Financial Indicators Research Articles

A Comprehensive Study of Techno Economic Viability (TEV) Report of Various Projects

This paper provides a comprehensive analysis of the TEV process, focusing on its application to both greenfield and brownfield projects across various sectors, including manufacturing, infrastructure, energy, and services. The study aims to guide decision-makers by bridging technical assessments with financial analysis to establish project profitability and sustainability. A Techno-Economic Viability (TEV) Study serves as a crucial tool in the decision- making process for both lenders and investors. It offers a detailed assessment of a project's technical feasibility, economic viability, and overall sustainability. It delves into the dual aspects of technical feasibility and economic viability, emphasizing their roles in influencing investment decisions across a wide spectrum of industries, including manufacturing, infrastructure, energy, and services. The findings from this research also illustrate real-world applications, highlighting two case studies with an emphasis on key financial indicators such as the Debt Service Coverage Ratio (DSCR) and Internal Rate of Return (IRR), which are used to measure a project’s ability to meet debt obligations and generate profits. Keywords: Techno Economic Study, Financial Viability, Technical Feasibility, Financial Modelling, Risk Analysis, Investment Decision

SIEMES DIGITAL TRANSFORMATION CASE STUDY TOWARDS ORGANIZATIONAL SCALE AND SUCCESS IN ELECTRICAL ENGINEERING INDUSTRY”

This study investigates a range of scaling strategies for electrical engineering firms, with a focus on various crucial aspects such as internal innovation, strategic regional expansion, geographical proximity, and collaborative partnerships. Internal innovation is highlighted as a key driver of growth, as it allows firms to develop new technologies and processes that can differentiate them from competitors. Strategic regional expansion is discussed as a means to tap into new markets and leverage local opportunities, while geographical proximity is examined for its impact on operational efficiency and customer relationships. Collaborative partnerships are also emphasized for their role in enhancing capabilities and driving mutual benefits. The case study of Siemens AG serves as a prominent example, showcasing how digital technologies like the Internet of Things (IoT), artificial intelligence (AI), and cloud computing have significantly transformed their approach to scaling. Siemens AG's experience underscores how these technologies can support internal innovation, streamline regional expansion efforts, overcome geographical barriers, and foster effective collaborations. The article proposes a detailed framework in which digital transformation is central to driving these strategies. It argues that digital technologies are not just enablers but essential components that fuel innovation across various domains. Additionally, digital transformation is posited as a critical factor in facilitating smoother and more effective expansion into new regions, bridging geographical gaps that can otherwise impede growth, and enhancing collaborative efforts both within and outside the organization. By integrating these elements, businesses can achieve sustainable growth and maintain a competitive edge in an ever-evolving and challenging market landscape.is considered near the evaluation of efficiency of management an enterprise. Evaluation technology is realized on the basis of comparison of financial indexes, in particular to the analysis of indexes of profitability and analysis of business activity

Decoding the ‘Social’ in ESG

Traditionally, a company’s financial performance indicators, such as revenue generating capacity, profit margins, and inventory turnover were the driving force behind a company’s attractiveness as an investment opportunity. The rising popularity of environmental, social and governance (‘ESG’) norms in the last decade however has seen a shift in investors’ risk appetite – investors are moving away from archaic definitions of performance indicators and assessing an investee’s impact in terms of ESG metrics.While there is clarity on what factors constitute ‘environment’ and ‘governance’ principles, the ‘social’ metric continues to remain shrouded in mystery. In this article, we discuss the factors which form the building blocks of the ‘social’ metric as well as review the yardsticks available to measure this metric, particularly from an Indian standpoint.

Technological and financial development as drivers of Latin America's energy transition

Transitioning to clean energy is a challenge for Latin American countries whose socio-economic realities require economic development and environmental sustainability to be carefully balanced. To drive this energy transition, it is essential that funding and technological innovation align with local contexts. This study aims to assess the implementation of the energy transition in Latin America from 2015 to 2020. To do this a performance indicator is developed that uses Data Envelopment Analysis (DEA) and employs data on areas such as patents and financial development as inputs. Subsequently, a Fractional Regression Model (FRM) is used to explore the socio-economic factors influencing this performance and examine its determinants. The results demonstrate significant variations in energy transition outcomes across the sample, with Brazil and Peru consistently achieving maximum DEA scores. Financial development was found to attract capital for the energy transition, with Brazil leading in the Financial Markets Development Index during the period studied. High population density was found to negatively impact energy transition across all FRM model specifications. Negative correlations between gross fixed capital formation and the level of energy transition suggest that there is lower investment in renewable energy in countries with less efficient transitions. Additionally, investment in R&D was only positively and statistically significant in efficient countries.

International financial integration and financial stress of emerging market economies: The role of institutional quality

In the wake of unprecedented financial liberalization policies adopted by Emerging Market Economies (EMEs) since the late 20th century, their integration into global financial markets has brought forth both opportunities and challenges. This paper endeavors to assess the intricate relationship between international financial integration (IFI) and financial stress in EMEs, focusing on the potential moderating effect of institutional quality. Addressing key questions, it examines how various IFI indicators impact financial stress and evaluates the role of different dimensions of institutional quality in influencing this relationship. Using a Financial Stress Index (FSI) and employing the panel threshold regression technique, the study unveils significant threshold effects of institutional quality on the IFI-FSI nexus, with variations across different institutional factors. This study underscores the vital need for policymakers to identify threshold levels in institutional quality indicators to strike a balance between attracting investments and preventing unwarranted financial distress.

The impact of new VAT enforcement on financial performance: Evidence from Saudi Arabia non-financial listed companies using the event study and ARMA model

This paper examines the influence of the new 15 % Value Added Tax (VAT) enforcement on non-financial listed companies in Saudi Arabia. By comparing financial data from 2019, before the VAT implementation and the COVID-19 pandemic, with data from 2020, during both the VAT increase and the pandemic, the research aims to uncover the consequences of this tax policy change. Utilizing charts, tables, and an event study analysis approach with the Autoregressive Moving Average (ARMA) model, we investigated key financial indicators such as Shareholders' Equity (SE), Total Income (TI), Total Revenues (TR), Net Income (NI), Total Expenses (TE), Other Changes in Operating Activity (COA), and Cash at the End of the Period (CEP). The findings reveal significant financial impacts, highlighting companies' challenges due to the increased tax burden. Shareholders' Equity dropped from an average of 11.55 million to 4.57 million, and Total Income and Total Revenues decreased from approximately 1.73 million to 316,234 and from 1.95 million to 331,605, respectively. Net Income sharply declined from 406,109 to 74,624, and Total Expenses decreased from about 1.18 million to 223,495. Other Changes in Operating Activity shifted from a negative mean of −168,936 to a positive 523,520, and Cash at the End of the Period fell from 1.99 million to 666,663. These results suggest that the VAT increase has significantly strained companies' financial performance, emphasizing the need to consider such tax reforms carefully, particularly in developing countries. The study concludes that while the VAT increase aims to enhance government revenues, it imposes substantial financial strain on companies, potentially leading to long-term economic repercussions. Policymakers should consider these implications and implement practical measures to optimize the VAT system and support business resilience in the face of such reforms.

Assessment of regional financial risk based on an integrated approach

Subject. The article considers a comprehensive model to assess the financial risk of the region. Objectives. The aim is to develop a financial risk prevention model that integrates economic, social, political, environmental, and financial indicators, having a significant impact on the economy. Methods. The study rests on methods of synthesis, analysis, comparison, and economic and mathematical calculations, i.e. weighted summation with preliminary normalization of each estimated indicator. The process of data collection, normalization, selection of indicator weights, and calculation of integral indicator of financial risk was implemented for 2022. Results. We established the presence of financial risks in the region. Both the results and the model itself can be useful for further research. The advantage of the model is the relative small number of applied indicators. This facilitates the collection of information, its processing and evaluation. The evaluation of the above calculations reflects the conditionality of ranking. The results that reflect dynamics are more indicative. Conclusions. The formed model, when assessing regional financial risk, enables to take into account the multidimensionality of modern regional economies.

Analysis of Financial Performances for Coca-Cola: Comparison with PepsiCo, Keurig Dr Pepper and Monster Beverage

In the global beverage market, Coca Cola, PepsiCo, Keurig Dr Pepper Inc., and Monster Beverage Corporation are the four giants whose financial performance has a profound impact on the entire industry. This study aims to analyze in depth the market performance and financial health of these companies by comparing their financial indicators. Research has found that Coca Cola Company has outstanding performance in profitability, asset utilization efficiency, and cost control, while PepsiCo, despite performing well in certain indicators, has a negative expected earnings per share growth rate, which has attracted attention. Monster Beverage Corporation shows high market expectations, but attention should be paid to the risks that its high P/E ratio may bring. Keurig Dr Pepper Inc. has relatively weak profitability, but its gross profit margin trend shows signs of improvement. This study not only provides investors and analysts with a comprehensive perspective on the financial health of major beverage companies, but also provides important references for understanding their strategic decisions and market performance, which can help make more informed investment decisions.

Assessing the impact of environmental component on the development of Russian banks applying ESG policy

Subject. The article discusses operations of steadily developing Russian banks in the context of environmental component. Objectives. The aim is to identify patterns in functioning of Russian banks, applying ESG policy in their activities, under the influence of environmental factors, to improve the management of their development. Methods. The study employs regression analysis. We selected 26 Russian banks based on ESG ratings, formed an empirical base for 2011–2020 from open sources (financial and economic indicators and macro-level indicators characterizing the environmental impact by the activities of economic entities), built a linear regression model in MS Excel. Results. The constructed regression model helped reveal that the indicator “Recycling and disposal of production and consumption waste in the Russian Federation” has a significant positive impact on the pretax profit of banks applying ESG policy. However, a negative trend in the impact of banks on the environment was identified in terms of connection between the resulting indicator and the indicator “Primary energy intensity level”. This suggests that the internal capacity of banks to implement the environmental component is not sufficiently developed and used. Conclusions. The findings can be used both at the macro and micro level when generating the strategy and tactics for ESG banking development. The offered regression modeling technique can be used to analyze the role of environmental component in the activities of banks in other countries, which implement ESG policy.

KAZAKHSTAN'S HEALTHCARE SYSTEM IN THE CONTEXT OF THE INTRODUCTION OF COMPULSORY SOCIAL HEALTH INSURANCE

This article is devoted to the analysis of the Kazakhstani healthcare system in the context of the implementation of the compulsory social health insurance system. It will examine issues related to healthcare costs, the degree of satisfaction of the population with health insurance, the quality of medical services, etc. Since its introduction in 2020, the healthcare insurance system in Kazakhstan has encountered significant challenges. These include public dissatisfaction with the quality of medical services, inadequate coverage of the population with health insurance, vulnerabilities in the insurance information system, the existence of medical supplements, underdeveloped infrastructure in rural areas, and information asymmetry in the health insurance sector. President of Kazakhstan, Kassym-Jomart Tokayev, criticized the reform process of the health insurance system and addressed these issues during a meeting with the Government on February 7, 2024. This article delves into the evolution of the Kazakhstan health insurance system by analyzing financial indicators and monitoring data. Through this analysis, problematic areas were identified, and practical recommendations were developed to optimize the compulsory social health insurance system. Various scientific methods were employed in this study, including institutional analysis, synthesis, literature review, comparative and contrastive, and sociological methods. The goal of this study is to provide recommendations for enhancing the healthcare system in Kazakhstan during the implementation of compulsory social health insurance. The empirical foundation of this study consists of legislative documents in the healthcare sector, official publications, and analytical and statistical reports from the Ministry of Health of the Republic of Kazakhstan. The hypothesis of the study is that the introduction of compulsory social health insurance in Kazakhstan is intended to promote positive changes in the healthcare system, but due to existing organizational and financial barriers does not improve the availability and quality of medical services. Keywords: health, health insurance, health care system, public administration, health care costs.

Relationship between return on invested capital and market value of oil and gas companies

Subject. The article addresses approaches and methods to calculate the rate of return (profitability) on invested capital of oil and gas companies based on financial statements, taking into account the stock market conditions. Objectives. The study aims at reconciliation (elimination of gaps) of market value indicators and financial indicators of oil and gas companies, using the economic value added model. Methods. We apply methods of forecasting, financial and factor analysis, and statistical methods on the basis of financial statements figures of the studied companies. Results. We analyzed the dynamics of profitability indicators and cost of invested capital for oil and gas companies in comparison with their market capitalization for 2015–2022, identified key factors influencing changes in ROIC, offered an adjusted approach to calculating the indicator, including the initial cost of invested capital. We tested the methodology and provided explanation of its practical significance, using the case of historical financial data and market capitalization of ExxonMobil. Conclusions. The study enabled to confirm the hypothesis that it is necessary to calculate the cost of fixed assets by the amount of total actual investments, net of accumulated revaluations to calculate the return on invested capital. In the numerator of the formula for this indicator, it is advisable to use profit before interest expenses, depreciation, income tax, and exploration, less investments to maintain current assets, and in the denominator – invested capital net of cash, current and non-current non-debt liabilities.

Функционирование умного сельского хозяйства в АПК городской агломерации

The article describes the possibilities and feasibility of using digital technologies in agriculture; population growth trends in the world and the need to use smart technologies to ensure increased labor productivity in the industry. The article identifies barriers to the implementation of digitalization in agriculture: lack of accessible infrastructure (broadband and incomplete Internet coverage in the re-gions); partial use of digital technology capabilities; lack of financial resources for the implementation of technologies; distrust of digital technologies; the presence of a large number of reporting forms in the industry; lack of information on digital technologies and the results of their use; low qualification of personnel in the field of digital technologies. The article reflects digital technologies for the formation of smart agriculture in the agro-industrial complex in modern conditions. The produc-tion and financial indicators of the Krasnodarskoye educational farm of the Kuban State Agrarian University over the past 5 years are analyzed as successfully demonstrating the experience of functioning of smart agriculture in the agro-industrial complex of the Krasnodar urban agglomeration.

US monetary policy and business cycles in developing countries

The article examines modeling the impact of news shocks of US monetary policy on developing countries. The study analyzes the transmission mechanism of the Federal Reserve's interest rate news shock. The authors argue that the volatility of macroeconomic indicators in emerging economies can be explained by changes in monetary policy in other countries, and highlight the main channels of influence: financial sector indicators and trade indicators. In this paper the authors first use the LSDV-estimator of the panel vector autoregression and identify unexpected and news shocks. To achieve this goal, quarterly data was collected from the first quarter of 1998 to the third quarter of 2017 for Russia, along with data starting from the first quarter of 1995 for several developing countries. The results obtained indicate that the expected tightening of US monetary policy leads to a decrease in output, investment, and weakening of exchange rate in Russia. The monetary news regarding an increase in the US Federal Reserve's interest rate in two quarters also results in a short-term increase in trade balance and interest rates. News about future economic developments affects macroeconomic indicators up to two quarters before the implementation of monetary policy changes. Additionally, the news shock accounts for up to 11% of the variation in macroeconomic indicators in Russia. Accordingly, the monetary policy news shock is found to be as significant as an unexpected shock. The paper concludes with the analysis of the role played by various financial and trade channels.

Global uncertainty and exchange rate conditions: Assessing the impact of uncertainty shocks in emerging markets and advanced economies

Economic and financial conditions respond significantly to increased measures of uncertainty, particularly in economic policy, monetary policy, and financial markets. This research employs the global vector autoregression (global VAR) model to assess the response of bilateral exchange rates, exchange rate volatility, exchange market pressure, and nominal and real effective exchange rates to shocks in global economic and financial uncertainty, along with U.S. monetary policy uncertainty, U.S. equity market volatility, and U.S. financial stress indicators. Spikes in uncertainty correspond to immediate increases in exchange rate volatility, buildup of exchange market pressure, and depreciation for emerging markets. The results are compared to advanced economies, China, and Russia separately, identifying key differences across markets.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

The spillover effects among financial stress, investor sentiment, and GCC stock markets: evidence under the bearish and bullish market states

ABSTRACT This study delves into an exploration of volatility spillover between financial stress, investor sentiment, and stock market index returns in the Gulf Cooperation Council (GCC) countries using the DCC-GARCH, wavelet coherence, and BEKK-GARCH models. A robustness check, applying Diebold and Yilmaz’s (2012, 2014) methodology, verifies network measures across markets. Significant patterns in financial stress index were observed during crises, with strong co-movement between financial stress, investor sentiment, and stock returns, especially during COVID-19 pandemic. Volatility transmission from stock returns to investor sentiment and the financial stress index was notable in some markets. Additionally, we found a significant volatility transmission from the financial stress index to market returns during periods of bearish stress in Bahrain and Kuwait and bullish stress across all countries. These findings offer insights for investors and fund managers optimizing portfolios and managing risk exposure.

Using machine learning to forecast clean energy, commodities, green bonds and ESG index prices: How important is financial stress?

PurposeThis study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.Design/methodology/approachWe utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.FindingsThe results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.Practical implicationsThis research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.Originality/valueTo the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

Развитие космической отрасли: опыт США, Китая и России

In recent decades, space has begun to be viewed as an important economic resource, an engine of global competition and technological development, capable of having a multiplier effect on the development of the entire economic system of a country. It is a necessary factor in global communication and information. Each state can use space for peaceful purposes, since it is a common resource for all countries. That is why the space industry is the object of the careful study by many researchers and a special sphere of government regulation. The top three leaders in the space industry are the USA, China and Russia. The purpose of this article is to conduct a comparative analysis of the development of the space industry in the USA, China and Russia, as well as to develop recommendations for improving the regulation of the Russian space industry based on foreign experience. The author selected the following characteristics as the main parameters of comparison: 1. Significant space achievements, 2. Industry financing, 3. Structure of the space industry, 4. The scale of private space sector development, 5. Motives for international cooperation in the field of space exploration. Based on the analysis, the features of space industry development in each country were identifi ed. Special attention in the study is paid to the Russian space industry. In particular, it was found that the main structural feature of the Russian space industry, in contrast to the USA and China, is the significant predominance of state-owned companies over private enterprises. This is associated with the lack of the necessary regulatory framework in our country and, as a consequence, the underdevelopment of the market for space products and services. At the same time, Russia can be considered a leader in terms of funding for the space industry (as a percentage of GDP), but in absolute financial indicators (in millions of dollars) Russia lags significantly behind the United States and China. In addition, Russia adheres to the practice of participating in international cooperation in the space sector, based primarily on political factors rather than economic motives, which fundamentally distinguishes its approach from the approach used by the United States. The article proposes a number of practical recommendations, the implementation of which, according to the author’s opinion, will contribute to increasing the efficiency of space activities in Russia.

Liquidity risk analysis via drawdown-based measures

Trading volumes are key variables in determining the degree of an asset's liquidity. We examine the volume drawdown process and crash recovery measures in rolling-time windows to assess exposure to liquidity risk. The time-varying windows protect our financial indicators from the massive amount of volume transactions that characterize the opening and closing of the stock market. The empirical study is carried out for three Nasdaq-listed assets from April to September 2022. Firstly, we shape all of the volume time series using a weighted-indexed semi-Markov (WISMC) model, as well as the EGARCH and GJR models for comparisons. Next, we calculate drawdown-based risk measures on real and synthetic data, simulated from all the considered econometric models. Finally, we employ the Kullback-Leibler divergence to compare real and simulated risk indicators. Results reveal that the WISMC model reproduces all the drawdown-based risk measures better than the EGARCH and GJR models do for all the considered stocks.

Taking ESG strategies for achieving profits: a dynamic panel data analysis

PurposeThe goal is to investigate the relationship between financial performance and environmental, social and governance (ESG) indicators and disclosures for a sample of Latin American firms.Design/methodology/approachDynamic panel data regressions are used to analyze a sample of 114 companies listed on the Latin American Integrated Market, MILA (Chile, Colombia, Mexico and Peru) for the period 2011–2020. The Altman Z-score and Piotroski F-score are used as indicators of the probability of default and comprehensive financial strength. Models are developed in which the relationship between economic value added (EVA) and Jensen’s alpha are evaluated against firms’ ESG practices.FindingsA direct relationship between ESG strategies and financial performance was found. Better practices and transparency in ESG are related to lower probability of bankruptcy, greater financial strength, greater EVA and superior risk-adjusted returns.Research limitations/implicationsESG data were obtained from the Bloomberg system based on a methodology that may differ from other sources. The sample covers four Latin American countries and large corporations. Independent variables were selected for their perceived validity, given their frequent use in previous studies.Practical implicationsEvidence for company management regarding the importance of strengthening ESG practices and reporting should be part of their balanced scorecards. For investors, the results support the importance of evaluating ESG practices in asset selection.Originality/valueThe present study is the first research to present empirical evidence on the relationship between ESG scores and disclosures for MILA countries, using a comprehensive set of financial performance indicators (Altman Z-scores, Piotroski F-scores, EVA and Jensen’s alpha).