Ordinary Least Squares Research Articles

Do stock prices affect corporate tax behavior?

Purpose This study aims to examine how firm-specific information in stock prices influences corporate tax behavior. Design/methodology/approach This study uses ordinary least squares (OLS) regressions to examine the association between stock price informativeness and effective tax rate (ETR) volatility. Findings Stock price informativeness is positively associated with short-term tax avoidance as measured by ETR volatility. Originality/value While prior research examines the association between market activity and the level of ETRs, this study examines other various aspects of tax behavior.

Resource supply and demand model of military settlements in the cold weapon era: case of Zhenbao Town, Ming Great Wall

AbstractAs an important military defense project in Chinese history, the Ming Dynasty Great Wall and its associated military settlements played a crucial role in maintaining border security. A systematic analysis of how military settlements allocated resources based on external threats and geo-strategic needs is essential to understanding their settlements defense systems. However, this aspect has been relatively under-explored in existing research. Therefore, a resource allocation assessment model for Great Wall military settlements during the Cold Weapon era is established to facilitate a more scientific analysis and quantitative assessment of Military Defense Capabilities(MDC) and Military Requirements(MR). This study employs a combined Analytical Hierarchy Process (AHP) and Principal Component Analysis (PCA) approach to determine the weight of each indicator for both military defense capability and military requirement. Spatial clustering using K-means is conducted to visualize the distribution of these strengths and weaknesses. An Ordinary Least Squares (OLS) regression model links military requirements with defense capabilities, assessing the relationship between settlement locations and historical battle sites. Residual analysis is utilized to identify areas of resource over-allocation or under-allocation. The study reveals that the regional differences in military defense capabilities and requirements within the Zhenbao Town area during the Ming Dynasty correspond closely with the locations of frequent conflicts. This finding suggests that the distribution of defensive capabilities were strengthened in areas with challenging terrain, while the effectiveness and allocation of defense capabilities were determined by logistical support and transportation conditions. This study enhances our understanding of historical military strategies from a historical geography perspective and offers innovative insights for analyzing Great Wall settlement, moving beyond mere sensory intuition and historical experience.

Chief executive officers’ compensation: Does gender pay parity exist in the Nigerian context?

The optimal contract theory posits that an effective compensation plan should be based on performance. Globally, legislators are concerned about the gender pay gap due to stereotypes against women in line with congruity theory. Despite the plethora of gender-related studies, empirical evidence on the gender pay gap at the upper-echelon management level is limited, especially in Africa. Hence, the study examines the effect of CEO gender on CEO compensation in the Nigerian deposit money banks using a longitudinal research design. The study employed the ordinary least square (OLS), fixed effect method, and random effect method to analyze the 144 firm-year observations collected from the Nigerian Exchange Group (NGX) factbooks and the financial reports of 12 banks during 2011–2022. The Hausman Test (chi sq = 3.623, P = 0.003) and Redundant Fixed Effect Test (chi sq = 8.159, P = 0.000) indicated that the appropriate method of reporting is the fixed effect method. The association between CEO gender and CEO compensation (coeff = –8.690 and t = –10.31) is statistically negatively related. The study concluded a gender pay gap in favor of men among Nigerian Nigerian deposit money banks’ CEOs. These findings align with the congruity theory. The study recommends a mandatory gender pay parity plan in line with the optimal contract theory to reduce gender pay inequality.

Analysis of Factors Affecting CO2 Emissions in Türkiye Using Quantile Regression

This study aims to show how the impact of factors on carbon dioxide (CO2) emissions differs at the quantile level and to demonstrate the superiority of the quantile regression method over the OLS method by using quantile regression and ordinary least squares (OLS) methods in order to examine the factors affecting CO2 emissions in Türkiye in depth. Covering the period 1990–2021, this study evaluates the relationship between CO2 emissions and GDP per capita growth, population growth, and renewable energy consumption. One of the important findings of the study is that the increase in the population ratio, which is insignificant according to the OLS method, positively affects CO2 emissions at the 0.25 quantile point. According to both OLS and quantile regression methods, GDP growth does not affect CO2 emissions, while renewable energy consumption has a significant and negative effect according to both models. These results demonstrate that economic growth has no discernible impact on CO2 emissions in Türkiye, while investments in renewable energy can significantly lower emissions and open the door for quantile regression to be used more widely in related research. Unlike traditional methods that focus only on the conditional mean, the quantile regression method provides a comprehensive framework for Türkiye’s sustainable development policies by exploring factor effects at different emission levels.

The impact of smart classrooms on the core literacy of college English learners and the relationship between core literacy and learning outcomes: Evidence from China

College English major students are taken as the subjects of evaluation, applying learning analysis technology based on learning behaviour data sets and traditional evaluation and comprehensively using the analytic hierarchy process for index weighting and evaluation practice to construct a comprehensive evaluation index system for the core literacy of college English major students. Secondly, this study deeply examines the impact of smart classrooms on the core literacy of college English learners. By applying different regression models, including Ordinary Least Squares (OLS), fixed effects model, and dynamic lagged fixed effects regression, the research results consistently show that the smart English teaching model significantly improves the core literacy of college students. The regression coefficients of all models are between 0.2150 and 0.2818, and they are robust and reliable at a significance level of 1%. In addition, the study explores the role of academic resources as a mediating variable and finds that smart English classrooms improve students' English core literacy by increasing academic resources. Academic resources are confirmed to mediate the positive impact of smart English classrooms on students' English core literacy, producing a mediating effect of 30.78%. Using deep neural networks, this study further explores the complex relationship between core literacy and learning outcomes. Therefore, as an innovative teaching model, the application potential of smart English classrooms in improving students' English core literacy is significant.

Single-party regime, cooptation, and strategic social spending in developing countries

How regime types affect the provision of social spending in the context of developing countries? This article provides a novel political-economic approach, arguing that single-party regimes are more likely to spend on pensions than other types of autocratic states to co-opt the large number of critical members although the regime type does not affect the general welfare spending across autocratic states. The theory emphasizes the effect of institutional and power structure heterogeneity across autocracies in shaping the incentives and strategies that the ruling elites co-opt and respond to the demands of the ruling coalition across different autocratic regimes. Using panel, ordinary least squares (OLS) regression with lagged dependent variable along with several empirical strategies, it finds the evidence supporting this argument with a new dataset from 1990 to 2012. The study provides new insights on how autocratic institutions especially the party utilize strategic social policies to resolve the elite-level dictator dilemma for regime survival that are absent in other autocratic types.

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Dust over water: Analyzing the impact of lake desiccation on dust storms on the Iranian Plateau

The desiccation of lakes on the Iranian Plateau, driven by both natural processes and anthropogenic activities, has led to significant land cover changes, exposing lake beds and triggering adverse environmental consequences. These transformations pose serious threats to ecological conditions and the provision of ecosystem services. This study investigates the potential links between lake desiccation and the occurrence of dust storms across major lakes on the Iranian Plateau. By utilizing remote sensing data and time series statistical analysis, including the Mann-Kendall test, Ordinary Least Square (OLS) regression, and frequency analysis, we assessed changes in lake extent and their effects on Aerosol Optical Depth (AOD) and Particular Matter (PM2.5) from 1985 to 2020. An analysis of Landsat imagery reveals that lake desiccation commenced in the late 1990s, exhibiting a marked downward trend that has intensified in recent years. The results show a significant inverse correlation between aerosol parameters (AOD and PM2.5) and lake extent. Reduced lake area is associated with elevated AOD and PM2.5 concentrations, with the highest aerosol levels observed when lakes reach their smallest extent. Notably, despite an 81 % reduction in the lake area, dust storm occurrences have increased by 20%. These findings highlight the critical role of lake desiccation in exacerbating dust storms in the surrounding regions. Furthermore, the temporal alignment between fluctuations in lake extent and dust storm frequency is validated through regression analysis with a 95 % confidence level. The consistency between our results and existing literature underscores the reliability of the observed relationship, emphasizing the urgent need to address the environmental consequences of lake desiccation to mitigate its broader ecological impacts and potential human health impacts.Graphical Abstract

Analyzing Cancer Survival Times Using the Exponentiated Fréchet-Gompertz Distribution

Understanding the survival of cancer patients is essential for determining optimal treatment strategies. This research introduces a robust distribution for analyzing survival data known as the Exponential Fr ́echet-Gompertz distribution (EFG). The EFG combines the unique characteristics of the Exponential Fr ́echet and Gompertz distributions, enhancing its efficiency and providing greater flexibility in representing complex datasets. The study specifically investigates the EFG distribution’s effectiveness in modeling cancer patients’ survival times. A comprehensive analysis of the distribution’s properties is presented, including the quantile and quartile functions, shape indices, moments, moment-generating function, characteristic function, mean residual life, mean waiting time, R ́enyi entropy, and order statistics. The parameters of the distribution are derivedusing five distinct methods: Maximum Likelihood Estimation (MLE), Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Cram ́er-von Mises (CVM), and Maximum Product of Spacings (MPS). A Monte Carlo simulation technique is employed to evaluate the performance of these estimation methods. The simulation results indicate that as sample size increases, the meansquare error (MSE) values for all estimators decrease. Notably, the MLE exhibits the lowest MSE, while the MPS has the highest MSE, particularly for smaller sample sizes. Furthermore, the study presents a comprehensive comparison of the effectiveness of these estimation methods in analyzing survival times for various cancer types, including bladder, bone, blood and brain cancer. The results indicate that the EFG distribution is an optimal model for representing the survival times of patients across these different cancers data. Furthermore, the W.LS method yielded superior estimations for most datasets concerning cancer patient survival. Overall, the EFG distribution demonstrates exceptional capability in accurately fitting survival times for cancer patients compared to other competing distributions.

ESG risk, CEO education and gender: Evidence from Southeast Asia

AbstractThis study explores the impact of a CEO's educational background from a top university on reducing corporate ESG risk and the potential moderating role of CEO gender. Using the QS World University Rankings (QS WUR) to define reputable universities, the research focuses on companies in ASEAN (Association of Southeast Asian Nations) countries, known for elevated ESG risks. Through Ordinary Least Squares (OLS) regression analysis, the results show that CEOs educated at top universities reduce ESG risk, while CEO gender does not moderate this relationship. The findings represent the importance of reputable universities as habitus to support the globalization of business and responsible management education, regardless the gender. The results also highlight the influence of educational background on corporate ESG performance, with significant implications for corporate strategy and CEO appointments.

A measurement study of the environmental quality and medical expenditures of elderly individuals: causal inference based on machine learning

BackgroundThe global surge of environmental pollution exacerbates health issues, disease incidence, and economic strain. In China, the increasing healthcare costs of the elderly population necessitate addressing this challenge as part of the “Healthy China” strategy. We explore the impact of environmental quality on elderly healthcare expenses.MethodsThis study devised a comprehensive environmental quality index for 30 Chinese provinces, excluding Tibet, which was correlated with medical expenses for individuals older than 60 years, using China Family Panel Studies (CFPS) data. Because the traditional econometric model cannot solve the endogeneity problem and the selection of instrumental variables is subjective, a new machine learning algorithm is adopted based on the traditional ordinary least squares (OLS) model and the fixed effect model to conduct causal analysis to ensure the reliability of the results. Finally, heterogeneity analysis was conducted based on the generalized random forest algorithm.ResultsSouthern provinces such as Jiangxi and Guangxi exhibited superior environmental qualities. A regional analysis revealed a gradient where environmental quality decreased from west to east and from south to north. Both conventional and machine learning methodologies underscored a pivotal finding: enhanced environmental qualities significantly curtail elderly healthcare expenses. A heterogeneity assessment revealed that such improvements predominantly benefit elderly people in the eastern and central regions, with marginal impacts in the west. For different groups, the improvement of environmental quality can significantly reduce the medical expenditure of people aged 60 to 75, with bedtime hours between 9 and 11 PM and a lower household income.ConclusionsThis study, employing machine learning and traditional models, demonstrates that enhancements in environmental quality significantly reduce medical costs for the elderly in China, especially in the eastern and central regions, and among demographics such as individuals aged 60–75 and low-income households. These findings underscore the potential of environmental policies to lower medical costs within the “Healthy China” initiative framework. However, the study’s scope is limited by the environmental quality index and the extent of data coverage, indicating a need for further research expansion.

Public Sector Financial Reforms and Performance of Government Entities in Nigeria

A few scholars have remarked that public agencies in Nigeria are typified by corrupt activities, as well as a slew of other issues. Surprisingly, despite the fact that all succeeding Nigerian politicians and bureaucrats also acknowledge that corruption is a barrier to national growth, the bulk of them assume office as doctors but exit as patients. As a result, Nigerian Government has introduced various economic reforms to boost the performance of government entities in the country. Therefore, this study examined the effects of public sector financial reforms on performance of government entities in Nigeria. Specifically, this study examined the effects of anti-graft reforms, information technology based reforms and financial planning reforms on performance of government entities in Nigeria, The study employed survey research design to collect data from 306 personnel across 188 Ministries, Departments and Agencies (MDA) in Nigeria. Ordered logistic regression and ordinary least square (OLS) regression were employed for data analysis. The study found that anti-graft reforms (Anti-Graft agencies), information technology based reforms (IPPIS and GIFMIS), and financial planning reforms (Budgetary control) significantly enhanced the financial performance of government entities in Nigeria. Likewise, the study revealed that anti- graft reforms (Anti-Graft agencies), information technology based reforms (IPPIS), financial planning reforms (Budgetary procedure) and financial reporting reforms (IPSAS) had significant positive effects on non- financial performance of government entities in Nigeria. Overall results revealed that anti-graft reforms (Anti-Graft agencies, anti-graft legal framework), information technology based reforms (IPPIS and GIFMIS), and financial planning reforms (Budgetary procedure) significantly boosted the financial performance of government entities in Nigeria. The study concluded that public sector financial reforms boosted the performance of government entities in Nigeria, and recommended that the Nigerian government strengthen the agencies implementing public sector financial reforms to boost the performance of government entities better.

Exploring Greenium and the Determinants of Green Bond Performance in Asia

This study examines the presence and determinants of the ‘greenium’ in Asian green bonds. The dataset includes green bonds issued by governments and corporations in Asian countries from 2016 to 2022. To analyse the determinants of the greenium, we applied a coarsened exact matching (CEM) model, an advanced form of the matching method. Ordinary least squares (OLS) regression was employed to assess the factors influencing the greenium. Our findings indicate that a greenium exists in green bonds issued in Asia, with significant effects observed in China, Korea, and Japan. Key drivers of the greenium include coupon rates, issuer type, oil prices, and the federal funds rate. This study contributes practical insights by highlighting that a greenium can be achieved through strategic project selection, transparent communication, and strong environmental commitment. Additionally, investors should account for coupon rates, issuer sector, and macroeconomic factors such as oil prices and the federal funds rate when investing in green bonds.

Analysis of Exchange Rate Stability on the Economic Growth Process of a Developing Country: The Case of South Africa from 2000 to 2023

This study examines the impact of exchange rate stability on the economic growth of South Africa from 2000 to 2023, a period characterised by significant political and economic changes. Exchange rate stability is critical for developing countries, affecting key macroeconomic variables such as trade balances, foreign direct investment (FDI), and inflation. For emerging economies like South Africa, maintaining a stable exchange rate can reduce uncertainty in international transactions, foster investor confidence, and support sustainable economic development. This research explores whether consistent exchange rate management has positively influenced South Africa’s economic trajectory, particularly by mitigating the adverse effects of global shocks and domestic volatility. Using the EasyData online database, which contains yearly time series data, the method of analysis adopted by the research is the ordinary least squares (OLS) regression method. The findings show that while exchange rate stability positively impacts GDP, the influence of FDI and political risk is more substantial. These results underscore the importance of fostering a stable economic environment through sound exchange rate policies, political stability, and efforts to attract foreign investments to ensure long-term economic growth.

Exploring Education-Induced Bargaining Power of Women on Household Welfare in Sub-Saharan Africa

Women’s education and empowerment have engaged the interest of policymakers and academics for many years. We employ individual-level data from Ghana and Uganda in this paper to offer a comparative analysis of the impact of women’s education and empowerment on six household welfare indicators: child labor, child school enrollment, female labor force participation, fertility rate, household food expenditure, and nutrition intake. Comparing the two countries is insightful due to their distinct socio-economic structures and cultural contexts, which might influence the dynamics of women’s empowerment differently. The study utilizes the Ordinary Least Squares (OLS) and Instrumental Variables (IV) regressions and provides a battery of robustness tests. The empirical results show that in a household, the woman’s and man’s education levels are significant determinants of household welfare. However, contrary to common assumptions, the woman’s education does not have a stronger effect than the man’s, and her relative bargaining position has negligible effects on the welfare indicators studied, at least for the cases of Ghana and Uganda. Further sensitivity checks support these findings, suggesting that female education can improve household welfare, but its impact may not necessarily operate through enhanced bargaining power within the household.

The Role of Agricultural Inputs on Crop Productivity: The Case of Zigam Woreda

In spite of the importance of agriculture in Ethiopia, it is characterized by low productivity and has been unable to produce sufficient quantities of output to feed the country’s population. In light of this various development strategies has been undertaken to improve the performance of agriculture. Intensification of agriculture through the use of new agricultural technologies has been emphasized over the last three decades. This study attempted to examine the contribution of agricultural input for crop productivity. The data for the study was collected from 91 sample farmers. This study was the study used both primary and secondary data. In this study researcher was used simple random sampling techniques. This research was use cross sectional approach and econometric method of data analysis to investigate the role of age, sex, land size, labor force, fertilizer, improved seed, extension service, and access to credit, education level and pesticides for crop production by collecting data from the household. In econometric method of data analysis researcher was used ordinary least square (OLS) Model. The econometric result show that land size, labor force, improved seed, fertilizer, credit service, extension service and education level have positive and significant effect on crop production. However, pesticide has a negative and significant impact on crop production. From the explanatory variables, education level has a higher coefficient. This indicates education level is more significant for crop production. According regression result <i>R</i><sup><i>2</i></sup> is 0.97, which implies 97% of output function is explained by the selected ten (10) explanatory variables. The policy implication is that to reduce farmers resistant to use farm inputs and to create knowledge about the optimal input use educate and training of farmers is necessary.

Studying the impact of profitability, bankruptcy risk, and pandemic on narrative tone in annual reports in an emerging market in the East

The purpose of this paper is to investigate whether the narrative tone of annual reports is influenced by profitability, bankruptcy risk, and pandemic in the context of Vietnam. The study applies the necessary regression analysis steps such as ordinary least squares (OLS), random effects model (REM), fixed effects model (FEM), and feasible generalized least squares (FGLS). Bootstrapping and System Generalized Method of Moments (SGMM) methods are used to test the robustness and address endogeneity and dynamic relationships in the data. The findings show that net tones increase while negative tones decrease in companies with high profitability. Companies at risk of bankruptcy use more negative tone and less net tone than companies that are not at risk of bankruptcy. The study also affirms that bankruptcy risk moderates the relationship between profitability and negative tone. During the COVID-19 pandemic, companies used more positive and negative tones than they did before the pandemic. After the COVID-19 situation stabilized, positive tones were used more, and negative tones were used less than during the outbreak. The research results also recognized that companies do not intend to hide information through impression management when faced with difficult economic conditions. This study has practical implications for investors and information users when considering management disclosures through the narrative tone of annual reports. To our knowledge, this is the first study (1) in an emerging market in the East—where there are fundamental cultural and linguistic differences compared to Western countries, (2) to build a list of Vietnamese words and phrases expressing emotional nuances used in finance and accounting, and (3) refers to the narrative tone of annual reports across different groups of companies.

IS THE OIL AND GAS BLOCK POLICY IN IDONESIA EFFECTIVE? EMPIRICAL APPROACH BASED ON COMPANY RATING DEVELOPMENTS

Objective: This study aims to identify the empirical influence of the assessment criteria for Indonesian oil and gas auctions on total oil and gas block production. The role of remaining concession time, contract policy and block size are considered. Theoretical Framework: In this topic, the concepts and agency theories underlying the research are presented, providing a strong basis for understanding the context of the influence of oil and gas block policies in Indonesia. Method: This research uses an exploratory quantitative approach. The research sample consisted of 157 petroleum and 140 natural gas blocks that had carried out production activities. Ordinary least square (OLS) regression used to empirically test the hypothesis. Results and Discussion: The results show that the ranking of oil and gas companies has a significant positive effect on the productivity of blocks. Meanwhile, remaining concession time has a significant positive relationship only for the natural gas. This research also found a significant negative effect of the gross split contract on block production. Finally, it was found that company ranking had a positive and significant effect on production both in large and small blocks. However, the relationship between ranking and productivity is stronger and more consequential for large-scale blocks compared to small-scale blocks. Research Implications: Contract policymakers need to review the gross split policy because it is not in line with the initial goal of increasing efficiency. The results clearly show that strict government policies can reduce agency costs while encouraging oil and gas production, however, this can hinder the competitiveness of the sector. The government does not need to give excessive priority to high-ranking companies in managing small-scale blocks in order to attract new investors. Originality/Value: To the best of our knowledge, this research is the first study to develop a ranking method for oil and gas companies in Indonesia.

Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning

Understanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design.

Exploring vocal biomarkers as non-invasive fine-tuning assays of cardiovascular health: heart failure model

Abstract Background Mobile health (mHealth) technologies offer a promising avenue for non-invasive heart failure monitoring through vocal biomarkers. The relationship between feature changes in anonymous and standardized voice recordings of heart failure patients and the NYHA scores of the same patients is assessed, aiming to identify vocal biomarkers as delicate indicators of chronic noncommunicable disease progression and/ or fluctuation. Methods In a cardiology outpatient setting, 55 chronic heart failure patients provided voice recordings via a mobile app, performing specific vocal tasks prospectively. We analyzed waveforms visually via Mel spectrograms and extracting features by audio processing, focusing on chroma centroids and tonnetz measurements across 147 clinical and laboratory parameters (Mean NYHA: 2.62, SD: 0.56). Applying Ordinary Least Squares (OLS) test, we assessed the relevance of spectral bandwidth mean (0.04), spectral bandwidth std (0.00), spectral roll-off std (0.011) between NYHA scores, given each p-score < 0.05. Results 147 voice recordings taken from 55 patients were analyzed to examine whether there were any correlations between changes in the voice recordings and NYHA scores. OLS test underscored the significance of spectral bandwidth mean (0.04), spectral bandwidth std (0.00), spectral roll-off std (0.011) between NYHA scores with p-value < 0.05. These findings show the potential role of multiple vocal features in reflecting heart failure progression and/or fluctuation. Analysis based on the clinical status progression from NYHA 2 to NYHA 3 over time showed notable changes in chroma centroids (mean = 0.006, std = 0.174, skew = 0.179, kurtosis = 0.205) and tonnetz (mean = 0.023, std = 0.131, skew = 0.023, kurtosis = 4.247). Conclusion This investigation highlights vocal biomarkers' potential in heart failure management, offering insights into non-invasive monitoring techniques and shows the importance of standardised recording procedures and patient engagement for data integrity. Future directions include quantifying vocal changes' correlation with clinical status and integrating additional parameters (e.g., edema, weight) to enhance predictive models for personalised care.Vocal features according to NYHA scores