Macroeconomic Indicators Research Articles

The Causality Relationship between Credit Default Swaps (CDS) and Portfolio Investments: The Case of Türkiye

This study examines the causality relationship between portfolio investments and credit default swaps (CDS) in Türkiye. Analysing the dynamics between portfolio investments and CDS premiums, two important variables for financial markets is critical to understanding how risk perception and investment decisions are affected. While portfolio investments are generally considered an indicator of the confidence of foreign investors in the country's economy, CDS premiums are an important risk measure that reflects the country's debt risk and the risk perception of market participants. In this context, examining the relationships between the two variables contributes to the understanding of the effects of investor behavior and risk perception on macroeconomic indicators in financial markets. In the study, the Granger causality test was applied using data from the period 2014Q1-2024Q1. The results obtained show that CDS premiums have a significant and unidirectional causal effect on portfolio investments. Increases in CDS premiums increase investors' risk perception and lead to a decrease in portfolio investments. On the other hand, no causal effect of portfolio investments on CDS premiums was found. These findings emphasize the importance of risk management in terms of portfolio investments in Türkiye and reveal that CDS premiums play a role in investor decisions.

머신러닝을 활용한 해운기업 파산 예측에 관한 연구

Purpose: This study aims to enhance bankruptcy prediction accuracy in the shipping industry by using advanced machine learning models, specifically XGBoost and LSTM, comparing the predictive factors between large enterprises and SMEs. Research design, data, and methodology: Utilizing financial data from 2001 to 2023 for the Korean shipping industry, the study analyzes key financial ratios and macroeconomic indicators. XGBoost and LSTM models are employed to develop customized bankruptcy prediction models for large enterprises and SMEs. Results: The XGBoost model outperforms others, effectively handling complex financial and macroeconomic variables. Large enterprises are more influenced by internal financial factors, while SMEs are sensitive to external economic conditions like a shipping index. Conclusions: The study highlights the need for tailored prediction models to improve bankruptcy risk management in the shipping industry, especially focusing on the size of the company. Future research should explore model applicability across different regions, and should consider integrating real-time data for enhanced accuracy.

Macroeconomic Determinants of Crime against Children A Case Study of Punjab

Despite it being a growing cause of concern, the relationship between macroeconomic indicators and crime against children has previously remained widely unnoticed. An extensive range of literature is reviewed that gives this domain of research a different perspective by investigating crime against children existent in various countries around the world. The analysis of this paper is divided into three sections. Firstly, a regression analysis has been run using time series data taken from Punjab, Pakistan for the years 2007-2017. Applying the Ordinary Least Square Technique and using income, unemployment, and education as independent variables and crime rate against female children as the dependent variable; a regression model was run. While income and education show a statistically significant relationship, a prediction cannot be made due to a limited data availability of the crime variable. Secondly, this paper takes a descriptive approach by using a correlation matrix to observe the relationship between total crime rate against male children and unemployment, poverty and social sector spending. Results of this Matrix indicate a positive relationship between crime with unemployment and crime with poverty, while there is a negative linkage among social sector spending and crime. However, a correlation matrix can only be used to define a causal relationship between variables and not be employed to make reliable cause and effect estimations.

Global dynamics of the IPO market: structural analysis and growth prospects

The global IPO (Initial Public Offering) market continues to play a critical role in raising capital for companies across various economic sectors. However, its dynamics remain ambiguous due to cyclical fluctuations driven by economic, financial, and regulatory factors. The relevance of this topic lies in the increasing importance of understanding the forces shaping IPO success, given the globalization of financial markets and growing competition. A thorough examination of the factors that determine IPO performance and the future prospects of this mechanism is vital for stakeholders.The purpose of this paper is to conduct a comprehensive structural analysis of the global IPO market's dynamics, identify the key drivers of its development, explore regional differences, and assess the market’s growth potential amid ongoing global transformations. The focus of the study is the global IPO market, with particular attention to its economic, financial, and regulatory dimensions.The methodological approach centers on structural market analysis, utilizing statistical and comparative methods to explore market dynamics across different regions. The research is further grounded in an analysis of macroeconomic indicators, regulatory changes, investment trends, and technological advancements that influence the IPO market.This study examines the impact of financial market globalization, regulatory adjustments, and technological innovations on the development of IPOs in various regions worldwide. The role of macroeconomic stability, interest rates, and market volatility in shaping IPO demand is also explored. In addition, the research delves into sectoral differences and regional trends, particularly the activity of IPO markets in the U.S., Europe, and Asia.The conclusion addresses the growth prospects of the global IPO market amid continuous shifts in the global economy and provides practical recommendations for companies considering public offerings and investors interested in IPO market opportunities. The study highlights how firms can navigate these volatile conditions and what investors should consider when participating in IPOs.

Comparative analysis of the predictive power of statistical models of macroeconomic indicators in conditions of permanent crises

Subject. The article discusses prediction of the state of the economy, the accuracy of forecasts of traditional models during crises, the need to find more effective model specifications to predict macro indicators. Objectives. The purpose is to carry out a comparative analysis of the predictive ability of ensemble methods in comparison with a set of models, including traditional statistical algorithms and machine learning algorithms. Methods. The comparative analysis of predictive ability of the models and interpretation of results obtained were performed using a dynamic factor model (DFM), a neural network with long-term short-term memory (LSTM), and integrated gradient methods (IG). Results. We performed the analysis of predictive ability of the ensemble model to forecast GDP, which combines DFM and LSTM to account for both linear and nonlinear dependencies in the data; the analysis of predictive power of various indicators, which showed that an increase in forecast error is observed for all models except DFM, the ensemble model with an error correction structure, and ARMAX. The obtained results can be used to build models of macroeconomic indicators in order to make strategic decisions by enterprises of various industries operating in a highly uncertain environment. Conclusions. The combination of DFM and LSTM in the ensemble provides higher accuracy forecasts than LSTM and competitor models, however, with less predictive power than DFM.

THEORETICAL APPROACHES TO THE ASSESSMENT OF THE FINANCIAL STABILITY OF BANKING INSTITUTIONS

The article considers the essence of the concept of ‘financial stability of a banking institution’. The external and internal factors of influence on the activities of commercial banks in the current socio-economic conditions of Ukraine are identified. It is noted that the leading role in ensuring financial stability belongs to the bank, subject to effective regulation and the use of internal methods to ensure financial stability. The existing methodological approaches to assessing the financial stability of banks used in domestic practice are analysed. It is noted that in order to ensure financial stability, it is necessary to regularly monitor and analyse vulnerabilities and risks of the financial system based on macroeconomic and prudential indicators, as well as to respond to them in a timely manner with appropriate measures. It is determined that the use of integrated approaches and the latest methods, such as econometric modelling, fuzzy set methods and graphical methods, allows obtaining a more detailed and accurate picture of the financial condition of banks and ensuring their effective management. It has been shown that the development and implementation of new methods and improvement of existing tools for analysing financial stability are extremely important for ensuring the stability and development of the banking sector. Effective financial stability management requires the use of modern methods and approaches that allow timely and accurate risk assessment and adaptation to dynamic conditions

Informal Employment Areas for Syrian Asylum Seekers in Türkiye: A Fuzzy TOPSIS Analysis

Informal employment is considered a major problem in developing countries. Although unregistered employment contributes to a country’s economy in terms of production, it can have a negative impact on macroeconomic indicators such as tax losses and unemployment. Unregistered employment can include both nationals and foreign workers. The aim of this study is to find out in which sectors Syrian asylum seekers are preferred in unregistered employment in Türkiye. Fuzzy TOPSIS was used as a method in the study. Fuzzy TOPSIS provides a ranking according to alternatives and criteria. The analysis results show that Syrian refugees are preferred in agriculture, construction and textile sectors respectively in unregistered employment in Türkiye. With these results, it is suggested that the legal regulations of Türkiye in immigration policy and unregistered employment and those asylum seekers should be evaluated at the legal level regarding the country’s economy.

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.

Экономика РФ в январе – апреле 2024 года

Based on the analysis of a number of macroeconomic and sectoral indicators, it is established that in January–April 2024, there was an economic decline compared to the corresponding period in 2023. Moreover, the extent of the decline is increasing month by month. It is noteworthy that the authorities, as in the past in the USSR, are turning a blind eye to the decline. The causes of the decline are determined by the combined impact of both cyclical factors (Western economic sanctions, overstrain from military expenditures) and long-term trends of a decline in fixed assets and a reduction in the workforce, as well as the qualifications of personnel at all levels. The future prospects of the Russian economy are determined, along with ensuring effective economic policy and building a functional state system, by the level of escalation of the conflict in Ukraine and the extent of economic sanctions. In the event of significant escalation of military actions, what will be required is a transition to a wartime economy with a high share of military expenditures in GDP, a reduction in consumer spending and non-productive investments, mobilization of labor resources in favor of the armed forces and the military-industrial complex, and the introduction of a rationing system for main food products, as well as the nationalization of several companies. The success of this structural maneuver is determined by the organization and qualifications of the state apparatus. In the case of moderate escalation of military actions and economic sanctions, the radical nature of structural and institutional changes will decrease.

Evolution of Financial, Non-Financial, and Macroeconomic Predictors in Corporate Bankruptcy: A Comprehensive Review

This paper analyzes the evolving interplay over the decades between financial, non-financial and macroeconomic indicators as predictors of corporate insolvency. A key goal is identifying the most frequently employed and validated models incorporating financial metrics, non-variables, and economic factors to gauge bankruptcy risk. This study conducts a rigorous review of 204 scholarly articles published from 1960 to 2024 accessed through databases, i.e. Scopus and Web of Science, using the PRISMA framework. Analysis categorizes predictors into financial, non-financial and macroeconomic variables, examining their relevance over time. Traditional financial ratios such as Debt-to-Equity and Return on Total Assets remain influential. However, management quality, governance, and economic aspects, including growth and interest rates, now complement standard finance models, enabling more nuanced predictions. The research underscores the value of internally and externally focused management systems for averting insolvencies and augmenting standard advice. Results suggest that separately weighing a firm's financial position against non-financial health, political environment, and location allows for more robust forecasting. Looking beyond solely numeric data to incorporate qualitative aspects also forms a more holistic view that benefits managers and policymakers seeking to support economic stability.

Innovation Success in the European Union: Aggregate Demand Analysis

In the current period of uncertainty in the European Union, innovation is becoming a key factor for maintaining not only economic stability but also competitiveness in the globalised environment. Following the financial crisis that hit Europe, aggregate demand indicators slowly started to stabilise, mostly reaching their peak in 2019. However, in the following year, due to the pandemic crisis, these indicators fell again, posing a new challenge for researchers around the world. This study examines the relationship between macroeconomic indicators of aggregate demand and innovation performance in 25 EU countries. Using data for the period 2013-2021 as input for the regression analysis, the research identifies firm investment and exports as important innovation support factors and highlights their key role. On the other hand, the research shows that household consumption and imports are barriers to innovation. Somewhat surprisingly, government spending on goods and services does not have a significant impact on innovation success, underlining the need to rethink government investment policies. This finding highlights the need for a more thorough analysis of the effectiveness of state investment in innovation and possible adjustments to state policies to maximise the promotion of innovation activities in the state. The study also highlights the importance of comprehensive strategies that promote investment and exports, while emphasising the removal of barriers to innovation in the form of household consumption and imports.

Effect of Economic Risk Sources on the Quality of the Business Environment in the SME Segment: A Case Study from the Slovak Republic

The aim of the article is to define and quantify the impact of specific economic risk sources on the quality of the business environment (QBE). The subject of the analysis was the evaluation of the impact of selected economic risk sources, including the macroeconomic environment, monetary policy and interest rates, and corporate financing on the QBE. Data was collected between 2018 and 2022 using an online survey method. A total of 480 SMEs from the Slovak Republic’s business environment completed the questionnaire. Advanced statistical techniques, including descriptive statistics, correlation analysis, and linear regression analysis, were employed to evaluate the statistical hypotheses. The empirical results yielded several significant findings. All examined sources of the macroeconomic environment, such as the level of basic macroeconomic indicators, the state of the macroeconomic environment in the context of starting a business, and innovation activities of SMEs, determine the QBE. Other key factors that positively influence the quality of the business environment include: stabilization of the business environment by the central bank's monetary policy; the level of bank interest rates on the innovation activities of companies and availability of bank loans. The findings are significant not only for the SME segment itself and their owners and top managers but also for national-level institutions along with non-profit organizations assisting in improving the entire SME segment in the business environment.

Subsidizing as a tool to ensure economic sovereignty: a quantitative analysis

Objective: to assess the relationship between the basic monetary instrument of state support for industry – subsidies – and basic macro-indicators of its effectiveness in the context of economic sovereignty. Methods: content analysis of publications, statistical analysis of macroeconomic indicators, econometric analysis on a wide sample of countries. Results: 1) the analysis of mentioning the category “subsidies” in Russian, English and Chinese printed publications confirmed the high interest to subsidies in China; 2) the analysis of correlation between the level of subsidies and the basic macro-indicators by countries on IMF data showed statistically significant correlations of the level of subsidies with the level of current account balance (low correlation) and GDP per capita (moderate correlation); 3) the evaluation showed no significant relationship between the level of subsidies and GDP per capita. According to the study results, conclusions were drawn about the absence of dependence between the level of transfers provided and the decrease of dependence on import. Scientific novelty: the hypothesis was checked that the level of subsidies in the state has a statistically significant impact on the country’s current account balance. The model is based on earlier studies with the added analysis of the role of subsidies in reaching the economic development goals. The novelty of this study consists in the absence of a significant relationship between the level of subsidies and the current account balance. Practical significance: the study results can be useful to specialists in the field of industrial policy. They provide a basis for a fuller understanding of the tools to ensure technological sovereignty. Modifications of the regression model can be used in further studies of the factors of the current account balance.

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction.

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

Relationship between Public Expenditure and Inflation

Effective management of inflation remains one of the arduous tasks for managers of global economies. Inflation remains a macroeconomic indicator whose management outcomes have the potential to decide the electoral fortunes of elected governments and determine the purchasing power of household incomes. The purpose of this research was to assess how a steady increase in public expenditure could impact inflationary levels. The quantitative approach to scientific inquiry was adapted and used in the research. Specifically, the cross-sectional design formed the basis of the research. Data required for the research were obtained mainly from secondary sources. These included textbooks, peer-reviewed articles published in journals and grey literature. Other sources were Google Search Engine, including Netcials, MacroTrends, and databases of the World Bank and Bank of Ghana, among other significant sources. Respective data on Ghana’s annual GDP values from 1960 through 2020, annual inflation data from 1965 through 2020, changes in annual inflation rates from 1965 through 2020, and annual public expenditure values from 1983 through 2020 were collected and used in the research. Descriptive statistics and regression models were used to describe the research variables and to evaluate their behaviour over the stated time frame on inflation. The study revealed lax monetary policy as the root cause of consistently high inflationary rates over considerable periods and remained a strong contributor to the weak purchasing power of national fiat currencies. Further, relative price distortions away from their economic equilibrium are not healthy for the economy, while disinflationary policies adapted for implementation should be contingent on the causes of inflation. The single largest cost of inflation to consumers is the erosion of income, whereas higher tax rates are not the panacea for the challenges of mounting public expenditure and public debts. Respective inflation rates recorded in Ghana during 2012 (7.13%) and 1995 (59.46%) remained the lowest and highest from 1993 through 2020. Fiscal periods 2017 through 2020, 2009 through 2012, and 2005 through 2008 recorded the respective best (9.33%), second-best (11.46%) and third-best (13.32%) annual average inflation rates. With the exception of 2013 through 2016, the performance of the Ghanaian economy in relation to annual average inflationary control witnessed improvements during the second political and administrative terms compared to the first terms. However, the best annual average inflation rate over an eight-year period during the Fourth Republic was recorded from 2009 through 2016 (13.45%). Findings from the research revealed a positive and significant relationship between public expenditure and inflation (coefficient value = 1.2004653; p = 0.004, p < 0.05). Public expenditure accounted for 21.30% of the variation in inflation rate from 1983 through 2020. Results from the statistical output validated the significant influence of public expenditure on inflationary hikes, the need for monetary and fiscal policies related to inflation control to be strategically reviewed and strengthened to improve implementation outcomes, and the potential of rising public expenditure to undermine the rigidity and robustness of economic fundamentals through an unexpected surge in inflationary levels. The findings underscored the need for managers of various global economies to be keen on “economic” public expenditure to the neglect of profligate public expenditure, so the socio-economic derivatives from investments in public infrastructure would strongly add to, rather than subtract from, national development and growth efforts. The fundamental objective of MPC should be pivoted around three cardinal functions: the creation of maximum employment, moderation of long-term interest rates and ensuring price stability. Excessive public expenditure requires careful consideration by economies that whet their national development appetite with increased public infrastructure projects.

Exchange rate flexibilization and macroeconomic determinants: Evidence from MENA region

Many emerging economies have significantly changed their economic policies by adopting floating exchange rate regimes. This article aims to investigate the relationship between exchange rate variations and key macroeconomic indicators in the MENA region, focusing specifically on Egypt, Tunisia, and Turkey. The study employs regression analysis to explore how macroeconomic factors such as inflation, interest rates, trade balances, foreign exchange reserves, etc. influence exchange rate movements in these economies. By conducting a comprehensive regression model, we aim to provide insights into the complex dynamics of exchange rate fluctuations and their implications for economic stability and policymaking in the MENA region.

From text to treasure: the predictive superiority of a FinTech index in stock market returns

This study employs a text analysis methodology to construct a Financial Technology (FinTech) Index, utilizing textual data from The New York Times. The primary aim is to investigate the correlation between financial technology and stock market performance. Our findings provide compelling evidence that the FinTech Index possesses substantial predictive capability for excess returns in the US stock market, a feature that becomes particularly pronounced during economic downturns. Notably, when compared with traditional macroeconomic indicators, the FinTech Index offers valuable incremental insights. Moreover, this study expands to include sector-level and international market analyses, demonstrating the broad applicability and robust performance of the FinTech Index. Importantly, through the use of out-of-sample testing, we substantiate that the FinTech Index demonstrates superior predictive accuracy, presenting opportunities for investors to achieve higher economic returns.

Shades of Resistance: Factors Influencing Populist Mobilization Against the EU Budgetary Conditionality Regime

Although the past decade has shown how populist governments may challenge the EU’s budgetary framework, we still lack an understanding of the circumstances under which populists are more likely to mobilize against EU-level decision-making in this field, and what this mobilization may look like. Combining the literature on populism as an ideology and EU decision-making, the article zooms in on the negotiations regarding the general regime of conditionality in EU budgetary politics and argues that economic and political factors have influenced populist mobilization. A qualitative comparison of the nine cases where populist parties feature in the government highlights that only two countries, Poland and Hungary, have actively opposed the introduction of the so-called rule-of-law conditionality. A closer look indicates that a combination of Euroscepticism, European Parliamentary affiliation, membership in the European Public Prosecutor’s Office, and the political power that populist parties possess at home, along with key macroeconomic indicators, have influenced populists in government to mobilize against the conditionality mechanism. In the second part of the analysis, the article showcases the actions of the Hungarian government, highlighting it as a specific example of populist mobilization. Viktor Orbán’s government has built a populist narrative around the issue, questioned the norm of the decision-making process, exerted a veto to block the agreement temporarily, and later challenged the regulation in court—in short, it engaged in unpolitics.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.