Analysis Of Financial Data Research Articles

TRANSPARENCY IN CORPORATE GOVERNANCE: ITS ROLE IN MITIGATING FINANCIAL RISKS AND IMPROVING PERFORMANCE METRICS

In-depth analysis of the vital role that openness plays in corporate governance is provided in this paper, with particular attention to how it improves organizational performance measures and reduces financial risk. Transparent corporate governance processes are more important than ever in a time of growing complexity in global business operations and elevated stakeholder expectations. Using a mixed-methods approach, this study combines qualitative insights from in-depth interviews with business executives, regulators, and governance experts with quantitative analysis of financial data from 500 publicly traded companies across several industries. The study's five-year duration, from 2019 to 2023, enables a thorough analysis of trends and causal connections. Based on reduced stock price volatility, fewer substantial financial restatements, and improved credit ratings, our results show a clear positive association between more corporate transparency and better financial risk management. Additionally, the study shows that businesses with more open governance processes routinely beat their less open competitors on a number of important performance criteria, such as customer happiness, employee productivity, and return on equity (ROE). The study also reveals complex connections between particular transparency metrics and performance results, emphasizing the necessity of customized strategies for improving openness depending on organizational and industry-specific factors. These revelations aid in the creation of an all-encompassing framework for putting corporate transparency into practice and gauging its effectiveness, offering practitioners, legislators, and scholars studying corporate governance useful direction.

Enhancing African market predictions: Integrating quantum computing with Echo State Networks

The integration of Quantum Computing into Echo State Networks (ESN) materializes in the form of the Quantum Echo State Network (QESN), a methodological innovation that reshapes predictive analytics within the domain of artificial intelligence. This investigation harnesses the novel QESN model alongside traditional ESNs, deploying them within the dynamic and burgeoning financial market of Africa. Our focus zeroes in on the Google Stock Price dataset, which provides a rich tapestry of regional financial activity. The QESN model distinguishes itself by a complex interconnection of qubits that conduct quantum operations, offering a marked amplification of computational capability. The empirical analysis reveals that the QESN model’s predictive prowess substantially exceeds that of its ESN counterpart, achieving an unprecedentedly low Mean Squared Error (MSE) of 0.00021 in forecasting market trends. This exceptional figure redefines the standards of financial prediction models in African markets and establishes the QESN as an instrumental breakthrough, providing unparalleled accuracy in the analysis and prediction of financial data.

Ntegrating Quantum-Inspired Principles into Financial Data Science: Optimizing Reservoir Computing Models

This research explores the integration of quantum-inspired principles into financial data science with a focus on optimizing Reservoir Computing (RC) models. This approach aims to achieve a more nuanced representation of market states, improving accuracy in predicting stock price movements and capturing complex interdependencies among financial assets. Utilizing historical data from Yahoo Finance, implement and optimize RC models enhanced by Quantum State Model (QSM) and Entangled Asset Model (EAM) techniques. The results demonstrate the potential benefits and challenges of applying these quantum-inspired methods to financial data analysis. Keywords: Computational Intelligence, Financial Data Science, Quantum Computing, Quantum Machine Learning (QML), Reservoir Computing

Trend Analysis of Enterprise Intelligent Financial Applications

Recently, intelligent financial applications have been regarded as an important pillar of enterprise development and will play a significant contribution to the progress of future enterprises. Enterprises need to make continuous progress and adapt to the rapidly changing social environment with high competitiveness, and finance, as an indispensable part of enterprise development, needs to make great progress. With the continuous development of digital transformation, intelligent finance has become an important driving force for enterprise development, and is widely used in information processing, personnel management, market analysis, business management, etc. The purpose of this paper is to explore the sustainable impact of intelligent finance on enterprise development, including the application of financial intelligence, financial shared development, data analysis and management, etc., and comprehensively sort, analyze and summarize the relevant research through the method of literature review. In addition, it provides reasonable suggestions for the future development direction of enterprise finance, so that enterprises can gain competitive advantages from intelligent financial applications, improve company efficiency, and achieve good and long-term development.

Trend Analysis of Enterprise Intelligent Financial Applications

Recently, intelligent financial applications have been regarded as an important pillar of enterprise development and will play a significant contribution to the progress of future enterprises. Enterprises need to make continuous progress and adapt to the rapidly changing social environment with high competitiveness, and finance, as an indispensable part of enterprise development, needs to make great progress. With the continuous development of digital transformation, intelligent finance has become an important driving force for enterprise development, and is widely used in information processing, personnel management, market analysis, business management, etc. The purpose of this paper is to explore the sustainable impact of intelligent finance on enterprise development, including the application of financial intelligence, financial shared development, data analysis and management, etc., and comprehensively sort, analyze and summarize the relevant research through the method of literature review. In addition, it provides reasonable suggestions for the future development direction of enterprise finance, so that enterprises can gain competitive advantages from intelligent financial applications, improve company efficiency, and achieve good and long-term development.

Impact of Bankruptcy on Firms Performance

Bankruptcy represents a critical event in the lifecycle of a firm, profoundly affecting its operational, financial, and strategic dimensions. This study aims to provide a comprehensive analysis of the impact of bankruptcy on firm performance, exploring various facets of its implications on both short-term and long-term operational effectiveness, financial health, and market position. The research adopts a mixed-method approach, integrating quantitative analysis of financial data with qualitative insights from case studies and expert interviews. Firstly, the study examines the immediate consequences of bankruptcy, such as disruptions in operations, loss of market trust, and creditor actions. It investigates how these factors contribute to declines in revenue, profitability, and market share in the short term, often leading to asset liquidation and restructuring efforts. Moreover, the research delves into the long-term repercussions of bankruptcy, including the enduring effects on organizational reputation, employee morale, and stakeholder relationships. It investigates the challenges faced by firms in regaining market credibility, attracting investment, and rebuilding their competitive advantage post-bankruptcy. Furthermore, the study analyses the role of management strategies, such as turnaround initiatives and reorganization efforts, in mitigating the negative impacts and facilitating recovery. Additionally, the research explores the sector-specific dynamics of bankruptcy effects, considering variations in industry regulations, market competition, and technological disruptions. It investigates how factors such as industry concentration, market demand elasticity, and technological obsolescence influence the severity and duration of performance decline following bankruptcy. Overall, the findings of this study contribute to a deeper understanding of the multifaceted implications of bankruptcy on firm performance, offering insights for managers, policymakers, and investors in navigating the challenges associated with financial distress and facilitating sustainable recovery strategies.

Dinamika Profitabilitas dan Kebijakan Hutang: Dampaknya terhadap Evaluasi Nilai Perusahaan melalui Kebijakan Dividen

In a dynamic business environment, companies are faced with the challenge of maximizing company value. Profitability and debt policy have been identified as key factors influencing company valuation. This research aims to understand the complex relationship between profitability and debt policy, and analyze how the interaction of these two factors influences the assessment of company value through the influence of dividend policy. This research method involves careful statistical analysis of the relevant company's financial data. Time series data will be taken from financial reports for a certain period, and a regression model will be used to measure the correlation between profitability, debt policy, and company value assessment through studying dividend policy. It is hoped that the results of this research will provide an in-depth understanding of how the dynamics of profitability and debt policy influence each other in the context of assessing company value. In addition, this research will provide insight into the crucial role of dividend policy in articulating the impact of these two factors.

Peran Agresivitas Pajak dalam Strategi Keuangan Perusahaan: Analisis terhadap Ukuran, Leverage, dan Profitabilitas

Tax aggressiveness has been a major focus in the corporate finance literature, because it influences strategic decisions related to tax payments and corporate financial performance. Previous studies show that tax aggressiveness can interact with factors such as company size, leverage, and profitability, but not many studies have specifically analyzed this relationship. This research aims to analyze the role of tax aggressiveness in a company's financial strategy with a focus on its effect on company size, leverage and profitability. The method used in this research is multiple regression analysis of financial data from a number of companies spread across various industrial sectors. Data was collected from the company's annual financial reports and analyzed using statistical software. The results of the analysis show that tax aggressiveness has a significant impact on the company's financial strategy, by influencing size, leverage and profitability. These findings indicate that firms tend to adopt certain financial strategies to optimize their tax benefits.

Application of artificial intelligence Sensors based on random forest algorithm in financial recognition models

Due to the important role of financial information disclosed by enterprises in investor decision-making and economic policy formulation, the corporate market has set the latest standards and requirements for the quality of financial reports. In the current situation, financial recognition work is mainly completed manually, which is inefficient and prone to errors, and is not suitable for large-scale applications. Therefore, we conduct in-depth research on financial recognition models. Firstly, we analyzed the random forest algorithm and used the SMOTE algorithm to expand the sample data. We successfully constructed an optimized random forest algorithm model, which can better improve accuracy and reduce misjudgment rates. Secondly, combined with the current financial data analysis of artificial intelligence technology, the steps for data management and data extraction were designed. Finally, while establishing a financial recognition model, a channel for obtaining internet information and a program for processing this information were also established. After experimental verification, the random forest model has shown better fitting performance in the field of financial fraud identification, providing more reliable support for applications in this field. Therefore, the financial recognition model constructed using artificial intelligence technology based on random forest algorithm has significant value in both theoretical and practical aspects, effectively reducing corporate financial risks.

ANALYSIS OF THE USE OF ARTIFICIAL INTELLIGENCE IN THE DIGITAL FINANCIAL ENVIRONMENT IN UKRAINE

The significance of the research is determined by the fact that the use of AI in the digital financial environment of Ukraine opens numerous opportunities for improving its efficiency, security, accessibility, and innovation. Therefore, this article is devoted to the analysis of the specifics of using artificial intelligence in the digital financial environment in Ukraine. Within the research, it has been proven that artificial intelligence is actively being implemented and adapted to the needs of various areas of the digital financial environment through specific transformations. It has been concluded that among the transformations driven by the use of artificial intelligence in the digital financial environment in Ukraine, we can distinguish the growing popularity of remote payments, the expansion of open banking usage, the development of digital identification, the advancement in financial data analysis, fraud detection, process automation, and the provision of personalized financial advice, the increase in the number of fintech companies working with AI, the heightened focus on cybersecurity, and the active regulation of the digital financial environment.

Differential entropy estimation with a Paretian kernel: Tail heaviness and smoothing

Differential entropy extends the concept of entropy to continuous probability distributions, measuring the uncertainty associated with a continuous random variable. In financial data analysis, accurately estimating differential entropy is pivotal for understanding market dynamics and assessing risk. Traditional methods often fall short when dealing with the heavy-tailed distributions characteristic of financial returns. This paper introduces a novel approach to differential entropy estimation employing a Paretian kernel function adept at handling tail heaviness’s intricacies. By incorporating an additional smoothing parameter, the Pareto exponent, our method offers flexibility in adjusting to light and heavy-tailed distributions. We compare our approach against established estimators through a comprehensive Monte Carlo simulation, demonstrating its superior performance in various scenarios. Applying our method to foreign exchange market data further illustrates its practical utility in identifying stochastic regimes and enhancing financial analysis. Our findings advocate for integrating the Paretian kernel estimator into the toolkit of financial analysts and researchers for a more nuanced understanding of market behavior.

Working Capital Management in Different Sector

Effective working capital management is crucial for the financial health and sustainability of businesses across industries. This research paper aims to investigate and compare the working capital management practices adopted by companies in different sectors. The study employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights gathered through interviews with industry experts. The research analyzes the working capital management performance of companies operating in various sectors, including manufacturing, retail, technology, and services. Key metrics such as liquidity ratios, inventory turnover, receivables collection period, and payables deferral period are evaluated to assess the efficiency and effectiveness of working capital management strategies employed by firms in each industry. Furthermore, the paper examines the impact of industry-specific factors such as demand volatility, supply chain dynamics, technological advancements, and regulatory environments on working capital management practices. By comparing and contrasting the findings across different industries, this study provides valuable insights into the factors influencing working capital management decisions and identifies best practices that can be adapted by companies to optimize their working capital efficiency and enhance overall financial performance. The research contributes to the existing literature by offering a comprehensive analysis of working capital management practices across diverse industries, highlighting industry-specific challenges and opportunities. The findings of this study have practical implications for managers, investors, and policymakers seeking to improve working capital management strategies and enhance the competitiveness and resilience of businesses in today's dynamic economic landscape.

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.

AN EMPIRICAL ANALYSIS OF FUND MANAGEMENT ANALYSIS AT POWER GENERATION AND INFRASTRUCTURE SECTOR

This empirical analysis delves into fund management practices within heavy industries, concentrating on investment strategies, performance evaluation metrics, and risk management techniques. Utilizing a blend of quantitative analysis of financial data and qualitative assessment of managerial decisions, the study investigates the drivers behind fund performance and uncovers optimal practices for enhancing fund management within heavy industrial sectors. The findings provide valuable insights for industry practitioners, offering actionable recommendations to bolster performance and mitigate risks. Additionally, this research contributes to the existing literature on investment management within specialized sectors, enriching our understanding of effective strategies tailored to the unique challenges and opportunities inherent in heavy industries.

Environmental Risk Identification and Green Finance Development Based on Multi-scale Fusion Recognition Network

AbstractThis paper aims to enhance the resilience of financial enterprises against environmental risks by leveraging financial data analysis tools. The approach involves designing environmental risk assessment indicators and rating criteria. The study utilizes a convolutional neural network model extended by a multi-scale feature fusion module to analyze environmental risk information in the industry. The proposed model achieves impressive results with accuracy (Acc), precision (P), recall (R), and F1 scores reaching 99.09, 96.31, 95.32, and 95.64, respectively. These metrics outperform those of comparison models. The success of this model is anticipated to pave the way for the transformation of green finance through automated industry-level environmental risk assessment. Furthermore, the method’s adaptability extends beyond environmental risks, offering a scalable solution for identifying and assessing environmental risks in various contexts.

Is auditor financial decision-making affected by prior audit report information? A behavioral approach

The role of financial experts is to provide their professional judgments with an opinion included in the financial reports after reviewing an entity's financial information, following a specific audit process. We investigate whether confirmation bias (through prior audit opinions) occurs among auditors during the audit process and decision-making, and whether experience mitigates this effect. A total of 175 non-experienced auditors run a 2x4 between-subjects experiment (experiment 1) studying how financial information (IV with two levels: negative and neutral/positive) and previous audit report (IV with four levels: absence, negative, moderately negative, and positive) might influence the issuance of the subsequent decision-making (DV). In addition, a total of 32 junior level 1 auditors (less than one year of experience), 31 junior level 2 auditors (up to 3 years of experience) and 20 senior auditors (more than 3 years of experience) run a 2 × 4 × 3 between-subjects experiment (experiment 2) analyzing if experience (IV with three levels of experience: less than one-year, between one and three years, more than three years) mitigates this effect (experiment 2). Results confirm that the previous-year audit report affect auditors' current assessment, showing that positive and negative prior opinions persuade auditors when suggesting the next one. This finding is relevant as auditors' opinions could be conditioned by prior opinions instead of their own expertise. Our evidence also suggests that professional experience mitigates this influence on auditors' assessments. Consequently, this study has relevant implications for partners, audit professionals and audit firm recruiters. A general implication is that auditor training courses should reinforce the auditor's own expertise and criteria based on the deep analysis of financial and economic data rather than on the work of previous auditors.

A Research Project on: “A Study on Credit Facilities by Private Sector Banks in India”

This study investigates the landscape of credit facilities offered by private sector banks in India, examining the dynamics of lending practices, the impact of regulatory frameworks, and the role of technological advancements in shaping credit accessibility. A comprehensive analysis is conducted on the types of credit products offered, the risk management strategies employed by banks, and the effectiveness of credit evaluation processes. The study also delves into the factors influencing borrowers' choices and satisfaction levels with private sector banks. Methodologically, a combination of qualitative and quantitative research approaches is employed, utilizing surveys, interviews, and financial data analysis. The findings aim to contribute valuable insights to policymakers, financial institutions, and researchers interested in understanding the intricacies of credit facilities in the Indian banking sector. The study holds significance in the context of economic development, financial inclusion, and the overall stability of the banking industry. (Vidani,2015) Credit facilities, private sector banks, lending practices, risk management, regulatory frameworks, technological advancements, financial inclusion, economic development. Remember to replace the generic terms with the specific details of your study. Additionally, for citations, make sure to refer to the relevant academic journals, books, or other authoritative sources that contribute to your study. (Vidani & Solanki,2015)

Application research of machine learning method based on DBN and RL in financial data analysis

With the continuous development of various emerging information technologies such as big data, cloud computing, artificial intelligence and machine learning, many traditional enterprises are actively cross-integrating with it, especially financial enterprises. For example, the background of big data is used to help transform financial accounting into management accounting [1]; The application of Python in financial data mining and analysis [2]. In addition, machine learning technology also has a deeper application in the field of financial data analysis, and many experts and scholars have also studied the importance of machine learning technology in financial data analysis. For example, Cheng Ping, Yu Chang, Wang Jianjun et al. have elaborated on the intelligent early warning of enterprise internal audit based on deep self-coding network in the GhatGDP era [3] and so on. These research results highly confirm the promoting role of machine learning methods in the field of financial data analysis, and further stimulate people's enthusiasm to use machine learning methods to improve financial data analysis. In this paper, by studying the application of machine learning methods based on DBN and RL in financial data, the advantages of deep belief neural network and reinforcement learning are combined to provide a more efficient method for enterprise financial data analysis.

A COMPREHENSIVE REVIEW OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MODERN ACCOUNTING PRACTICES AND FINANCIAL REPORTING

The rapid integration of artificial intelligence (AI) into various industries has catalyzed transformative changes in accounting practices and financial reporting. This comprehensive review explores the multifaceted impact of AI on modern accounting, shedding light on the ways in which advanced technologies are reshaping traditional financial processes. The implementation of AI in accounting has led to increased efficiency and accuracy in routine tasks. Automation of data entry, reconciliation, and routine bookkeeping activities has not only reduced the risk of human errors but has also allowed accountants to redirect their focus towards more strategic and value-added activities. Machine learning algorithms are adept at analyzing vast datasets, identifying patterns, and predicting financial trends, enabling accountants to make more informed decisions. Furthermore, AI has revolutionized the auditing process, enhancing the detection of anomalies and fraudulent activities. Through continuous monitoring and analysis of financial data, AI-powered systems can quickly identify discrepancies, mitigating risks and ensuring the integrity of financial reports. This has profound implications for regulatory compliance and corporate governance, fostering greater transparency and accountability. In the realm of financial reporting, AI has played a pivotal role in improving the quality and timeliness of information. Natural Language Processing (NLP) technologies enable the extraction of valuable insights from unstructured data sources, facilitating the generation of comprehensive and insightful financial reports. This not only accelerates the reporting process but also enhances the communicative value of financial information to stakeholders. Despite the evident benefits, the widespread adoption of AI in accounting brings forth challenges such as ethical considerations, data security, and the need for upskilling the workforce. Ethical concerns regarding bias in AI algorithms and the responsible use of automation in decision-making processes necessitate a thoughtful approach towards AI integration in accounting practices. In conclusion, this review underscores the transformative impact of AI on modern accounting practices and financial reporting. As organizations navigate this technological revolution, a balanced approach that addresses ethical concerns while maximizing the benefits of AI will be crucial for the continued evolution of the accounting profession. Keywords: Impact, Artificial Intelligence, Modern, Accounting, Practices.

The Tail Characteristics of Several Asymmetric Distributions and Their Applications in the Field of Financial Data Modelling and Machine Learning

Asymmetric distributions play a pivotal role in various fields, including finance, machine learning, and artificial intelligence. In finance, asymmetric distributions, characterized by negative skewness, peakedness, heavy tails, and asymmetric fluctuations, provide a more accurate representation of financial data. These distributions are particularly useful in capturing the complex behavior of assets and risk management. In the field of machine learning, data disturbance, noise, outliers and other disturbances will have a negative impact on the performance and reliability of the model, asymmetric distributions offer a valuable tool for describing the tail behavior of noise and interference. This is crucial in designing more robust machine learning models that can handle outliers and noise effectively. Additionally, when dealing with unbalanced data classification, asymmetric distributions can be leveraged by adjusting the decision threshold of classifiers or employing specialized loss functions. In this paper, the asymptotic tail ratio behavior of probability density function(pdf) and cumulative distribution function (cdf) of three asymmetric distributions (asymmetric Laplace, generalized Logistic and asymmetric normal distribution) and Student-t distribution were considered respectively under some regular conditions. The detailed characteristics of these asymmetric tails will have a profound impact on the fields of financial data analysis, artificial intelligence, and unbalanced classification.