers of various uncertainties to Russian financial stress: Evidence from quantile dependency and frequency connectedness approaches

Being geopolitically exposed, the Russian financial sector is vulnerable to various ­uncertainties. The aim of the article is to examine the quantile movements and dynamic connectedness of uncertainty indices with the financial stress index of Russia employing the cross-quantilogram (CQ), recursive cross-quantilogram (R-CQ) and TVP‑VAR dynamic connectedness using monthly data from July 2011 to August 2023. It is found that for Tweeter-based Economic Uncertainty (TEU) and Global Economic Policy Uncertainty (GEPU), there is strong positive dependence on the Russian Financial Stress Index (RFSI) in the bearish states of market in the initial memory, and the strength of this positive spillover effect gradually wilts towards longer memory structures. Unlike the GEPU, Russian Economic Policy Uncertainty (REPU) has long-lasting heterogeneous spillover effects on RFSI. Though there are significant positive, as well as negative, spillover ­effects of Global Geopolitical Risk (GGPR) on RFSI in the initial memory, across the longer­ memory structures these entire heterogeneous effects wash out. However, Russian Geopolitical Risk (RGPR) have long-lasting heterogeneous spillover effects on RFSI, unlike GGPR. GEPU, GGPR and RGPR were the net transmitters while RFSI, TEU and REPU were net receivers of volatility shocks. Since RFSI shows resilience over the long-term horizon to global geopolitics and economic uncertainty, investors are advised to keep patience and hold their capital/investment up to a minimum of 1 year in Russian financial system in order to be rewarded with positive returns.

Financial Development and Foreign Direct Investment Impact on Economic Growth: Evidence from Nepal

The interplay among financial development, foreign direct investment (FDI), and economic growth has been a subject of keen interest among scholars, both from a theoretical standpoint and through empirical research. Focusing on Nepal, this study examines the distinctive connections among economic expansion, the influx of foreign capital, and the evolution of the financial sector. Given the lack of a definitive metric for financial development, this research adopts the financial development index, along with the financial institution index and financial market index, as alternative indicators. It also considers additional variables that could influence economic growth beyond financial development and FDI. Utilizing the Autoregressive Distributed Lag (ARDL) bounds testing method and the Error Correction Model (ECM), the analysis uncovers a robust positive link between financial development and FDI in enhancing Nepal's economic output. Furthermore, both the short-term and long-term analyses prove to be reliable, devoid of issues like heteroscedasticity, autocorrelation, and multicollinearity. Importantly, the findings indicate that in the face of short-term disruptions, the dynamics between these variables adjust back to a long-term equilibrium state.

Simulation-Based Evaluation of Advanced Threat Detection and Response in Financial Industry Networks Using Zero Trust and Blockchain Technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

Advancing Financial Resilience: A Systematic Review of Default Prediction Models and Future Directions in Credit Risk Management

This research presents a systematic review of a substantial body of high-quality research articles on Default Prediction Models published from 2015 to 2024. It is a comprehensive analysis of a DPM wide spectrum approaches including Textual Models, Systematic Review Studies, Hybrid Models, Intelligent Models and Statistical Models. The reason behind this study is rooted in the critical need to mitigate and understand the credit default risk that poses a significant threat to financial stability worldwide. By employing an evidence-based approach and methodological rigorously, this research critically evaluates the gaps, effectiveness and evolution in existing DPM methodologies. It is not only synthesized the current landscape of DPM study but also identified the direction for the future research, by offering novel insights and bridging theoretical gaps for enhancing the strategies of credit risk management. This study stands out by focusing on high citation research from top tier publishers, ensuring the quality and relevance of its analysis. The findings of this study have profound implications for stakeholders across the financial sector, including bankers, investors, regulatory bodies, and researchers. It aims to advance financial stability by providing a comprehensive overview of DPM advancements and pointing towards areas that require further exploration. By doing so, it contributes significantly to the development of more effective and sophisticated DPM strategies, thereby enhancing the robustness of financial institutions against potential defaults.

Saving Honest Entrepreneurs through the Second Chance Policy in Ghana

This paper analyses the existing legal frameworks on insolvency in Ghana and how they sufficiently provide to save honest entrepreneurs during financial reforms. Financial sector reforms are a welcomed mechanism to foster financial sector efficiency for sustainable growth. During the period of decision-making in financial sector reforms, it is expected that some business concerns would have their lifespan terminated. Notwithstanding, recent episodes of financial deregulation have revealed unintended consequences of demobilising the sector which has affected innocent entrepreneurs. The aftermath of the financial sector reforms has passed the Corporate Restructuring and Insolvency Act, 2020 (Act 1015). This paper aims to address the issue of whether or not the existing legal framework benchmarked against the Capital Adequacy, Asset quality, Management, Earnings, Liquidity (CAMEL) framework absolves honest entrepreneurs in such a period as against fraudulent entrepreneurs. The paper adopted the doctrinal legal research approach using distinct research tools including data from primary sources, statutes, journal articles, online resources and other obligatory tools. It finds that recent financial sector reforms did not segregate the fraudulent entrepreneur from honest entrepreneurs to make room for the CAMEL to save the latter. It concludes on how a second chance policy can be developed along with existing statutes to settle and save honest businesses in periods of economic volatility.

Fueling the bottom line: Decoding the effects of oil on banking performance in net oil-importing economies

Banks play a pivotal role in the financial sector, assuming critical functions such as facilitating the monetary policy transmission mechanism and acting as intermediaries between savers and borrowers. Meanwhile, oil represents a fundamental input for economic activities, and its inherent volatility serves as a significant catalyst for economic instability. Given the critical roles of banking institutions and oil in the economy, their relationship garners attention from various stakeholders. The growing body of literature has examined the nexus between oil and banking performance. However, existing research has predominantly concentrated on either oil-exporting jurisdictions or country-specific analyses. Hence, the present study endeavors to bridge this gap in the literature by investigating the intricate dynamics between oil and banking performance, specifically in net oil-importing countries. The dynamic panel method is employed. The findings indicate that oil has direct and indirect effects on the profitability of banks operating through transmission channels.

Natural language processing in finance: A survey

This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) ESG (Environmental, Social, Governance) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.

Finance Modeling Approach Using Machine Learning

The integration of machine learning (ML) into financial modeling represents a transformative shift in the finance sector, enhancing analytical capabilities and decision-making processes. This article explores the fundamental concepts of financial modeling and machine learning, illustrating how ML methodologies—both supervised and unsupervised—improve traditional financial models by enabling the analysis of extensive datasets to uncover intricate patterns. By leveraging historical data, ML enhances predictive accuracy in various applications, including risk assessment, algorithmic trading, credit scoring, and fraud detection. The article highlights significant advancements in predictive analytics through techniques such as regression analysis and time series forecasting, which allow financial analysts to navigate market uncertainties more effectively. Moreover, the automation of data processing and feature engineering through ML leads to increased operational efficiency, reducing human error and enhancing real-time decision-making capabilities. However, challenges such as data quality issues, model interpretability, and ethical considerations regarding algorithmic bias necessitate careful management to ensure responsible implementation. The article emphasizes the importance of transparency in ML models to foster trust among stakeholders while meeting regulatory requirements. Future research directions are proposed, focusing on the integration of explainable AI with blockchain technology and the implications of big data analytics on ML models. By addressing these emerging challenges and opportunities, finance professionals can leverage advanced analytical tools to drive innovation and enhance strategic initiatives in an increasingly complex financial landscape. Ultimately, this article underscores the pivotal role of machine learning in reshaping financial modeling practices and its potential to redefine the future of finance.

Determining the leaders of Ukraine’s insurance market based on the adaptation of the DEA method

The escalating wartime risks in Ukraine has led to a rapid reduction in insurance coverage in life and non-life segments. The purpose of the study is to rank Ukraine’s insurance companies based on an adaptation of the DEA method to the insurance market conditions. The study utilized the Supervisory Statistics of the National Bank of Ukraine as data. The study also used ranking of insurance companies based on technical efficiency criteria. The output indicators include the profitability of total capital (Output1) and the occupied share of the insurance market (Output2). The input indicators comprise the volume of total assets of insurers (Input1), the share of equity capital in assets (Input2), the level of gross payments (Input3), and the level of payments to insurance reserves (Input4). The ranking of insurance companies is based on minimizing the distance of each from the bounds of technical efficiency by solving a set of optimization problems. Based on the modeling results, a list of market leaders (7 companies) was formed for the end of the third quarter of 2023. They served nearly 37% of the insurance market in Ukraine, accumulating 41.5% of the total assets of this financial market sector. So, the primary issue for insurance companies is the loss of solvency due to the absence of adequate levels of insurance reserves. Therefore, priority measures should include strengthening regulatory constraints in this financial market segment to facilitate its qualitative renewal.

Unveiling asymmetric return spillovers with portfolio implications among Indian stock sectors during Covid-19 pandemic

This paper aims to provide a systematic inquiry into the return spillover dynamics between a network of Indian sectoral indices during the pre- and post-pandemic periods. To analyze the same, this paper uses the asymmetric time-varying parameter vector autoregressions (TVP-VAR) framework. Furthermore, in the spirit of Broadstock et al. (2020), we perform dynamic portfolio exercises based on common hedging techniques and the minimum connectedness portfolio approach to determine what better captures asymmetry. Our daily dataset includes 12 sectoral stocks spanning from January 01, 2017, to May 5, 2023. The findings reveal that negative connectedness dominates throughout the sample period, demonstrating that profit-maximizing agents and risk-averse investors are more likely to react negatively to news. We also show that in the network, the average net transmitters are the banking and other financial service sectors, whereas the net receivers are the information technology, pharmaceutical, and fast-moving consumer goods sectors throughout the period under consideration. Our results show that the minimum connectedness portfolio (MCoP) approach is a very useful method based on Sharpe ratios, as it is either the first or second most profitable among these three competing methods. These results, therefore, yield valuable insights for policymakers and investors

The bright side of the doom loop: Banks’ sovereign exposure and default incentives

The feedback loop between sovereign and financial sector insolvency, termed the “doom loop”, was a key driver of the European debt crisis and motivated an array of policy proposals. This paper revisits the “doom loop” focusing on governments’ incentives to default. We introduce a 3-period model with strategic sovereign default, where debt is held by both domestic banks and foreign investors. The government maximizes domestic welfare; thus, the temptation to default increases with externally-held debt. Importantly, the cost of default arises endogenously from the damage that default causes to domestic banks’ balance sheets. Domestically-held debt thus serves as a commitment device for the government. We show that two prominent policy prescriptions – lower exposure of domestic banks to domestic sovereign debt or a commitment not to bailout banks – can backfire, since default incentives depend not only on the quantity of debt, but also on who holds it. Conversely, allowing domestic banks to buy additional domestic sovereign debt during times of sovereign distress can avert the doom loop. In the context of a monetary union similar unintended negative consequences may arise from a backstop by the central bank (e.g., the ECB’s Transmission Protection Instrument) if imprecisely calibrated or the pooling of debt (e.g., European safe bond, also known as ESBies).

The Impact of Mobile Money on Perception about Cash and Financial Inclusion: Micro-Level Evidence from Uganda

The debate about the future of cash has recently intensified as technology innovations in the financial sector accelerate digital payments and the creation of new, digital forms of money. Several countries have embraced initiatives towards cashless or cash-light payments. In Sub-Saharan Africa (SSA), mobile money has dominated the list of digital payment innovations. These innovations hold the potential to alter individuals’ behaviour towards cash. Will mobile money render cash less dominant in Africa? Can it promote financial inclusion? We address these questions by exploring individual-level data for Uganda, a country in an African region that pioneered the mobile money revolution in the world. We use the Propensity Score Matching method to robustly compare mobile money users and non-users across various indicators that capture individuals’ perceptions about cash, and the extent to which they remit, save, and borrow money. We present the first evidence that mobile money users, compared to non-users, are more likely to perceive cash as risky and less likely to prefer carrying large amounts of cash. We also confirm that mobile money users are more likely to receive and send remittances, save, and borrow. They also save and borrow larger amounts. The estimation methodology used in the paper, robustness analysis, and a falsification test help reduce potential concerns about selection bias and reverse causality. Two implications emerge from the findings. First, the rapid expansion of fintech technologies in Africa is likely to reduce, although slowly, the demand for and usage of cash over time. Second, the expansion of fintech technologies could help promote financial inclusion in Africa

The Mediating Effect of Information Technology Usage on the Relationship of Internal Audit Processes and Internal Auditor Competence towards Internal Audit Effectiveness: Evidence from the Chinese Financial Sector

The effectiveness of internal auditing is determined by various factors, such as the independence of the internal audit function, auditors’ competence, effective audit planning, efficient audit processes, and support from senior management. The digitalization and informatization of modern enterprises have transformed the way companies are managed and have similarly impacted internal audit practices. Informatization not only alters traditional internal audit processes but also changes auditors’ ways of working and thinking. From the perspective of resource-based theory, the effective allocation and full utilization of human, technological and organizational resources can enhance the effectiveness of internal audit. Therefore, the purpose of this study is to examine the impact of internal audit processes (IAP) and internal auditor competence (IAC) on internal audit effectiveness (IAE), as well as the mediating effect of information technology usage (ITU) on the relationship between IAP and IAC and IAE. To test the proposed hypotheses, a questionnaire survey was conducted within Chinese financial enterprises, yielding a total of 133 valid responses. Statistical data analysis was performed using the partial least squares structural equation modelling (SEM-PLS) method. The results indicate that both IAP and IAC have a significant impact on IAE and ITU. Furthermore, ITU serves as a mediating factor, enhancing the positive contributions of IAP and IAC to IAE.

Is digitalization essential for abating carbon emission growth in South Asia?

Output generation processes across South Asia are deemed unclean (highly emission-intensive) due to excessive reliance on fossil fuels. Thus, decarbonizing the growth processes within this region has critical emphasis among policymakers. However, since the South Asian countries are yet to be ready to undergo the renewable energy transition, it is unlikely that “their annual carbon discharge levels will subside” anytime soon. Under such circumstances, these countries need to limit the rate at which their carbon emission levels rise each year. Therefore, the impacts of digitalization on annual carbon emission growth rates in selected South Asian countries are assessed in this study. Overall, considering internet penetration rate as a proxy of digital technology adoption, the results confirm that policies aimed at digitalizing the concerned South Asian economies not only exert emission growth rate-reducing impacts via a direct channel, but also indirectly reduce emission growth rates by making natural resources sectors more productive, expediting the energy sector greening processes, greening the financial sectors, making corruption controlling measures within institutions more effective, and limiting urbanization-induced environmental quality-worsening effects. Therefore, it is relevant for the South Asian government to emphasize on digitalization while simultaneously ensuring that green digital technologies are adopted within the major macroeconomic sectors across this region.

Clean innovation, heterogeneous financing costs, and the optimal climate policy mix

Access to finance is a major barrier to clean innovation. We incorporate a financial sector in a directed technological change model, where research firms working on different technologies raise funding from financial intermediaries at potentially different costs. We show that, in addition to a rising carbon tax and a generous but short-lived clean research subsidy, optimal climate policies include a clean finance subsidy directly aimed at reducing the financing cost differential across technologies. The presence of an endogenous financing experience effect induces stronger mitigation efforts in the short-term to accelerate the convergence of heterogeneous financing costs. This is achieved primarily through a carbon price premium of 39% in 2025, relative to a case with no financing costs.

Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

The rapid growth in FinTech is attracting significant investor attention. There are concerns that FinTech stocks may be a source of systemic risk and this has implications for the stability of the financial sector. This study uses a TVP-VAR model to estimate the dynamic connectedness between global FinTech stocks and the stocks of large US banks, regional US banks, US insurance, and global private equity. The findings reveal that FinTech and insurance stocks are net receivers of shocks while large US banks are a major source of shocks. Thus, FinTech stocks are not a source of systemic risk but can be affected by financial contagion and systemic risk originating from large US banks. The highest connectedness was observed at the onset of the COVID-19 lockdowns illustrating the dramatic impact that a health pandemic can have on systemic risk. Systemic risk also increased during the 2023 US bank panic. Connectedness has implications for constructing investment portfolios. The equally weighted, risk parity, and risk parity connectedness portfolios have similar portfolio summary statistics, but lower risk adjusted returns than the maximum Sharpe ratio portfolio which better captures the low correlation between FinTech stocks and traditional financial stocks. These results are robust across various portfolio rebalancing periods (daily, weekly, monthly).

PENGARUH KINERJA KEUANGAN DAN KEPEMILIKAN MANAJERIALTERHADAP MANAJEMEN LABA DIINTERVENING ASIMETRI INFORMASI

Earnings management is an activity carried out by management by interfering in the preparation of a company's financial statements for the benefit of the company or individuals. The practice of earnings management can be detected using the financial performance approach, managerial ownership, and information asymmetry. This study aims to examine the effect of managerial ownership, information asymmetry, and profitability on earnings management carried out by companies. This study took the research population from financial sector companies listed on the Indonesia Stock Exchange for the 2019-2021 period. The type of data used in this study is secondary data in the form of company financial reports that are used as samples. The research method used in this study is a quantitative research method. The sample was selected using the purposive sampling method. For hypothesis testing, this study uses multiple linear regression analysis. Based on the expected research results in this study are: (1) Financial performance has a significant effect on earnings management; (2) Managerial ownership does not have a significant effect on earnings management; (3) Financial performance has a significant effect on information asymmetry; (4) Managerial ownership does not have a significant effect on information asymmetry; (5) Information asymmetry has an effect on earnings management; (6) Financial performance has an indirect effect on earnings management practices mediated by information asymmetry; (7) Managerial ownership has an indirect effect on earnings management practices mediated by information asymmetry

HARVESTING INSIGHTS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING AGRICULTURAL FINANCE

The intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.

The (in)effectiveness of financial consumer protection: Quasi-experimental evidence from consumer finance in China

The booming consumer finance sector has grappled with internal and external challenges, which call forth strong regulatory responses to financial consumer protection (FCP). Existing studies, however, have explored the economic consequences of FCP from a macro or meso perspective and failed to evaluate the overall effectiveness of the FCP framework. To remedy this, we collected a novel dataset from an online complaint platform in China, which included over 12,000 real-world complaints from March 2019 to September 2022. Built on the dataset, we employ a regression discontinuity in time approach to examine the effect of a representative FCP policy (i.e., Implementation Measures of the People's Bank of China for Protecting Financial Consumers' Rights and Interests) on the average rating of the consumers' perception of dispute resolution. We confirm the effectiveness of the FCP policy in improving dispute resolution, and the results remain robust for a series of tests. Moreover, the effectiveness of the FCP policy varies in different aspects of perceptions and complaint topics. The heterogeneous analysis demonstrates that first-mover and foreign-shareholding consumer finance companies are more responsive to the Measure. Finally, regulatory punishment and media attention are two channels in which FCP policy benefits consumers' perception of dispute resolution. We further discuss some important policy and managerial implications.

An Empirical Assessment of India’s Position in Global Sustainable Bond Market

This study aims to examine India's position in the global green bond market and identify areas for improvement to develop its sustainable finance sector. Analysing data from the Luxembourg Green Exchange (1999-2024) and Indian market sources, including Securities and Exchange Board of India, National Stock Exchange, and Climate Bond Initiatives. Findings reveal that green bonds dominate the global market, with the Luxembourg Green Exchange showing the highest variability in bond coupons and maturity durations. In India, the private sector holds a significant 84% market share, with companies issuing various sustainable bonds. However, challenges persist, including lack of standardisation, limited investor awareness, and inadequate disclosure and reporting standards. Research findings have implications for policymakers, investors, and issuers, emphasising the importance of a supportive ecosystem for sustainable finance to flourish in India. Addressing the challenges and leveraging opportunities can help India unlock the full potential of its green bond market and contribute to global sustainable finance efforts. This study contributes to understanding India's green bond market and its potential for growth, highlighting the need for increased standardisation, transparency, and alignment with national and international best practices to develop India's sustainable finance sector.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.