Financial Variables Research Articles

Financial Well-Being of U.S. Military Veterans and Health Impact: Results From the Survey of Household Economics and Decisionmaking.

Research has shown that psychosocial well-being in veterans, including financial status, is related to better clinical outcomes after leaving military service. The current study examines variables linking financial well-being to physical health in veterans and non-veterans and identifies financial variables related to veteran status. We analyzed data from the nationally representative 2021 Survey of Household Economics and Decisionmaking conducted by the U.S. Federal Reserve Board and compared the responses of veterans (N = 1176) to a non-veteran sample matched by age, sex, education, race, ethnicity, and geographic region (N = 1176). Multivariable analyses revealed that although veterans and non-veterans were similar in many financial domains, veterans were more likely to spend money on the lottery and gambling, pay overdraft fees on bank accounts, and take out payday or pawn shop loans. Analyses showed over one-third (35%) of veterans reported credit card debt, significantly higher than non-veterans. In veterans and non-veterans, higher physical health ratings were related to higher income, lacking medical debt, living in a community of greater economic advantage, and having a rainy-day fund in case of financial emergencies. Ratings of one's credit score were also significantly associated with ratings of one's physical health, in both veterans and non-veterans. The data pinpoint specific financial domains to inform policy, education, and outreach aimed at improving veterans' psychosocial well-being. The results also reveal that individual and environment-level financial variables were related to physical health in this national survey, demonstrating the value of assessing financial well-being in the context of medical care, for both veteran and non-veteran patients.

Financial, institutional, and macroeconomic determinants of cross-country portfolio equity flows: The case of developed countries

This paper examines the determinants of financial equity flows to investigate the role played by business cycles, government debt and sovereign rating scores, and whether the impact depends on the magnitude and direction of the flows. Using a new, richer dataset of flows among developed countries over 2001–2018, our key findings are as follows: (i) equity flows are more intense among countries at the same stage of the business cycle (ii) equity flows are higher to countries with a relatively lower debt to GDP ratio (iii) financial and macroeconomic variables are important for big equity flows, while institutional variables are important for the small flows. Overall, considering a wider range of factors under-explored in the literature, we provide a stronger understanding of the development of risks in the financial sector as well as the linkages with other sectors of the economy.

Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era

ABSTRACTThis study investigates the forecasting power of three well‐established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short‐term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out‐of‐sample forecasting analysis specifically targets the period characterized by the short‐term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short‐term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.

Examining the impact of mobile money on financial inclusion in Sub-Saharan Africa: the role of institutions and governance

Purpose This paper aims to investigate the role of institutional quality in the relationship between mobile money and financial inclusion among Sub-Saharan Africa (SSA) from 2002 to 2022. Design/methodology/approach The paper uses annual data from SSA on a bundle of four financial inclusion variables, six institutional quality indicators (i.e. rule of law, government effectiveness, control of corruption, voice and accountability, regulatory quality and political stability) and total volume of mobile money transaction in a year. The two-stage least squares regression was used to validate the hypotheses. Also, the random effects model was also used to account for potential unobserved heterogeneity across countries in SSA. Findings The empirical results reveal that institutional quality and mobile money have direct impact on financial inclusion. Also, institutional quality plays a positive and significant contingency role in the relationship between mobile money and financial inclusion. Originality/value The study contributes to financial inclusion theory by providing multi-country empirical evidence to validate the theory in explaining mobile money’s role in expanding financial access. It also highlights the key insight from financial inclusion theory regarding the need for strong governance institutions for technology-enabled inclusion. By examining interactions between mobile money, institutions and financial inclusion across 15 African SSA economies, the study allows for more generalizable conclusions about contextual dependencies.

Forecasting exchange rate volatility: An amalgamation approach

The importance of exchange rate volatility forecasting has both practical and academic merit. Our aim is to provide a comprehensive analysis of the forecasting ability of financial and macroeconomics variables for future exchange rate volatility. We employ seven widely traded currencies against the US dollar and examine linear models and a variety of machine learning, dimensionality reduction and forecast combination approaches, along with creating a grand forecast (amalgamation approach) from these approaches. Our findings highlight the predictive power of the amalgamation approach, as well as the positive contribution of macroeconomic and financial variables in the forecasting experiment. Furthermore, we generate forecasts on the separate frequencies of volatility using wavelet analysis, in order to extract frequency-related information and examine timing effects in the performance of the methods.

Towards sustainable agricultural development and food security in East Asia: The role of broad money and banking credits

The existing literature has extensively explored the role of financial development in boosting agricultural output. However, there is a lack of empirical research on the effects of broad money and banking credits on food production in East Asian countries. This study addresses this gap by examining the relationship between financial variables and agriculture in selected East Asian economies from 1995 to 2019, focusing on food security. Our findings indicate that increases in broad money and financial sector credit significantly enhance long-term food security by 0.216% and 0.846%, respectively. Additionally, we observed positive correlations between food security and agricultural inputs: a 1% increase in agricultural land, fertilizer use, and renewable energy adoption improves food security by 0.219%, 0.049%, and 0.146%, respectively. Robustness checks and Granger causality tests further validate these results. This study underscores the critical role of financial resources and agricultural inputs in food security and calls for a reassessment of credit systems to mitigate any adverse effects. These findings provide important insights for policymakers aiming to strengthen food security in East Asia.

Inklusi Keuangan dan Efektifitas Kebijakan Moneter : Studi Kasus Negara Indonesia dan Malaysia

Financial inclusion, defined as access and use of formal financial products and services by individuals and companies, has become a major focus in Indonesia and Malaysia in recent years. Authorities in both countries have implemented various policies and programs to increase financial inclusion, hoping to provide economic benefits, namely reducing inflation. The aim of this research is to analyze the influence of financial inclusion, exchange rates, interest rates, broad money, and GDP on inflation in Indonesia and Malaysia. The data used in this research is secondary data ranging from the period 2013Q1 to 2022Q4. The analytical method used in this research is the Vector Error Correction Model (VECM). The conclusion of this research states that the financial inclusion variable has a negative influence on inflation in Indonesia with a VD contribution at the end of the period of 29.37%, while in Malaysia Financial Inclusion has a positive influence on inflation with a VD contribution at the end of the period of 11.22% . The exchange rate negatively influences inflation in Indonesia and Malaysia with VD contributions at the end of the period of 1.28% and 3.87% respectively. Interest rates have a negative influence on inflation in Indonesia with a VD contribution at the end of the period of 1.71%, while in Malaysia interest rates have a positive influence on inflation with a VD contribution at the end of the period of 13.19%. Broad Money positively influences inflation in Indonesia and Malaysia with VD contributions at the end of the period of 2.27% and 2.57% respectively. GDP has a positive influence on inflation in Indonesia with a VD contribution at the end of the period of 22.01%, while in Malaysia GDP has a negative influence on inflation with a VD contribution at the end of the period of 45.39%.

BPR RESILIENCE IN ECONOMIC UNCERTAINTY: COMPARISON OF BPR FINANCIAL PERFORMANCE IN INDONESIA BEFORE AND DURING THE COVID-19 PANDEMIC

The primary objective of this study was to examine the performance of BPRs in Indonesia before and during the Covid-19 pandemic. The focus was on assessing financial performance variables, including Loan to Deposit Ratio (LDR), Non-Performing Loan (NPL), Return on Asset (ROA), and Credit Disbursement. To achieve the study's objectives, a Descriptive Statistical Analysis was employed to provide a detailed overview of BPR performance during the two distinct periods. Additionally, a Difference Test, utilizing the Wilcoxon Test, was conducted to assess the significance of the differences observed between the pre-pandemic and pandemic periods. The results of the analysis revealed notable differences in the condition of BPRs before and during the Covid-19 pandemic. Significant changes were observed in financial performance variables such as NPL, ROA, and Credit Disbursement. However, LDR exhibited a tendency to be insignificant during this comparative analysis. It is crucial to acknowledge certain limitations in this study. The analysis focused primarily on financial performance variables, and other relevant factors may need consideration for a comprehensive understanding. Additionally, the study's findings are specific to the given time frame and may not account for potential longer-term effects. The identified differences in BPR performance provide valuable insights for policymakers and industry stakeholders. These findings can serve as a foundation for designing effective policies and strategies to address the economic impact of the pandemic. Moreover, the study offers essential insights for the banking industry, guiding them in adapting and building resilience in response to unexpected external changes.

The Effect of Financial Literacy on the Financial Management of Students

This study aims to determine the effect of financial literacy on financial management of SMA Negeri 1 Ulunoyo students and the magnitude of the effect of financial literacy on financial management of SMA Negeri 1 Ulunoyo students. The method used in this research is quantitative method. The data collection technique used a questionnaire (Likert Scale). Data analysis techniques used to strengthen questionnaire data are validity test, reliability test, correlation coefficient, linear regression, determinant coefficient, hypothesis testing, normality test, multicollinearity test and heteroscedasticity test. This study used 58 respondents with Simple Random Sampling data collection technique. The results of this study indicate that Financial Literacy affects the Financial Management of SMA Negeri 1 Ulunoyo, South Nias Regency by 54.8%, while 45.2% is influenced by factors not in this study. With increased financial literacy, students will increasingly manage their finances wisely, carefully and regularly by applying indicators of each financial literacy variable and financial management variable, so that students can prepare for future needs and finances.

Evaluating The Performance of Machine Learning Models in Audit Opinion Prediction – A Study in Vietnam

This study investigates the effectiveness of machine learning models in predicting audit opinions using a dataset from the FiinPro-X platform, comprising 9,783 audited consolidated financial statements from public companies listed on Vietnamese stock exchanges from 2016 to 2023. The dataset spans various industries, excluding banks and financial institutions, and focuses on identifying key financial, non-financial, and qualitative variables that influence audit opinions. Six supervised learning algorithms were applied—Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM), and Naive Bayes—evaluated based on their ability to predict both fully acceptable (unqualified) and non-fully acceptable audit opinions. All data processing and model training were implemented in a Python environment. The Random Forest model demonstrated the best overall performance, achieving an accuracy of 0.868 and an AUC-ROC of 0.87, though its F1 score for predicting non-fully acceptable audit opinions was lower (0.585). This suggests that while machine learning models can improve prediction accuracy, challenges remain in handling imbalanced data and non-linear relationships among input variables. The study also reduced the number of features by 30%, improving the models’ performance. Future research should further refine data and feature construction processes to ensure comparability and practical applicability.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Unveiling the drivers of green loan disclosures: a study of financial and governance determinants

PurposeThis study aims to investigate the factors influencing Indian banks’ choice of green loan disclosure practices. The study analyzes the effect of financial and governance variables to understand the sustainable reporting (through green lending) behavior of Indian banks.Design/methodology/approachThe data on green loan disclosure has been hand-collected from the annual reports using a content analysis approach. Using the data of 26 banks for 12 years (2012–2023), the study uses the panel regression method to control for cross-sectional heterogeneity and generalized methods of the moment to address potential endogeneity issues.FindingsThe empirical results depict that larger banks with sufficient risk capital and a strong corporate governance framework demonstrate greater disclosure of green loans. However, growth opportunities and higher market value impedes the reporting of green lending.Research limitations/implicationsThe findings of the study will enhance the extant literature on sustainability disclosure by integrating the financial sector companies in the context of an emerging economy. However, future research may include nonbanking finance companies as well.Social implicationsBanks use societal deposits to invest in productive avenues, and therefore, it is paramount to understand their social and environmental consciousness while evaluating a financing proposal. This research provides a thorough understanding of the sustainable reporting of banks through the lens of green lending.Originality/valueThis research provides unique evidence on the bank-specific determinants of green loan disclosure in an emerging economy context as against the extant literature which primarily focused on sustainable reporting of nonfinancial companies.

The Nexus Between Financial Stability and Economic Growth in Southeast Asia

AbstractFinancial stability is an important part of economic growth. The main objective of this study is to analyse how financial stability (using Bank Z-Score) affects economic growth. The data used is country-level data from five selected Southeast Asian countries (Indonesia, Cambodia, Malaysia, Philippines, and Thailand) for the period 2011 – 2021 using the ARDL model approach. The results of this study found strong evidence that financial stability (banking stability) has a positive effect on economic growth for countries in the Southeast Asian region. This study also reveals that it is not only important financial stability variables to achieve economic growth, but investment and trade openness must also be increased if the country is to achieve its expected economic growth. A positive correlation between investment and trade openness suggests a potential for these factors to contribute to economic growth synergistically. Keywords:Financial Stability, Economic Growth, Bank Z-Score, ARDL

A self-organizing map clustering adaptive artificial neural network model for analysis critical success factors in different phases of MTO projects case study: electrical equipment manufacturer and supplier in Iran

PurposeThe purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.Design/methodology/approachThis study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.FindingsCluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.Research limitations/implicationsThe main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.Practical implicationsIn this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.Originality/valueThis paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

Limited Firm Insurance and Aggregate Implications

This paper studies the impacts of financial shocks on firm insurance, firm dynamics, and macroeconomic implications. A key departure from the literature is that firms provide wage insurance contracts to risk-averse workers in a long-term relationship. Such contracts endogenously impose a form of inflexible debt liability to firms, and make firms with limited financial net worth more vulnerable to shocks than others. In our model, firms have heterogenous productivity and financial net worth, and also firms face financial frictions. Search and matching in the labor market endogenously determines the value of wage contract, and thus the level of liability to the firm. We have three new findings: (1) The wage dynamics of the quantitative model are consistent with those in empirical studies, with significant insurance against firm-level idiosyncratic shocks, and also a weak comovement between aggregate wage and GDP when there are aggregate shocks. (2) Firms with limited financial resources are hit by financial shocks significantly more than others, with larger negative impacts on dividends, credit borrowing, firm value, and firm entry; the endogenous wage insurance also becomes more limited, and there are more separations. These impacts on both firms and workers are different from—and cannot be accounted for by—standard models with a representative firm or worker. (3) Quantitative exercises for the Great Recession in the United States suggest that aggregate productivity shocks and financial shocks play different roles; without financial shocks, we cannot account for the dynamics of financial variables, including the reduced credit borrowing, spikes in credit spreads, and large drops in dividend payments. Lastly, we provide microlevel empirical supports for our model implications. This paper was accepted by Lukas Schmid, finance. Funding: Y. Wang acknowledges the financial support from the National Natural Science Foundation of China [Grant 72150003] and the grant from Shenzhen Municipal Government [Grant 1210614103]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01627 .

The Influence of Financial Literacy and Financial Behavior on the Financial Performance of SMEs: A Regression Analysis Study in Indonesia

The method used in this research is the regression analysis method with the variables used, namely Financial Literacy (X1), Financial Behavior (X2) on Financial Performance (Y). This research uses quantitative methods and uses a sample of 118 respondents who are SME owners spread throughout Indonesia. The research uses SPSS software to assist in data processing. From the results of this research it can be concluded that the t test results obtained a significant value for the financial literacy variable of 0.019 and a regression coefficient value of 0.209. The significance value <0.05 and the positive regression coefficient value indicate that financial literacy has a positive effect on financial performance . The results of the t test obtained a significant value for the financial behavior variable of 0.005 and a regression coefficient value of 0.200. The significance value <0.05 and the positive regression coefficient value indicate that financial behavior has a positive effect on financial performance. These findings also show that financial literacy has a positive effect on the performance of SMEs and financial behavior has a positive effect on the financial performance of SMEs in Indonesia

The Usage of Paylater Among College Students: The Role of Self-Control, Hedonistic Conformity, and Family Financial Education

Research aims: The increasing phenomenon of the unwise use of Paylater in society, even among college students, is interesting to review further. This study aims to test the role of self-control and hedonistic conformity with the moderation of family financial education on the use of Paylater by university students.Design/Methodology/Approach: Primary data was obtained through a survey involving 117 respondents who have or are currently using the Paylater service. Furthermore, the data was processed using the Partial Least Squares (PLS) method.Research findings: This study produced the following findings: hedonistic conformity was proven to have a positive effect on the use of Paylater in the full sample and in two sub-samples. This finding is in line with the TPB modification used in this research, hedonic conformity as a proxy for subjective norms has been shown to contribute positively to the use of Paylater among colleege students. Meanwhile, the role of family financial education has not been proven to moderate the regression path of self-control and hedonic conformity on the use of Paylater.Theoretical Contribution/Originality: This study contributes to enriching the literature on personal financial management, especially regarding the use of Paylater based on internal and external factors of students.Practitioners/Policy Implications: These results offer recommendations for personal financial management, especially for university students, to avoid the risk of being trapped by Paylater by paying attention to their social community and prioritizing family financial education.Research Limitations/Implications: Respondents were mostly undergraduate students, so this study cannot explain the dynamics of financial behavior among undergraduate and postgraduate students who have different demographic characteristics. Therefore, future research needs to analyze different sample groups to enrich the scientific perspective. Furthermore, to provide depth of analysis, this study proposes to add other internal aspects of the individual to the TPB theoretical framework (e.g.: perception of usury and financial risk tolerance). Last but not least, regarding the development of indicators from the family financial education variable that does not yet include personal debt management education items, hence this indicator can be added as a proxy for this variable to obtain in-depth measurements.

Microfinance, Financial Inclusion and Economic Welfare in Africa: A Panel Investigation

This study explores the relationship between microfinance, financial inclusion, and economic welfare in a sample of 23 African countries for the period of 2004 to 2023 using annual time series data. We measured microfinance with the number of Branches of Microfinance banks, Microfinance Borrowers, Microfinance outstanding deposits, Microfinance outstanding loans; financial inclusion number of registered mobile money accounts per 1,000 adults; the number of mobile money agent outlets par 1,000 adults; and digital card ownership; governance and institutional quality with Rule of law, regulatory quality, and government effectiveness; and economic welfare with household consumption; while we controlled for the inflation rate, interest rate and exchange rate. These variables were estimated using Panel Least Squares, Fully Modified OLS (FMOLS), Dynamic OLS (DOLS), and Panel Autoregressive Distributed Lag (ARDL) estimation techniques. The result of the cointegration revealed that cointegration exists between the variables of the model. Findings from the aforesaid estimation techniques show that there is the existence of long-run relationships between microfinance, financial inclusion, and economic welfare since the coefficients of the microfinance and financial inclusion variables are statistically significant. The coefficients of the error correction terms which measure the effects of the short-run dynamics of the model suggest that the speeds of adjustment from the long run to the short run in the models would be 76%, 61%, 83%, 67%, 68%, 34%, and 80% respectively for all the specified models. Following the findings of the study, conclusions were drawn and the study suggests that microfinance institutions should adapt to the digitization of their products and services for wider coverage on one hand; the government should provide digital financial amenities to create a fertile ground for micro-financial institutions to rely on to maximize the welfare of the economy.

The value relevance of deferred tax assets: An empirical analysis of German HDAX-listed companies

Deferred taxes emerge from timing differences in recognizing income and expenses between commercial and tax financial statements. However, the capitalization of deferred tax assets remains contentious, with questions raised about their value relevance to investors. This study aims to investigate the informational value and economic significance of deferred tax assets in financial statements prepared in accordance with both German commercial law (HGB) and International Financial Reporting Standards (IFRS). The analysis is based on financial data from 1,066 firm-year observations of HDAX-listed companies from 2000 to 2022. Using an Ordinary Least Squares (OLS) regression model, the study examines the relationship between deferred tax assets and the market value of equity. Key financial variables, including research and development expenses, deferred tax liabilities, net income, and the market-to-book ratio, are incorporated to provide a comprehensive assessment of deferred tax asset relevance in capital markets. The results demonstrate that deferred tax assets have a negligible impact on the market value of companies, with no statistically significant effect detected. Conversely, research and development costs, as well as net income, exhibit a strong positive influence on firm valuation. These findings suggest that deferred tax assets serve largely as an accounting mechanism, lacking informational value for investors.