Financial Data Research Articles

Development of a decision support system for client acceptance in independent audit process

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.

Unraveling Financial Markets: Deep Neural Network-Based Models for Stock Price Prediction

This paper delves into the potential and challenges of leveraging deep neural networks (DNNs) in stock price forecasting. Traditional econometric models often grapple with the complexities of financial time series data, leading to the exploration of DNNs, especially architectures like Long Short-Term Memory (LSTM) networks, to capture intricate patterns in such data. While these networks present promising results, challenges such as model interpretability, non-stationarity of data, overfitting, and computational demands remain. The financial sector's increasing digitization and influx of alternative data offer a unique opportunity for DNNs, emphasizing their capability for automatic feature extraction. However, the integration of DNNs necessitates a multi-disciplinary approach, involving financial experts, data scientists, and computational specialists. The paper concludes with an optimistic outlook for the synergy between deep learning and traditional financial theories, aiming for a harmonious blend of modern computational techniques with foundational financial principles.

Audit firm tenure and corporate tax avoidance: evidence spanning COVID-19 pandemic

Purpose This study aims to examine the effect of audit firm tenure (AFT) on corporate tax avoidance (CTA) and the moderating effect of the COVID-19 pandemic. Design/methodology/approach The sample comprises 41,074 firm-year observations from 32 countries from 2015 to 2020, for which data are collected from various sources: financial data from the Refinitiv database, country corporate tax rates from the Tax Foundation, and other country-level data from the World Bank database. The authors use the book tax difference to measure CTA and multiple proxies for AFT. Findings This study finds that a longer AFT is associated with higher CTA, confirming the notion that long AFT impairs auditor independence. The findings remain robust when considering various AFT proxies, incorporating Hofstede’s cultural factors, using weighted least-squares estimation and addressing endogeneity through propensity score matching. This study also finds a non-linear relationship between extended client and auditor relationships and CTA, supporting the mandatory audit firm rotation regulation and increasing investors’ caution regarding the consequences of extended client–auditor relationships on firm behaviour. Research limitations/implications This study offers new evidence on the effect of the COVID-19 pandemic on the link between AFT and CTA and documents a non-linear relationship between AFT, which has not been addressed in prior studies. Practical implications The findings of this study have several significant practical implications. First, governments and policymakers gain insights into the consequences of extended auditor–client relationships, hence calling for a review of auditing and taxation regulations. Second, the findings provide important insights into the issue of auditor independence, especially during long engagements and crises such as COVID-19. Finally, investors and tax authorities should be more cautious about the risks of aggressive tax avoidance during crisis periods. Originality/value To the best of the authors’ knowledge, this is the first study to use a global data set to investigate the effect of AFT on CTA during the COVID-19 pandemic.

Does Working Capital Management Affect Financial Performance: Empirical Evidence of Indian Manufacturing Sector?

This research aims to analyse the Working Capital Management affects financial performance in Indian manufacturing sector. This research employs secondary data for data collecting from BSE, which comprises 20-year financial data from 2003 to 2022 using the sample of 419 companies. The study used descriptive statistics, random effects, and fixed effect models to define the sample and evaluate the influence of working capital management on manufacturing industry. Findings suggested that working capital management proxies have a major impact on the financial performance of the company. Proxy of CCC indicates that negative and significant impact of GOP, NPM, and NPR. The coefficient of ICP indicates that negative and significant relation between firm performances. In addition, we discovered a statistically significant inverse relationship between APP and GOP and NPM and NPR, suggesting that the more quickly a company in our sample pays its bills, the less profit it generates. The above table demonstrates that RCP has a positive and significant alliance with GOP. To the best of the author’s knowledge, this paper provides the study to examine the WCM’s effect for firm’s performance and contributes valuable insights on this company’s investors and managers.

Role of R&D Investment on Future Performance: Evidence From Malaysian Companies

R&D Investment is an issue that is still interesting in business research today because it constantly explores company development in terms of technology and innovation. Our study intends to investigate the impact of R&D investment on Malaysian company performance in the future. This empirical study uses company financial statement data accessed via the OSIRIS database. The sample is a non-financial company in Malaysia from 2003 to 2019. This study evaluates The hypothesis using unbalanced panel data regression on 460 company years of observation. Our findings indicate that R&D investments have a degrading impact on the performance of Malaysian companies in the future. These findings indicate that companies in developing countries such as Malaysia have yet to give special attention to investing in R&D as the main business strategy.

Which direction for stakeholder capitalism? Approaches and reporting towards POP accounting

AbstractOur study uncovers the evolving landscape of stakeholder capitalism. This paradigm recognises the intrinsic value of capital among diverse stakeholders and underscores the importance of engaging them in the long‐term value‐generation process. Through bibliometric analysis, we explore the multifaceted nature of stakeholder capitalism, its historical evolution, and the current debates surrounding its implementation, particularly in social reporting and integrated performance representation. Our findings illuminate the growing emphasis on POP accounting as a potential bridge between traditional shareholder‐centric models and a more inclusive stakeholder‐oriented approach. POP accounting emerges as a vital tool for demystifying complex financial data, offering a transparent, accessible narrative that resonates with broader stakeholders. By integrating insights from various sectors and geographical regions, this paper sheds light on the practical implications of adopting POP accounting within stakeholder capitalism. It suggests it could catalyse a more democratic, transparent, and sustainable corporate governance framework. Our research contributes to the ongoing corporate responsibility and governance discourse, providing actionable insights for businesses, policymakers, and academics committed to fostering an equitable, stakeholder‐inclusive future.

Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market

Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high-volume, institutional-like raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors’ existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech-enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%–10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling-like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech-enabled access to high-volume historical price data increases individuals’ overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology-led, wider data access for retail investors. This paper was accepted by D. J. Wu, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01379 .

Assistance in Preparing Simple Bookkeeping and MSME Taxation for the East Java Fatayat Business Group

The Pawon Urip Program in Lumajang Regency is community empowerment in terms of food security and community nutrition with the aim of empowering the socio-economic sector of the Pejarakan Village community. Physical observations, social observations, and interviews, as well as exploratory descriptive analysis, were used to answer the research objectives. The economy of the people of Pejarakan Village is classified as lower middle class; this, of course, cannot be separated from the influence of less than optimal levels of education and land use; the social phenomenon of apathy is also reflected when people are too busy. Pawon Urip planting actually provides benefits to the community; although insignificant, all the processes that go through until the results can be enjoyed have an impact on the knowledge, psychological, and economic aspects of the community. The managerial implication in this research is that with assistance, MSME owners will better understand the basics of accounting and taxation, which will help them prepare their books correctly. Good bookkeeping will also help increase financial transparency and may impact investor or lender confidence. With accurate financial data, MSME management can make better business decisions. Whether in terms of investment, expenses, or other business strategies.

Integrating generative AI into financial market prediction for improved decision making

This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy. Through investment return analysis, the application value of model predictions in actual investment strategies is confirmed, providing investors with new ways to improve the decision-making process. In addition, the evaluation of model stability and reliability also shows that although there are still challenges in responding to market emergencies, overall, GAI technology has shown great potential and application value in the field of financial market prediction. The conclusion points out that integrating generative artificial intelligence into financial market forecasts can not only improve the accuracy of forecasts, but also provide powerful data support for financial decisions, helping investors make more informed decisions in a complex and ever-changing market environment. choose.

THE IMPACT OF APPLYING INTERNATIONAL STANDARD NINE ON THE QUALITY OF ACCOUNTING INFORMATION

There is an increasing need to find professional institutions that sponsor the work of companies in the financial and accounting fields due to the changes and developments taking place in the economic environments. Hence, the International Accounting Standards Board - IASB, which includes many other sub-boards like the Financial Accounting Standards Board - FASB, has issued many scientific publications in financial and accounting fields. Furthermore, the International Accounting Standards Board, through its sub-boards, including the Financial Accounting Standards Board, has formulated several International Financial Reporting Standards ( IFRS ) to improve the quality of financial reporting and the transparency of the information it contains in a globally applicable and accepted manner. This information would reflect the current situation of economic institutions in terms of performance and financial position in response to changes in economic environments, and to meet the needs of This framework will be applied consistently across transactions, industries, and capital markets to improve the comparability of financial data between economic institutions operating in the same sector globally and within a comprehensive, unified, and stable framework.

The intelligent prediction and assessment of financial information risk in the cloud computing model

The intelligent prediction and assessment of financial information risk in the cloud computing model

Effect of Mergers and Acquisitions in the Indian Banking Sector: A Case Study on Bank of Baroda

Effect of Mergers and Acquisitions in the Indian Banking Sector: A Case Study on Bank of Baroda

Restoring trust in ESG investing through the adoption of just transition ethics

The prominent growth in environmental, social and governance (ESG) investment is evident, with the number of global assets managed sustainably more than doubled over the last decade. This trend is expected to continue until 2030. This type of financial data is positive but given the United Nations stated 'climate emergency' and 'climate survival' in society today, there needs to be an even greater acceleration of growth in ESG investment. Unfortunately, significant negativity has emerged on ESG in recent years. This 'Cutting Edge' study explores the reasons why and how ESG investment has veered off the journey towards enabling society to achieve both its targets under the 2030 United Nations Sustainable Energy Agenda and the 2015 Paris Agreement. It examines the factors prompting leading multinational companies, particularly in the energy and food sectors, to shift their corporate strategies. The key message advanced is that ESG frameworks and guidelines are not problematic; rather, the issue lies in the practice of ethics in decision-making within corporations. Addressing this ethical challenge, which is at the heart of ESG practices, across different professions and disciplines can rebuild trust among stakeholders in ESG investing. This form of interdisciplinary ‘just transition ethics' can re-orient us back on the journey towards a just and sustainable world.

Nonpharmacological intervention therapies for dementia: potential break-even intervention price and savings for selected risk factors in the European healthcare system

BackgroundNew effective treatments for dementia are lacking, and early prevention focusing on risk factors of dementia is important. Non-pharmacological intervention therapies aimed at these factors may provide a valuable tool for reducing the incidence of dementia. This study focused on the development of a mathematical model to predict the number of individuals with neurodegenerative diseases, specifically Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis. Scenarios for non-pharmacological intervention therapies based on risk factor reduction were also assessed. The estimated total costs and potential cost savings from societal were included.MethodsBased on demographic and financial data from the EU, a mathematical model was developed to predict the prevalence and resulting care costs of neurodegenerative diseases in the population. Each disease (Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis) used parameters that included prevalence, incidence, and death risk ratio, and the simulation is related to the age of the cohort and the disease stage.ResultsA replicable simulation for predicting the prevalence and resulting cost of care for neurodegenerative diseases in the population exhibited an increase in treatment costs from 267 billion EUR in 2021 to 528 billion EUR by 2050 in the EU alone. Scenarios related to the reduction of the prevalence of dementia by up to 20% per decade led to total discounted treatment cost savings of up to 558 billion EUR.ConclusionThe model indicates the magnitude of the financial burden placed on EU healthcare systems due to the growth in the population prevalence of neurodegenerative diseases in the coming decades. Lifestyle interventions based on reducing the most common risk factors could serve as a prevention strategy to reduce the incidence of dementia with substantial cost-savings potential. These findings could support the implementation of public health approaches throughout life to ultimately prevent premature mortality and promote a healthier and more active lifestyle in older individuals.

Navigating the Cascading Ripples: A Study on the Interplay between the COVID-19 Recession and the Financial Fortunes of Micro Finance Institutions (MFIs)

Purpose: The success of Micro Finance Institutions (MFIs) hinges on effective decision-making amidst challenging economic circumstances, necessitating the ability to anticipate and navigate future business conditions. This research aims to explore the relationship between financial performance and the COVID-19 pandemic's impact on MFIs listed on the National Stock Exchange (NSE) in India. By analyzing financial data and employing statistical techniques, the study seeks to elucidate the resilience of MFIs during the pandemic and the role of financial ratios in predicting earnings per share (EPS). Additionally, the research aims to contribute to a deeper understanding of the interplay between financial indicators and organizational outcomes within the microfinance sector. Design/Methodology/Approach: This research adopts a quantitative approach, utilizing secondary data obtained from quarterly financial reports of selected MFIs listed on the NSE from 2018 to 2022. Statistical analyses such as paired t-tests, correlation, and regression are employed to assess the impact of the COVID-19 pandemic recession on MFIs' profitability and to investigate the relationship between financial ratios and earnings per share (EPS). Findings/Result: Despite the COVID-19 pandemic recession, the profitability of Indian Micro Finance Institutions (MFIs) remained largely unaffected, as indicated by non-significant differences in earnings before interest and taxes (EBIT) before and after the pandemic. Regression analysis highlighted significant influences of financial ratios like the current ratio, total asset ratio, net profit ratio, and gross profit ratio on earnings per share (EPS), underscoring the sector's resilience and the importance of financial analysis in predicting MFI performance. Originality/Value: This research offers novel insights into the resilience of Indian Micro Finance Institutions (MFIs) during the COVID-19 pandemic and underscores the significance of financial analysis in guiding strategic decision-making processes within the microfinance sector. Paper Type: Empirical Research.

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

Business strategies to counter climate change risks to long lived production assets

The business community is concerned about how to account for long-lived production assets in an era of climate change. Globally, accounting standards and guidance are emerging but these are yet to provide the level of clarity required by the business and accounting profession on the uncertainty of future climate and how this can compromise some long-lived assets. Very few organisations have the necessary expertise and data to integrate climate change scenarios into their long-lived production asset accounting. This poses an additional risk to business and society. We explore different approaches for including risk and uncertainty in business accounting and reporting. Seven desk-based case studies of emerging practice in the food sector demonstrate how these varied climate risk and uncertainty models for infrastructure are being deployed around the world. Through review of data from company annual reports, documents and websites from 2021 to 2023, we find that specific climate-impacted long-lived asset accounting is relatively rare, notwithstanding that risk and uncertainty models are in effective use in other emerging areas. We provide recommendations on the development of data and modelling frameworks for climate-related financial data and an improved accounting discipline for climate-related financial impacts.

Factors Affecting the Implementation of Risk-Based Internal Auditing

This paper aims to investigate the factors affecting risk-based internal audit (RBIA) implementation in public sector organizations in Saudi Arabia. This paper utilized 234 usable answered questionnaires from internal audit managers, internal auditors, accountants, and executives working in Saudi public sector agencies. The gathered data were analyzed by applying partial least squares–structural equation modeling (PLS-SEM). Results show that management support, internal auditor role, risk management system, and training in risk management all positively and significantly influence the RBIA. Improved internal auditing procedures and an efficient internal monitoring system will significantly curtail any risks impeding the organization’s goals, diminish the temptation to fabricate financial data or statistics, and enhance the accuracy of financial reporting/statements. Moreover, this study’s results have crucial implications for managers of public sector organizations, heads of internal audit departments, internal auditors, and accountants seeking to improve the reliability of internal audits and other aspects of financial information. Published research on what variables are influencing RBIA implementation is scarce. This study adds to the nascent literature by focusing on Saudi Arabian public sector organizations, establishing empirical variables based on an in-depth review of the relevant research and conducting an empirical investigation of the factors associated with RBIA implementation in the Saudi economy. By concentrating on public sector organizations in Saudi Arabia, this paper sheds light on other nations with comparable systems for governance policies and processes in their government-run entities.

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.