This study tries to assess whether aggregated domestic and global macro-financial conditions can assist to explicate daily equity-market returns and volatility in India and considers the implications for financial risk and project-related decision making. Daily BSE and NSE index returns are analysed for January 2000–March 2024 using principal component analysis (PCA), EGARCH and FIEGARCH specifications, with event-period indicators for the Global Financial Crisis, demonetization, COVID-19 and the Russia–Ukraine war. The PCA results point out that the retained components summarize a large share of the common variation in the underlying macro-financial variables. In the conditional-mean equations, the domestic and global PCA factors are usually statistically insignificant, whereas market uncertainty measured by India VIX is significant in selected specifications. Event-period indicators divulge strong conditional-mean effects for the Global Financial Crisis and COVID-19, while demonetization is associated with a negative return effect. In the variance equations, the models identify substantial conditional heteroskedasticity and asymmetric volatility. The Student-t EGARCH specifications provide lower in-sample AIC values than the normal EGARCH and FIEGARCH alternatives. FIEGARCH estimates indicate statistically significant fractional integration, with a larger estimated long-memory parameter for BSE than NSE; however, the available model-comparison evidence does not establish that fractional persistence is superior to asymmetric short-memory modelling. The findings suggest that financial project managers, treasury functions and investment decision-makers should treat market uncertainty and crisis regimes as central risk-management inputs, while avoiding the assumption that low-frequency macroeconomic indicators have an immediate daily effect on equity risk.
Stock markets and stocks have been around for centuries. For investors, forecasting changes in the price of stocks has long been an aim. Using computer tools for information systems can lead to better and more accurate results. Machine learning uses machines to mimic human thinking and habits. This helps in making predictions and decisions based on data. Today, machine learning is common in areas like face recognition, investment advice, and natural language processing. This study proposes a robust framework for stock trading signal classification within the Indian equity market using a Multivariate Random Forest (MRF) approach. Separate Random Forest classifiers are designed to capture the unique market traits of each company. Bootstrap sampling and random feature selection help the ensemble model to find intricate relationships among various technical variables. This method reduces the variance that comes with individual decision trees. By leveraging the ensemble learning capabilities of Random Forest, the model effectively mitigates the risks of overfitting while managing the high-dimensional feature space typical of financial time series. The Multivariate Random Forest-based stock market model offers a more reliable and practical way to classify stock market signals. The model aids in making smarter, data-driven choices for portfolio management and key market drivers.
Building Information Modeling (BIM)-based cost estimation has moved beyond automated quantity take-off toward workflows that combine structured cost data, open data exchange, semantic models, machine learning, provenance, and decision support. This study maps that transition and identifies the reliability gap that separates automated calculation from defensible cost decisions. Three targeted Scopus RIS sets yielded 497 records, 393 unique records, and 147 studies for bibliometric mapping; a broader four-set synthesis identified 1,436 records, 1,299 unique records, and 167 studies for critical review. A claim-level audit of 100 published and verified sources was used to construct the conceptual framework. Publication output rose from 24 papers in 2022 to 41 in 2024, with 27 papers already recorded by 1 August 2026. Keyword co-occurrence revealed four dominant structures around BIM-quantity take-off, integration-machine learning, semantics-3D modelling, and cost estimation-5D BIM, while quality control remained peripheral. The synthesis shows that accuracy alone is insufficient because reliability also depends on completeness, consistency, reproducibility, agreement, traceability, semantic interoperability, and version integrity. The resulting Audit-Ready BIM Cost Estimation (ARBICE) architecture integrates seven layers from evidence grounding to decision assurance. The framework repositions BIM cost estimation as a traceable data-to-decision system and provides testable directions for cross-platform validation, explainable discrepancy diagnosis, and audit prioritization.
From a discriminated approach, this study examined cryptocurrencies and fraud with a focus on audit trail and regulatory slack. The is anchored on the rapid growth of cryptocurrencies in the global financial systems with their unique attributes of being decentralized, fast, and borderless transaction platforms. While these innovations have improved financial inclusion and efficiency, they have concomitantly enacted new opportunities for fraud and regulatory challenges. Thus, from a qualitative approach, a content analysis of the open-source current literature was deployed to glean evidence-driven insight. It was observed that while cryptocurrencies serve as alternative investment growth and wealth storage means, they are highly vulnerable to fraud, due to anonymity, lack of centralized control, and rapid technological growth. Therefore, the structural characteristics of cryptocurrencies precinct, significantly weakening traditional control mechanisms and creating opportunities for fraudulent exploitation. Also, blockchain technology which anchors the crypto world creates digital bread crumbs that often fissile out depending on the technological prowess of the perpetrator, enacting difficulties in the audit trail. These have serious implications along theoretical, policy and practical lines. Therefore, the study recommends achieving an optimal effective mitigation of cryptocurrencies downsides within the espoused thematic focus of the study requires a holistic and integrated approach that combines strong regulatory frameworks, advanced technological tools, institutional capacity building, and increased public awareness, as no single mechanism is sufficient to address the complexity and dynamic nature of fraud in digital financial systems.
This study examines the perceived impact of artificial intelligence (AI) applications on accounting practices, the ability of Jordanian industrial companies to anticipate future financial needs, and the effective utilization of organizational resources. The study adopts a descriptive-analytical design and uses a structured questionnaire administered to accountants, financial managers, heads of accounting departments, and internal auditors in industrial companies listed on the Amman Stock Exchange. A total of 150 questionnaires were distributed; 12 were excluded because of incomplete or inaccurate responses, leaving 138 valid questionnaires for analysis. The instrument was assessed for internal consistency using Cronbach’s alpha, and the overall coefficient was 0.875, indicating strong reliability. Descriptive statistics and one-sample t-tests were then used to assess respondents’ evaluations against a neutral benchmark of 3 on a five-point Likert scale. The results indicate strong positive assessments of AI applications across all three study dimensions. The overall mean for accounting-related applications was 4.46, the overall mean for improving future financial forecasting was 3.96, and the overall mean for effective resource utilization was 4.01. The corresponding reported t-statistics were statistically significant at the 5% level. The findings suggest that AI-enabled accounting technologies are perceived as useful for improving the timeliness and quality of accounting information, strengthening financial forecasting, supporting risk identification, and improving the utilization of physical, financial, and human resources. The study contributes empirical evidence from the Jordanian industrial context and highlights the need for organizational readiness, employee training, internal controls, and continuous technological adaptation.
Attempts have been made in this research to forecast returns of exchange rates of foreign countries in relation to Naira using the SVM, Neural Network (NN), and Random Forest (RF) forecasting models. The Value-at-Risk and Expected Shortfall results demonstrate that exchange rate risks intensified significantly during the post-crisis period. USD/NGN exhibited the highest post-crisis tail risk under the Neural Network model, with VaR₉₉ and ES₉₉ values reaching 1.2211 and 1.3003 respectively, indicating extreme downside exposure and elevated currency market fragility. Similarly, EUR/NGN and CAD/NGN recorded heightened post-crisis risk levels, reflecting increased investor uncertainty and inflationary exchange rate pressures. By contrast, the RF model generated more moderate and economically plausible risk estimates, suggesting stronger robustness and stability in volatile emerging market environments. Graphical analyses corroborate these findings, showing that Neural Network forecasts produced explosive and exponential depreciation trajectories in the post-pandemic era, while RF forecasts exhibited smoother and more gradual adjustment paths consistent with managed exchange rate dynamics. The study found consistently higher post-crisis VaR and ES values across models signal rising tail risks, which imply potential for large currency swings. Such volatility could exacerbate macroeconomic fragility, increase the cost of external debt servicing, and drive inflationary pressures through more expensive imports. Across all models, the RF system consistently delivered superior predictive accuracy, forecast stability, and tail-risk moderation particularly during the crisis and post-crisis periods. In contrast, the NN model produced exponential post-crisis forecast trajectories especially for USD/NGN and EUR/NGN; highly sensitive to structural breaks and may exaggerate persistent volatility in crisis-prone economies. Though informative, such outputs tended to overshoot plausible devaluation trends for the Naira, likely due to exaggerated extrapolation of recent market behaviors. SVM forecasts showed modest error levels and smoother, more plausible trends, offering less extreme but significant signals of future currency devaluation. This reinforces the importance of hybrid modeling approaches and the integration of non-linear machine learning tools in forecasting into central banking operations, exchange rate surveillance frameworks, and investor risk assessment strategies under turbulent economic conditions to enhance resilience against future external shocks and currency market disruptions.
This study aims to map the evolution of earnings persistence research, identify its intellectual, conceptual, and social structures, and examine its relationships with earnings quality, corporate governance, audit quality, and sustainability while proposing future research directions. A bibliometric approach was employed using the Scopus database. Following the PRISMA procedure, 482 records were identified, and 291 articles published between 2006 and 2026 were retained after applying timespan, document type, subject category, language, and journal filters. The dataset was analyzed using Bibliometrix/Biblioshiny through performance analysis, scientific mapping, and structured content analysis, including co word analysis, co citation, bibliographic coupling, and collaboration network analysis. The findings reveal increasing publication trends following IFRS adoption, with earnings quality remaining the dominant theme alongside growing attention to ESG, sustainability, and corporate governance. However, cross country collaboration and digital reporting remain underexplored. This study provides a comprehensive bibliometric overview and proposes future research directions for earnings persistence.
This paper examines the relationship between digital payment infrastructure (DPI), education spending, and government performance to produce sustainable development outcomes in a Sub-Saharan Africa (SSA). On panel data of Kenya, Nigeria, South Africa, Rwanda and Ghana over 2010-2022, the results of analysis use two-stage least squares (2SLS), fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and quantile regression methods to overcome the endogeneity, non-stationarity, and distributional heterogeneity. Findings indicate that DPI has a strong, positive and significant effect on the Sustainable Development Index among all estimators and quantiles, which support financial digitization as a structural cause of multidimensional development. The effectiveness of governance improves development based on the short-run dynamics and a distribution-specific effect, whereas government spending on education is always in the negative; this is due to the inefficiency, leakages in governance and long gestation lags and not necessarily the ineffectiveness of education. The internet penetration has negative conditional impacts, which explains the need to focus on digital finance rather than on an overall connection. The results highlight the fact that the outcomes of developing countries are not only determined by the distribution of resources but also the quality of institutions, their effectiveness in implementation, and the strategic targets of digitalization. The policy suggestions focus on digital financial inclusion, governance enhancement, education quality reforms, and integrated development plans.
This article has explained the influence of selected financial indicators on banking growth by taking into consideration SBI, HDFC and HSBC. Thus, monthly log data has been considered over a period from 2005 to 2024. The study has considered the Cobb-Douglas production function as a model specification to examine the above issue. It has been found that IDR is an important financial indicator to justify the banking growth in relation to CAR, NPAs, PPE, RO Adv., ROA, ROE and ROI of SBI, HDFC and HSBC.
This paper examines the relationship between earnings management and earnings quality in two countries (93 companies from Tehran Stock Exchange and 92 companies from Saudi Arabia Stock Exchange) for the period (2013-2022 Tehran) and (2014-2023 Saudi Arabia). The data were collected as a year -firm and analyzed using multiple regression. The earnings quality was measured through three separate attributes (earnings predictability, earnings smoothness and relevance of earnings). To achieve the research goals, three hypotheses were developed and, in each hypothesis, the moderating role of one of the earnings quality indicators for each category of non-stressed and distressed companies were studied. The results of the research showed that in all cases of measuring the earnings quality, the earnings management in distressed and non-distressed firms are efficient. Similarly, earnings quality of earnings predictability type in the Tehran Stock Exchange and the earnings quality of relevance type in the Saudi Stock Exchange and the distressed firms, the earnings quality of relevance type in the Tehran Stock Exchange and the earnings quality of earnings smoothness type in the Saudi Stock Exchange can explain future profitability. Moreover, for the first time, the emphasis on the relationship between the attributes of the earnings quality and the type of earnings management and future profitability is introduced globally, especially in Saudi Arabia and Iran. By using international data, the comparison between the approach of Saudi Arabia and the approach of Iran will be done.
This research is motivated by the low level of financial welfare among lecturers, which is influenced by the complexity of economic factors, financial behavior, and the development of financial technology. In the context of Muslim society, variables play a very important role in shaping financial satisfaction, especially if mediated by healthy financial behavior. The approach used is Systematic Literature Review (SLR) with the PRISMA protocol, which includes a literature search on the Google Scholar database using the Publish or Perish tool and Bibliometric and VOSviewer analysis of publications during 2014–2024 as many as 127 articles. The four independent variables have a positive influence on financial satisfaction, either directly or indirectly through financial behavior, with sharia financial literacy and financial technology occupying the most dominant position. The integration of the four variables in a single model makes a theoretical contribution to the development of a conceptual framework that integrates cognitive, behavioral, technological, and religious value dimensions. In this paper, the variable of qona'ah attitude is used which is rarely used in the concept of financial satisfaction. This study is mainly in data sources that only include open access literature in the 2014–2024 timeframe, which has the potential to ignore important findings from paid articles or publications prior to that period. For further research, it is recommended to test this mediation model in cross-border and cultural populations, as well as the exploration of the integration of other psychological variables such as financial self-efficacy.
This study discusses Integrated Reporting (IR) research through the lens of its thematic, geographical, and citation evolution from 2006 to 2024. The methodology demonstrates 1,136 SCOPUS-indexed publications, the PRISMA framework, VOSviewer for co-occurrence mapping and Bibliometrics for trend and thematic analysis. The findings reveal that the subject has evolved towards empirical (quantitative) investigations addressing IR quality, determinants, and organizational outcomes. Geographical mapping shows research concentration in Europe and emerging engagement from Asia-Pacific regions, while citation analysis highlights the growing influence of sustainability and ESG-oriented frameworks. Thematic mapping further identifies a paradigm shift from standalone IR studies toward integrated approaches combining CSR, ESG, and SDG perspectives, reflecting the institutionalisation of IR. Unlike previous bibliometric studies, this paper covers a longer time span and a broader dataset, about how the field has matured as a bridge between financial and non-financial reporting. The study thus shows new areas of research to focus.
The present study has considered securities data and Environmental, Social and Governance (ESG) measures of firms from France, Japan and the United Kingdom. Securities data and ESG measures are subjected to cross-sectional OLS regressions of working capital and cash conversion cycle on ESG risk ratings. Agency cost effects have been found, as ESG risk increased working capital, while reducing the cash conversion cycle. Results are consistent across all three countries. It has been concluded that failure to meet ESG goals increases firm risk. The increase in risk may be met by increasing short-term liquidity. The unnecessary increase in short-term liquidity limits the firm’s ability to employ funds to exploit growth opportunities and maximize shareholder wealth.
Micro and Small Enterprises are a critical catalyst for socio-economic development in Brazil. However, financial and technical limitations frequently hinder the access and implementation of management tools by Micro and Small Enterprises. This study addresses this challenge through a case study that applies the Throughput Accounting to determine the most profitable production mix for the small enterprise Bianfer Indústria Metalúrgica. The company manufactures and commercializes parts and components for agricultural machinery and equipment in Brazil. Production mix decisions are currently based on the owners’ experience, sales history, and Absorption Costing. This approach, however, generates additional costs and inventory thereby compromising the profitability of Bianfer Indústria Metalúrgica. The pursuit of enhanced profitability led to the formulation of three hypothetical scenarios to compare the production mix proposed by Absorption Costing and Throughput Accounting concerning the Return on Assets (ROA). Mathematical modeling and scenario simulations were conducted using the Microsoft Office Excel 365. The results indicate that Throughput Accounting is readily adaptable, solves the problem more quickly, and provides superior financial gains (ROA from 1.36% to 2.71%). This study addresses an important practical gap that can guide students, professionals, and researchers in the application of Throughput Accounting. The main contribution of this study is empirical evidence that Throughput Accounting is an effective management tool for Micro and Small Enterprises. The implementation of Throughput Accounting through a simple Microsoft Office Excel model can significantly improve production mix decision-making in Micro and Small Enterprises.
Aviation transportation, as the aerial corridor supporting the global economic operation, has become increasingly significant in the post-pandemic recovery phase. However, beneath the industry prosperity lie numerous risks and challenges. This paper initially elaborates systematically on the rationale for selecting CNN models for conducting research on financial risk early warning, followed by the choice of publicly listed airlines in the A-share market, thereby establishing samples for financial risk early warning and financial health. Subsequently, through differential testing of these two sample categories, suitable financial risk early warning indicators tailored for airlines are scientifically and systematically sifted out. Moreover, to address issues such as the different dimensions of indicator data, the imbalance in the number of sample categories, and dataset partitioning, data preprocessing efforts are undertaken. Finally, the processed data is fed into the CNN model for training, followed by an assessment and analysis of its early warning efficacy.
Technology-mediated client behaviors have emerged as critical determinants of organizational effectiveness and competitive positioning in the financial services landscape. This study examines multi-criteria client risk assessment within financial institutions, exploring the key facets that drive organizational capability development in managing digital transformation challenges. Using logistic regression and mediation analysis, we conducted an in-depth analysis based on a sample of 2,824 client profiles and comprehensive social media behavioral validation using 53,187 Reddit posts. Our findings reveal that technology usage assessment capabilities, age-based segmentation strategies, and behavioral motivation evaluation are the primary factors influencing organizational effectiveness in client risk management. In particular, systematic technology assessment emerged as the most critical determinant, underscoring the importance of developing sophisticated behavioral analytics capabilities to address evolving digital client behaviors. The implications of our findings extend to organizational strategy, innovation, and future research directions in financial services management, offering valuable insights to improve institutional effectiveness and competitive positioning against evolving technology-mediated challenges.
This paper explores studies that determine life insurance efficiency, an area that is gaining in recognition as being important to investigate. As well a scrutinization and exploration of the numbers and recent trends of methods and some aspects of life firm efficiency measurement is implemented. In its overview of life company efficiency items written since 1982 this project shows how the most fundamental elements of life enterprise efficiency estimation, for example the set-up and form of outputs and inputs, have been coped with. Therefore, an assessment of the overall results of efficiency studies is possible. Similarly, ideas for potential further research are portrayed. One conclusion drawn from the results of this project is that the steady increase in both the volume and scope of pieces scrutinizing life firm efficiency means that it is being perceived as being of greater importance. Consequently, this review will be of value to both practitioners and regulators concerned with this subject in that it will enable an assessment of which aspects of this field of study need more research and which are otherwise worth developing.
This study investigates the dynamic causal relationship between India’s GDP and key macroeconomic variables (exports, imports, inflation, exchange rate, BSE and NSE) over a period from January 2000 to December 2024 by using monthly data. Cointegration test has confirmed the presence of one cointegrating equation that means long-run equilibrium relationship exists along with short-run dynamics as provided by VECM estimates through HAC standard errors provided by Newey-West. The CUSUM test has confirmed overall model stability and while heteroskedasticity has been taken care of by applying HAC standard errors. The study has also shown both uni-directional as well as bi-directional casualties.
The study investigates the impact of globalisation on stock market performance in Nigeria. We gathered secondary data from the Nigerian Exchange Group for the years 2016–2024 using an ex post-facto research design. Results show that market capitalisation, stock turnover ratio, and daily stock trading volume in Nigeria are significantly positively impacted by globalisation as measured by foreign direct investment (FDI). The study has several implications including that globalisation can enhance economic growth, promote trade and augment economic efficiency. We recommend that; policy makers and market regulators should facilitate increased participation in international financial markets by implementing policies that attract FDI and portfolio investments.
The supermarket sector is one of the most important components of the retail industry. Rapidly growing chain supermarkets stand out in this sector. This study introduces a hybrid method to assess the performance of stores within a supermarket chain. In the study, stores are compared over a five-year period using various criteria. These criteria include financial metrics such as rent cost, employee cost, energy cost, waste cost, supply cost, and sales revenue, as well as store size. Initially, the presence of differences between supermarkets based on these criteria was investigated. According to the criterion-based analysis, it is not easy to make a decision about the overall performance of supermarkets. Therefore, conducting the analysis using multi-criteria decision-making methods provides more meaningful results. In the proposed hybrid method, the criteria are first weighted using the entropy method to determine their importance levels. It was determined that the most important criterion is rent cost. The performance was then calculated using the VIKOR, MOORA, and ARAS methods. As a result of the analysis, the same store showed the best performance in all three methods.
