Intelligent Architecture for Comparative Analysis of Public Companies Using Semantics and XBRL Data

This study dealt with the Extended Business Reporting Language ‘its definition ‘and its importance. The study aimed to know the impact of applying the Extensible Business Reporting Language (XBRL) on the quality of annual financial reports from external auditors' view ‘for public shareholding companies listed on the Amman Stock Exchange. To achieve the objective of the study ‘the researcher has distributed questionnaires to (395) of the external auditors, and the number of responded of the questionnaire (179). In this study ‘‘the researcher used a set of statistical methods in the SPSS. Among the findings of this study there is knowledge to external auditors towards the application of Extensible Business Reporting Language, and that there is an impact of the applying Extensible Business Reporting Language on the quality of annual financial reports. In addition, there is an effect of applying Extensible Business Reporting Language on the dimensions of the quality of the annual financial reports, such as financial and non-financial data, in addition to transparency and reliability, as well as its impact on reducing the costs of information dissemination. In addition, this study recommended to management of ASE to continue preparing workshops and training courses for external auditors to raise awareness of the disclosure using the Extensible Business Reporting Language. In addition, the commitment of companies listed on the ASE to disclose all its data using the Extensible Business Reporting Language, because it has significant impact to raising the level of quality of annual financial reports. Finally, increasing the studies and research on the topic of Extensible Business Reporting Language from researchers due to the importance of this area, and its recent applied in Jordan.

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

Modern critical care medicine is based on flow of information. This information requires significant amount of interpretation to become clinically valuable. Despite the wealth of data in the modern Intensive Care Units (ICUs), intensivists often rely on fragmentary information. In an attempt to improve data trending utilization, application of financial analysis (FA) methods to vital sign data samples was examined. Two randomly chosen vital sign datasets of patients who spent at least 30 days in the ICU were retrospectively reviewed. Hourly vital sign data were retrieved and recorded for each patient. Variables tracked included systolic blood pressure for patient #1 and heart rate for patient #2. These variables were then entered into specialized FA software and subjected to computer-based processing. Trends in the recorded data were examined using (1) the stochastic oscillator (SO), (2) the moving average (MAV) convergence-divergence tool, (3) price envelope analysis and (4) MAV analysis. Both blood pressure and heart rate analyses demonstrated that vital sign data could be successfully trended using FA techniques. Not only was the vital sign data easy to read and interpret when formatted in financial-like fashion, but some trends that were not apparent on gross inspection of the numeric data were clearly demonstrated on FA. Much like with financial patterns, trends noted within vital sign data appeared to be more significant when more than one indicator identified them, utilizing the concept of a confirmatory variable. Vital sign data, much like financial data, were subject to trend reversals. Such reversals in vital sign data appeared to follow rules similar to those followed by financial vehicles and markets. This report demonstrates that vital sign data can be subjected to the same manipulations as financial market data. Furthermore, FA tools appear to provide the interpreter of the data with means to define, confirm, and possibly predict trends and trend reversals.The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.Republished with permission from: Stawicki SP. Application of financial analysis techniques to vital sign data – A novel method of trend interpretation in the Intensive Care Unit. OPUS 12 Scientist 2007;1(1):14-16.

Reinforcement prompting for financial synthetic data generation

With the rapid development of digital economy, enterprises need to make financial management activities and development decisions based on a large number of financial data and information. This also exposes the shortcomings of the traditional financial analysis and processing mode in adaptability, functionality and efficiency. In this regard, based on the actual needs of enterprises, this paper will put forward a set of construction scheme of enterprise financial data visualization analysis system, give full play to the practical application advantages of data mining technology in the era of big data, change the original processing methods, and provide an efficient, reliable and convenient new way for enterprise financial data analysis. The whole system adopts B/S architecture design, the client design is based on VUE framework, and the server construction is based on Django framework, and the financial data is analyzed, mined and visually displayed by using Numpy, Pandas, Matplotlib and other class libraries in Python environment. Practice has proved that the system can support users to call K-means, Decision Tree, Logistic and other algorithm models online for multi-dimensional analysis of financial data, which realizes an important supplement to traditional financial analysis, and achieves the purpose of improving financial management efficiency and assisting enterprise development decision-making.

In many practical problems, it is important to consider different distributions that could be used to model a data set. In this work, we analyze the generalized trapezoidal (GT) model in financial application. The primary reason for this is that the family of the GT distributions includes models with bounded domain used in risk analysis, and it belongs to the nonparametric class of log-concave densities under determined parametric restrictions. In the literature, one can find several references on the log-concavity and applications with interesting qualitative implications in many areas of economics, actuarial sciences, biology and engineering. Here, we classify the log-concavity of the GT model based on its parameters. We observe that it can be useful in analyzing some data sets, especially a financial market data example is used to illustrate that the GT distribution is better fitting than the symmetric distributions previously considered for this data set. Furthermore, in the particular example, the fitted GT distribution satisfies the log-concavity constraints.

This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.

The study aimed to know the effects of analysis of financial statements on financial decisions, and the degree of benefit from them, and to identify what financial statements, what is its importance for the institutions within the framework of the Kingdom's Vision 2030 of ideas and trends, and to identify the contribution of financial statement analysis to financial decision-making. The study was based on the descriptive and analytical approach, and the study population consisted of all financial decision makers. The study was based on a simple random method (70) of financial decision makers. The study was based on the questionnaire and consisted of the following axes (financial statements in companies, financial decision-making, the effects of analysis of financial statements on financial decision-making). The study came out with a number of results, the most important of which are: There is approval by the respondents to all paragraphs of the first axis "financial statements in companies", with a relative weight of 82.8%. There is an agreement by the respondents on all paragraphs of the second axis "making financial decisions in companies", with a relative weight of 81.3%. There is strong approval by the respondents on all paragraphs of the third axis "the effects of analysis of financial statements on financial decision-making", with a relative weight of 86.4%. The financial statements are a key tool to know the financial position of the company, so they must be accurate and reliable before being published by management. The lack of credibility in the financial statements leads to mistrust in the company by investors, and does not give them the possibility to diagnose and make sound decisions. In light of the previous results, the study recommended the following: • Organizing several forums, conferences and forums to clarify the mechanism of preparing the financial statements and how to analyze them, and the need to raise awareness of financial decision makers about the importance of financial statements in the financial decision-making process.

This study explores the impact of Strategic Financial Management and Financial Planning and Analysis on Competitive Advantage within Bank Syariah Indonesia KC Ternate, with a focus on the mediating role of Financial Decision-Making Quality. Utilizing a quantitative research design, data was collected from 70 customers through structured questionnaires and analyzed using Smart PLS (Partial Least Squares). The findings reveal significant direct effects of Strategic Financial Management and Financial Planning and Analysis on Competitive Advantage, with coefficients of 0.50 and 0.32, respectively. Additionally, the analysis uncovers significant indirect effects where both Strategic Financial Management and Financial Planning and Analysis influence Competitive Advantage through improved Financial Decision-Making Quality, with coefficients of 0.25 and 0.21, respectively. These results underscore the critical role of high-quality financial decision-making in enhancing the competitive advantage of the bank, highlighting the need for effective strategic financial management and planning to drive organizational success.

Decision support systems use statistical and computational techniques. These statistical and computational techniques are used to predict the outcomes when it is applied on financial data. This chapter discusses the time series forecasting and predictive analytics within the financial domain. This work will cover the ARIMA method and deep learning methods. These methods will facilitate forecasting future through the analysis of financial time series data. The outcomes generated by these methods will help the financial managers and other professionals in the financial domain to improve their results. This work investigates predictive analytic techniques in the finance domain and computational algorithms to forecast the future trends.

Within over a decade of liberalisation of the Greek telecommunications market and given the current adverse conditions caused by the economic crisis, a limited number of companies managed to survive and relatively stabilise their market position. On one hand, this paper constitutes a comprehensive financial analysis of the Greek telecommunications companies that are currently listed in the Athens Stock Exchange. The respective companies are: Hellenic Telecommunications Organization (OTE), Forthnet, and Hellas Online (HOL). These companies’ financial data over the past five years, from 2008 to 2012, are analysed using universally accepted methods. On the other hand, this work presents a relatively original approach of analysing companies’ survivability, by combining the four widely accepted distress prediction models of Altman, Springate, Ohlson, and Zmijewski. In this manner, criticism on the credibility and applicability of this model on the specific field is also provided.

The present article aims to analyze how the imbalances generated by the economic and medical crisis caused by the COVID 19 pandemic in 2020 were reflected in the balance and economic performance at the microeconomic level. Two companies operating in two of the most affected economic areas in 2020, Tourism and Pharmaceutical Industry, represented the foundation of our analysis. Accounting data from the Balance Sheet and Profit and Loss Account, available on the website of the Bucharest Stock Exchange, were the main tools for analyzing the impact that the economic and medical crisis had on indicators such as Working Capital, Self-financing Capacity, Current Ratio, Debt/Equity Ratio, Return on Assets, Return on Equity, etc. The period under consideration covers a period of 4 years, i.e., 2018-2021. The two areas of activity represent, in our opinion, two major poles of the effects of the COVID 19 pandemic, being the loser and the winner (in financial terms) of the economic and medical developments generated by these. The questions we propose to answer based on the financial analysis are: could these two companies also be considered the winner and the loser of 2020 crisis? And what is the story told by the financial data about a company's vulnerability during the crisis?

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.

An Overview of Financial Statement Analysis: The Mechanics

This article examines the brand-name effect of airline crashes. These disasters raise several financial issues. A crash represents the loss of a plane, the loss of lives, the loss of scheduling capacity, higher insurance costs, and the potential loss of consumer goodwill. This article focuses on the last of these. Are consumers reluctant to fly with airlines that have poor safety records or do they treat crashes merely as random events that bear no reflection on the quality of the airline?