Koncept integrisane analize finansijskog zdravlja globalnih korporacija

Financial statement analysis is an essential process that helps businesses, investors, and other stakeholders assess an organization's financial health, stability, and overall performance. It involves a detailed examination of financial data, including income statements, balance sheets, and cash flow statements, to gain insights into a company’s profitability, liquidity, and solvency. Various analytical techniques, such as ratio analysis, trend analysis, and comparative financial analysis, are applied to interpret financial information effectively. Ratio analysis helps measure key financial aspects like profitability, liquidity, and financial leverage, while trend analysis enables the identification of financial patterns and forecasting future performance. Comparative analysis allows businesses to benchmark their financial position against competitors and industry standards, ensuring they stay competitive in the market. This study aims to explore the significance of financial statement analysis, its methodologies, and its impact on decision-making. Financial analysis provides valuable information that aids in strategic planning, investment decisions, risk management, and overall business sustainability. By thoroughly understanding the financial strengths and weaknesses of a company, stakeholders can make well-informed strategic choices, optimize resource allocation, and improve operational efficiency. Additionally, financial analysis plays a crucial role in assessing the creditworthiness of businesses, enabling banks and financial institutions to determine loan eligibility and lending risks. Investors rely on financial statement analysis to evaluate the potential risks and returns associated with investments, ensuring that they make sound financial decisions. While financial statement analysis offers numerous benefits, it also comes with challenges, such as data inaccuracy, financial statement manipulation, and market fluctuations, which can impact the reliability of the analysis. Additionally, financial models can be complex, requiring expertise to interpret data accurately. The integration of advanced technologies, such as artificial intelligence and big data analytics, has improved the efficiency and accuracy of financial analysis, enabling businesses to make data-driven decisions with greater confidence. Furthermore, this study examines how financial analysis influences corporate sustainability and long-term business growth. Companies that implement strong financial analysis practices can better manage their cash flow, reduce financial risks, and enhance overall profitability. A well-structured financial analysis framework ensures transparency, regulatory compliance, and investor confidence, contributing to business stability. As financial analysis continues to evolve with technological advancements, businesses and investors must adopt modern analytical tools to enhance their decision-making capabilities Keywords : Financial statement analysis, financial health, business performance, investors, stakeholders, income statement, balance sheet, cash flow statement, ratio analysis, trend analysis, comparative analysis, profitability, liquidity, solvency, strategic planning, investment decisions, risk management, business sustainability, resource allocation, operational efficiency, creditworthiness, financial institutions. .

Financial analysis is an essential tool that helps businesses, investors, and analysts understand the financial health, stability, and overall performance of a company or investment. It involves carefully examining financial statements, such as balance sheets, income statements, and cash flow statements, to assess key financial aspects. By analyzing important financial ratios such as profitability, liquidity, efficiency, and solvency decision-makers can determine how well a business is performing and identify potential risks or opportunities. Additionally, financial analysis considers market trends, economic conditions, and industry comparisons to provide a broader understanding of a company's financial position. This process is crucial for making informed decisions, whether it's for investment purposes, business expansion, cost-cutting strategies, or risk management. However, financial analysis also comes with challenges, such as market fluctuations, changing regulations, and the risk of misleading financial data. By using structured methodologies, such as trend analysis, ratio analysis, and comparative analysis, businesses and investors can make better financial decisions and ensure long-term success. Keywords: Financial analysis, financial health, stability, performance, investment, financial statements, ratios, profitability, liquidity, efficiency, solvency, market trends, decision-making, risk management, trend analysis, ratio analysis, comparative analysis, business expansion, economic conditions, industry comparisons

ABSENT fiat, the form of organization that survives in an activity is the one that delivers the product demanded by customers at the lowest price while covering costs.1 Our goal is to explain the survival of organizations characterized by separation of "ownership" and "control"-a problem that has bothered students of corporations from Adam Smith to Berle and Means and Jensen and Meckling.2 In more precise language, we are concerned with the survival of organizations in which important decision agents do not bear a substantial share of the wealth effects of their decisions. We argue that the separation of decision and risk-bearing functions observed in large corporations is common to other organizations such as large professional partnerships, financial mutuals, and nonprofits. We contend that separation of decision and risk-bearing functions survives in these organizations in part because of the benefits of specialization of

Purpose: This paper aimed to analyse the introduction of financial analysis and risk management for internal decision-making in the Malaysian public sector and determine the limitations/ gaps as well as areas for improvement. Design/ Methodology/ Approach: The paper was based on the analysis of documents, government circulars and guidelines pertaining to financial statement analysis and risk management, including interviews with officers at two ministries. Findings: Policy and guidelines were established for financial analysis and risk management in the Malaysian public sector. However, given the complexities and unique context of each ministry, there is a need for clearer and more context-specific guidelines regarding financial and risk analysis. It is also crucial that the conducted financial and risk analyses are relevant and meet the specific needs of public sector entities. The usefulness of information relies on a comprehensive understanding of the context and types of decision needed. Therefore, fostering collaboration and engagement between the management accountant unit and other key units within a ministry is crucial to conduct an effective financial and risk analysis. Originality/ Value: This study offered insights into the financial and risk analysis reporting framework and its implementation in the Malaysian public sector. Keywords: Management accounting, public sector accounting, financial statement analysis, risk management, public sector accountants.

Capital risk allocation is an important problem in corporate, financial and insurance risk management. There are two theoretical aspects to this problem. The first aspect consists of choosing a risk measure, and the second consists of choosing a capital allocation rule subordinated to the risk measure. When these two complementary aspects are settled, the problem is reduced to a computational problem. No universal prescription for choosing a risk measure exists, let alone a subordinated capital allocation rule. However, expert opinion may be expressed as a range of capital to be allocated to each risk. For example, the managers of each line of activity of a corporation or bank may have a way to estimate a minimum and maximum losses with high confidence. In that case, one is confronted with two interesting numerical problems. First, to devise a method to allocate risk capital within the bounds provided by the expert. Second, to use the upper bounds as stand-alone risk prices to determine a risk measure, which can then be used to compute the prices of other risks. The risk measure could also be used to compute a range for the capital allocations of a new collection of risks, to which our numerical procedure to allocate risk capital can be applied. The aim of this paper is to use a model-free, nonparametric approach based on the method of maximum entropy in the mean to solve both problems and to examine how they are intertwined. In this way we circumvent the problem of choosing between risk valuation and risk pricing methods, and between risk capital allocation rules consistent with them. Also, the maximum-entropy-based methodology provides us with a method to determine coherent risk measures consistent with the bounds provided by expert opinion or management. The risk measure provides us with a method to determine either risk prices or bounds to be used for capital allocation for a different collection of risks.

PT Sangyo Jaya Abadi (PT SJA) is a subsidiary of PT Kuroshio Jaya Abadi that uses Computer Numerical Control (CNC) machines and Digital Tracking of Production as its business development. The establishment of PT SJA to make it more focused, facilitate proper planning, and creates efficiency in controlling future operational activities, as well as create a brand image by expanding market share and improvement in the face of the Industrial Revolution 4.0. Therefore, PT SJA conducts financial and risk analysis by performing financial calculations and analysis as well as looking at the risks that will occur. The method used is financial planning contained in the framework and risk management using the ISO 31000:2018 approach. From a financial perspective, the analysis carried out includes an investment feasibility analysis and financial performance analysis as a basis for knowing the prospects of the investment plan that underlies decision making for investors, to determine whether the investment can be accepted or rejected. In terms of risk, the analysis carried out includes identification, evaluation, and risk management. The results of the financial analysis show that PT SJA's investment can be made. The results of investment analysis can assist management in making strategic decisions, and the results of risk management analysis can reduce or prevent risks that will occur so that the business can run well.

Risk aggregation and capital allocation are of paramount importance in business, as they play critical roles in pricing, risk management, project financing, performance management, regulatory supervision, etc. The state-of-the-art practice often includes two steps: (i) determine standalone capital requirements for individual business lines and aggregate them at a corporate level; and (ii) allocate the total capital back to individual lines of business or at more granular levels. There are three pitfalls with such a practice, namely, lack of consistency, negligence of cost of capital, and disentanglement of allocated capitals from standalone capitals. In this paper, we introduce a holistic approach that aims to strike a balance of optimality by taking into account competing interests of various stakeholders and conflicting priorities in a corporate hierarchy. While unconventional in its objective, the new approach results in an allocation of diversification benefit, which conforms to the diversification strategy of many risk management frameworks including regulatory capital and economic capital. The holistic capital setting and allocation principle provides a remedy to aforementioned problems with the existing two-step industry practice.

Risk aggregation and capital allocation are of paramount importance in business, as they play critical roles in pricing, risk management, project financing, performance management, regulatory supervision, etc. The state-of-the-art practice often includes two steps: (i) determine standalone capital requirements for individual business lines and aggregate them at a corporate level; and (ii) allocate the total capital back to individual lines of business or at more granular levels. There are three pitfalls with such a practice, namely, lack of consistency, negligence of cost of capital, and disentanglement of allocated capitals from standalone capitals. In this paper, we introduce a holistic approach that aims to strike a balance of optimality by taking into account competing interests of various stakeholders and conflicting priorities in a corporate hierarchy. While unconventional in its objective, the new approach results in an allocation of diversification benefit, which conforms to the diversification strategy of many risk management frameworks including regulatory capital and economic capital. The holistic capital setting and allocation principle provides a remedy to aforementioned problems with the existing two-step industry practice.

With the rapid development of big data technology and the widespread use of the Python programming language in data science, financial analysis is gradually shifting from traditional manual calculations and analysis to automated and intelligent approaches. This paper explores the application of Python in financial analysis within a big data environment, examining its specific uses in data collection, processing, analysis, and the automation of financial reporting. First, it introduces the advantages of Python and big data technology, along with their integration methods. Next, it outlines the basic concepts and common techniques of financial analysis. Then, it delves into Python’s applications in financial analysis, including data preprocessing, financial statement analysis, predictive modeling, and risk management. Through case studies, the paper demonstrates how Python enhances the efficiency and accuracy of financial analysis for businesses. Finally, it summarizes the current state of Python’s application in financial analysis, addressing challenges such as data quality issues and the integration of artificial intelligence, while also exploring future trends. This study provides practical guidance for professionals in the financial sector and offers insights for future research.

With the rapid development of big data technology and the widespread use of the Python programming language in data science, financial analysis is gradually shifting from traditional manual calculations and analysis to automated and intelligent approaches. This paper explores the application of Python in financial analysis within a big data environment, examining its specific uses in data collection, processing, analysis, and the automation of financial reporting. First, it introduces the advantages of Python and big data technology, along with their integration methods. Next, it outlines the basic concepts and common techniques of financial analysis. Then, it delves into Python’s applications in financial analysis, including data preprocessing, financial statement analysis, predictive modeling, and risk management. Through case studies, the paper demonstrates how Python enhances the efficiency and accuracy of financial analysis for businesses. Finally, it summarizes the current state of Python’s application in financial analysis, addressing challenges such as data quality issues and the integration of artificial intelligence, while also exploring future trends. This study provides practical guidance for professionals in the financial sector and offers insights for future research.

Petroleum exploration companies are confronted regularly with the issue of allocating scarce capital among a set of available exploration projects, which are generally characterized by a high degree of financial risk and uncertainty. Commonly used methods for evaluating alternative investments consider the amount and timing of the monetary flows associated with a project and ignore the firm`s ability or willingness to assume the business risk of the project. The preference-theory approach combines the traditional means of project valuation, net present value (NPV) analysis, with a decision-science-based approach to risk management. This integrated model provides a means for exploration firms to measure and to manage the financial risks associated with petroleum exploration, consistent with the firm`s desired risk policy.

Technological advancement has exposed companies to various risks. With the adoption of technological infrastructures in many companies, various processes have been rendered vulnerable to different forms of threats. Evidence from the current empirical studies on organizational management indicates effective risk assessment is a crucial aspect in any organization. Global technological advancement has equally redefined risk assessment and management strategies. Global leading technology companies such as Apple Inc., Amazon Inc., and Google are leveraging modern technology to unlock hidden data to aid in risk assessment and management. As evidenced in this research report, the use of big data has revolutionized risk assessment in many companies across the world. Big data technology has enabled organizations to collect, store, and assess huge information to aid in risk assessment and management. The pro-cesses of risk identification, assessment, mitigation, monitoring, and reporting have been redefined due to the adoption of big data analytical technology. As a result, this research reviews the specific roles played by big data in organizational risks management. The research carries out a comparative case study analysis among three companies that utilize big data in risk management. Specifically, the roles of big data technology in risk management at Apple Inc., Amazon Inc., and at Google are identified. Results from this comparative analysis are used in formulating recommendations for various organizations that desire to adopt big data analytical technology in risk management.

This research seeks to compare and merge risk management with financial analysis within project management to determine how the integration impacts project behavior and performance in different industries. The empirical study of the research is based on a multiple case study methodology and covers the construction industry project, the IT project, the infrastructure project, as well as two energy sector projects, with an emphasis on the application of tools and techniques implementing the risk and financial functions. The research establishes the positive correlation between risk management and financial analysis and shows that integrated implementation of these techniques leads to better cost management, more accurate project cost estimation, increased control of project risks and, therefore, improved results compared to planned objectives. Some examples include the Highway Expansion (construction) and the Oil Refinery (energy), which show how techniques such as Monte Carlo simulations and sensitivity analysis coupled with scorecards aid early decision-making and resource management for the achievement of improved results. On the other hand, such projects as the Software Development (IT) case study indicated that poor integration of risk management practices was characterized by financial overruns and project delays. The research discusses the issue of cultural resistance, tool sophistication, and data quality, all of which constrain the integration of these functions. In sum, the results underscore the necessity for an integrated approach to risk and financial management, especially in mega-high-risk projects, and provide directions for the applications of the tools for achieving it.

New Risks: Issues and Management

The subject of research of this work is the current direction in the financial activities of small industrial enterprises – financial express analysis. The purpose of the study is to develop a scientific and methodological basis for a simplified procedure of financial supervision of small industrial enterprises in the country in order to provide qualified assistance to managers of small industrial enterprises in the financial sphere of their activities. The article solves the following tasks: study of the state of small industrial enterprises and their role in the development of Ukraine's economy, definition of "financial supervision", development of algorithm for financial express analysis of small industrial enterprises, choice of directions for financial express analysis of small industrial enterprises financial supervision, the formation of a bank of input data on four components, testing of scientific and methodological support on the example of a particular enterprise. The following methods are used: theoretical generalization, retrospective analysis, comparative analysis, analytical, analysis and synthesis, rapid analysis. The following results were obtained: the method of financial express analysis of a small industrial enterprise was proposed on the basis of financial supervision; selected areas of financial analysis under a simplified procedure; the scheme of algorithm of carrying out procedure of financial supervision is developed; the bank of the input data which are necessary for the decision of the set task is formed; substantiation of the choice of applied mathematical models is carried out; the experimental approbation of the offered scientific and methodical approach to carrying out the financial express-analysis on the factual basis of the real small industrial enterprise is carried out; appropriate recommendations were given to the management of the researched enterprise. Conclusions: It is determined that in the current unstable economic conditions, small industrial enterprises need professional assistance of consulting orientation in financial activities. Obtaining up-to-date relevant information on the financial condition of small industrial enterprises is a very important task that requires a rapid financial analysis of their activities. The study of this issue showed the lack of consensus of analysts on the directions and methods of financial rapid analysis. This led to the need and feasibility of developing for the management of small industrial enterprises under a simplified procedure of an orderly methodology of financial rapid analysis on the basis of financial supervision.