{"paper_id":"4d754e68-15cc-44d9-b91e-e080ba44c7ac","body_text":"Financial Flexibility and Firm Performance: Evidence from an Emerging Market | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Financial Flexibility and Firm Performance: Evidence from an Emerging Market Ayse Soy Temur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9538172/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Financial flexibility is a critical corporate capability that enables firms to withstand financial shocks and exploit investment opportunities. This study examines the determinants of financial flexibility and its relationship with firm performance using quarterly panel data from an emerging market context. The empirical sample consists of 25 non-financial firms included in the BIST30 index over the period 2010Q1–2025Q2. The analysis employs cross-sectional dependence tests, second-generation unit root procedures, panel model selection tests, and Driscoll–Kraay robust estimation. The findings indicate that return on assets positively affects financial flexibility, whereas leverage has a strong negative effect. Firm size is also negatively associated with flexibility, while net profit margin is statistically insignificant. The results suggest that profitability enhances firms’ financial resilience, whereas excessive indebtedness constrains financial maneuverability. The study contributes to the literature by providing long-term panel evidence from an emerging market using a composite measure of financial flexibility that jointly captures liquidity strength and indebtedness. Purpose This study aims to examine how profitability and capital structure decisions influence financial flexibility in an emerging market context. It also seeks to provide new firm-level evidence from Turkey using listed companies. Design/methodology The study uses quarterly panel data for 25 non-financial BIST30 firms covering the period 2010Q1–2025Q2. A comprehensive panel econometric framework is employed, including model selection procedures and Driscoll–Kraay robust standard errors. Findings Return on assets has a positive and statistically significant effect on financial flexibility, while leverage has a strong negative effect. Firm size is negatively associated with flexibility, whereas net profit margin is not statistically significant. Practical implications Managers should consider leverage decisions not only in terms of financing costs but also in relation to preserving long-term financial flexibility. Strong internal profitability may improve firms’ resilience during uncertain economic conditions. Originality/value This study contributes to the literature by using a long-term quarterly panel dataset from an emerging market. It also measures financial flexibility through a composite indicator that jointly reflects liquidity strength and indebtedness. The evidence provides new insights into firm-level financial resilience in volatile markets. JEL Classification: C33, G30, G31, G32 Financial flexibility Firm performance Panel data Capital structure Emerging markets 1. Introduction Financial flexibility refers to a firm's ability to preserve its financial structure in the face of unexpected shocks and to exploit emerging investment opportunities. Recent evidence suggests that financial flexibility strengthens firms’ financial resilience and helps sustain performance during periods of uncertainty (Campello et al., 2010 ; Akbar et al., 2021 ). Rising macroeconomic volatility and global crises have highlighted the importance of being strong not only in profitability terms but also in terms of financial adaptability. In this context, financial flexibility reflects firms’ capacity to respond through liquidity management, borrowing capacity, and financing strategies. Recent studies also show that financial flexibility improves financing efficiency and resource allocation decisions (Butt et al., 2023 ; Li et al., 2025 ; Xu et al., 2024). Financial flexibility has been defined from different perspectives in the literature. The Financial Accounting Standards Board (FASB, 1984) defines financial flexibility as the ability to change the timing and amount of cash flows in response to unexpected opportunities and economic shocks. Similarly, the American Institute of Certified Public Accountants (AICPA, 1993) describes financial flexibility as the ability to adjust expected cash inflows and cash outflows. Fitch Ratings ( 2015 ), a credit rating agency, defines financial flexibility as a firm’s capacity to meet its financial obligations while maintaining its credit quality and managing financial pressures. In the academic literature, financial flexibility is generally discussed within the framework of liquidity management and borrowing capacity. Myers ( 1984 ) defines financial flexibility as a firm's ability to utilize liquidity and borrowing capacity to exploit temporary investment opportunities or absorb financial shocks. Opler, Pinkowitz, Stulz, and Williamson ( 1999 ) emphasize the importance of maintaining sufficient cash reserves to meet unexpected financing needs and to take advantage of investment opportunities. Gamba and Triantis ( 2008 ) describe financial flexibility as the ability to obtain financing at low cost and to respond effectively to uncertainty, while Denis and McKeon ( 2012 ) highlight that financial flexibility is shaped by both cash reserves and borrowing capacity. Arslan-Ayaydin et al. ( 2014 ) demonstrate that financially flexible firms are more successful in maintaining their operations and capturing strategic opportunities, particularly during crisis periods. There are also studies examining the relationship between financial flexibility and firm performance in the context of Turkey. Research conducted in Turkey indicates that financial flexibility has significant effects on firms’ investment behavior and performance, especially during financial crisis periods. For instance, Arslan-Ayaydin et al. ( 2014 ) show that financially flexible firms are able to maintain higher investment capacity during crisis periods. Other studies focusing on Turkey also indicate that financial structure decisions, particularly borrowing policies and liquidity management, play a decisive role in firm performance. These findings suggest that financial flexibility is an important factor that enhances firms’ financial resilience in emerging markets. Despite the growing body of literature on financial flexibility, existing studies predominantly focus on developed markets or rely on limited time horizons and single-measure approaches. Moreover, the dynamic interaction between firm-specific financial characteristics and financial flexibility remains underexplored in emerging market contexts. In particular, there is a lack of comprehensive empirical evidence based on long-term quarterly data that captures structural changes across different economic periods. This gap is particularly relevant for emerging economies characterized by higher financial constraints and macroeconomic volatility. To address this gap, this study examines the relationship between financial flexibility and firm performance using quarterly panel data from an emerging market context. The empirical sample consists of 25 non-financial firms included in the BIST30 index over the period 2010Q1–2025Q2. Firm performance is represented by return on assets (ROA), return on equity (ROE), and net profit margin (NPM), while firm size and leverage are included as control variables. By employing a composite measure of financial flexibility that jointly captures liquidity strength and indebtedness, the study provides new evidence on firm-level financial resilience in emerging markets. This study contributes to the literature in several ways. First, it extends existing evidence on financial flexibility and firm performance in an emerging market context. Second, it employs a comprehensive panel data framework with robust estimation techniques that account for cross-sectional dependence and related econometric issues. Third, financial flexibility is measured using a composite indicator that jointly captures liquidity and borrowing capacity, offering a broader perspective than single-measure approaches. The remainder of the paper is organized as follows. The second section reviews the literature on financial flexibility and firm performance. The third section describes the data set, variables, and research methodology. The fourth section presents the empirical findings. The fifth section discusses the results, and the final section concludes the study and provides policy implications. 2. Conceptual Framework and Literature Review The concept of financial flexibility has been examined from various perspectives in the literature and is generally associated with firms’ ability to adapt to unexpected economic shocks and to take advantage of emerging investment opportunities. For this reason, financial flexibility is considered an important financial characteristic that contributes to firms’ ability to maintain financial resilience during periods of economic uncertainty and to ensure long-term financial sustainability. In recent years, increasing global economic uncertainty, fluctuations in financial markets, and experiences from financial crises have highlighted the importance of firms being strong not only in terms of profitability but also in terms of financial resilience. This situation has increased the need to examine the role of financial flexibility in firm performance more comprehensively. Recent studies also show that financial flexibility represents an important strategic factor that enhances firms’ resilience against economic crises and financial constraints (Campello et al., 2010 ; Akbar et al., 2021 ). In the literature, financial flexibility is often associated with firms’ investment decisions. Financially flexible firms are considered to be better able to evaluate emerging investment opportunities and are less affected by financial constraints. Opler et al. ( 1999 ) demonstrate that firms with high cash reserves are better able to take advantage of investment opportunities and mitigate the adverse effects of financial constraints. Similarly, Denis and McKeon ( 2012 ) argue that financial flexibility arises not only from cash reserves but also from borrowing capacity, and that these two components together enhance firms’ ability to adapt to changing market conditions. More recent studies also highlight that financial flexibility is closely related to capital structure policies, cash management, and investment decisions (Dang et al., 2018 ; Gao, Harford and Li, 2020 ). Another issue frequently examined in the literature is the impact of financial flexibility on firm performance. Firm performance is commonly measured using accounting-based profitability indicators. In this context, return on assets (ROA) reflects the ability of assets to generate income, return on equity (ROE) represents the return generated for shareholders, and net profit margin (NPM) indicates profitability relative to sales. While some studies find a positive relationship between financial flexibility and firm performance, others suggest that this relationship may vary depending on sectoral characteristics, financing policies, and macroeconomic conditions. Recent studies indicate that the effects of financial flexibility on investment behavior, risk management, and firm performance become more pronounced during periods of economic uncertainty (Bonaimé, Hankins and Jordan, 2020 ; Habib, Hasan and Al-Hadi, 2021 ). Empirical studies generally indicate a positive relationship between financial flexibility and firm performance. Financially flexible firms are better able to exploit investment opportunities, face fewer financing constraints, and are more resilient to economic shocks (Gamba and Triantis, 2008 ; Denis and McKeon, 2012 ; Akbar et al., 2021 ). Using panel data analysis, Arslan-Ayaydin et al. ( 2014 ) show that financially flexible firms exhibit higher performance particularly during crisis periods. Similarly, Gamba and Triantis ( 2008 ) demonstrate that firms with strong liquidity positions and better access to low-cost financing tend to achieve superior financial performance. Myers ( 1984 ) provides a theoretical explanation of financial flexibility by defining it as firms’ ability to maintain financial stability while exploiting temporary investment opportunities. Panel data analysis is widely used in studies examining this relationship. Panel data methods allow researchers to control for unobserved heterogeneity across firms and time effects. Empirical studies using panel data generally find that cash holdings have a positive effect on firm performance, whereas leverage tends to have a negative impact (Opler et al., 1999 ; Arslan-Ayaydin et al., 2014 ). These findings suggest that financial flexibility may provide a strategic advantage for firms operating in emerging markets characterized by high macroeconomic volatility. However, empirical evidence on the relationship between financial flexibility and firm performance in emerging economies remains relatively limited. This issue is particularly important in countries such as Turkey, where macroeconomic volatility is relatively high. In this context, the present study aims to contribute to the literature by examining the relationship between financial flexibility and firm performance for firms listed in the BIST30 index using panel data analysis. In addition, based on the pecking order theory and the financial flexibility literature, it is expected that profitability and financial structure indicators may play a determining role in financial flexibility. Based on this theoretical framework and the existing empirical findings, the following research hypotheses are developed. Research Hypotheses H 1 Return on assets (ROA) has a positive and significant effect on financial flexibility. H 2 Return on equity (ROE) has a significant effect on financial flexibility. H 3 Net profit margin (NPM) has a positive and significant effect on financial flexibility. H 4 Firm size (SIZE) has a significant effect on financial flexibility. H 5 Leverage ratio (LEV) has a negative and significant effect on financial flexibility. 3. Data, Variables and Measurement This section provides information on the data used in the study and the research methodology. 3.1. Data Set This study employs a quarterly panel data set covering the period 2010:Q1–2025:Q2. The data set consists of publicly disclosed financial statements of non-financial firms listed on Borsa Istanbul (BIST) and included in the BIST30 index. The financial statement data used in the analysis were obtained from the Public Disclosure Platform (KAP), which is the official reporting system through which publicly traded companies in Turkey disclose their financial reports. A balanced panel data structure was constructed within the scope of the study, and a total of 62 quarterly observations for 25 firms were analyzed. Firms operating in the banking and financial sectors were excluded from the sample. The primary reason for this exclusion is that the balance sheet structures of these sectors differ structurally from other industries due to their high leverage levels and sector-specific regulatory frameworks. In financial institutions, borrowing levels are inherently high as part of their business model, and regulatory requirements play a decisive role in determining capital adequacy. These characteristics reduce the comparability of financial flexibility indicators across sectors. Therefore, in order to maintain sample homogeneity and prevent the analysis results from being influenced by structural differences across sectors, financial firms were excluded from the study. The use of quarterly data enables a more precise observation of the responses of financial flexibility to macroeconomic shocks and short-term financial fluctuations. Particularly in emerging markets where financial volatility tends to be high, annual data may fail to adequately capture changes in firms’ financial structures. In this context, the use of higher-frequency data allows for a more detailed analysis of the dynamics of financial flexibility. All analyses in this study were conducted using the R programming language. As the empirical methodology, a panel data analysis approach was adopted, which allows simultaneous examination of both the time-series and cross-sectional dimensions of the data. Within the panel data framework, Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models were estimated. To determine the most appropriate model specification, the F test, Breusch–Pagan Lagrange Multiplier test, and Hausman test were applied. Furthermore, in order to address potential autocorrelation, heteroskedasticity, and cross-sectional dependence problems detected in the error terms, Driscoll–Kraay (1998) robust standard errors were employed to obtain corrected coefficient estimates. The sample consists of 25 non-financial firms listed in the BIST 30 index (Table 1 ). Table 1 Firms included in the sample No Company Stock Code Sektor 1 Anadolu Efes Biracılık ve Malt Sanayii A.S. AEFES Manufacturing, Food, Beverages and Tobacco 2 Aselsan Elektronik Sanayi ve Ticaret A.S. ASELS Technology, Defense 3 BİM Birlesik Magazalar A.S. BIMAS Wholesale and Retail Trade 4 Çimsa Çimento Sanayi ve Ticaret A.S. CIMSA Stone and Soil Based Manufacturing 5 Emlak Konut Gayrimenkul Yatırım Ortaklıgı A.S. EKGYO Real Estate Investment Trust 6 Enka İnsaat ve Sanayi A.S. ENKAI Construction and Public Works 7 Eregli Demir ve Çelik Fabrikaları T.A.S. EREGL Basic Metal Industry 8 Ford Otomotiv Sanayi A.S. FROTO Metal Goods, Machinery, Electrical Devices and Transportation Vehicles 9 Gubre Fabrikaları T.A.S. GUBRF Chemicals, Pharmaceuticals, Rubber and Plastic Products 10 Hacı Omer Sabancı Holding A.S. SAHOL Holdings and Investment Companies 11 Kardemir Karabuk Demir Çelik Sanayi ve Ticaret A.S. KRDMD Basic Metal Industry 12 Koç Holding A.S. KCHOL Holdings and Investment Companies 13 Koza Altın İsletmeleri A.S. KOZAL Mining and Quarrying 14 Migros Ticaret A.S. MGROS Wholesale and Retail Trade 15 Pegasus Hava Tasimaciligi A.S. PGSUS Transportation and Storage 16 Petkim Petrokimya Holding A.S. PETKM Chemicals, Pharmaceuticals, Rubber and Plastic Products 17 Sasa Polyester Sanayi A.S. SASA Chemicals, Pharmaceuticals, Rubber and Plastic Products 18 Tav Havalimanlari Holding A.S. TAVHL Holdings and Investment Companies 19 Tofas Turk Otomobil Fabrikasi A.S. TOASA Metal Goods, Machinery, Electrical Devices and Transportation Vehicles 20 Turkcell İletisim Hizmetleri A.S. TCELL Information and Communication, Telecommunications 21 Tupras-Turkey Petrol Rafinerileri A.S. TUPRS Chemicals, Pharmaceuticals, Rubber and Plastic Products 22 Turk Hava Yollari A.O. THYAO Transportation and Storage 23 Turkey Sise ve Cam Fabrikalari A.S. SISE Holdings and Investment Companies 24 Turk Telekomunikasyon A.S. TTKOM Information and Communication, Telecommunications 25 Ulker Biskuvi Sanayi A.S. ULKER Food, Beverage and Tobacco Note : Banks and financial institutions were excluded from the analysis due to the absence of certain key variables, such as inventories, in their financial statements. The table is constructed based on the firms included in the BIST30 index as of August 2025. 3.2. Variables and Measurement In this study, firm performance is measured using accounting-based indicators, namely return on assets (ROA), return on equity (ROE), and net profit margin (NPM). ROA reflects the extent to which a firm efficiently utilizes its assets, ROE represents the return generated for shareholders, and NPM captures operating profitability and sales efficiency. When considered together, these three indicators allow for a multidimensional evaluation of a firm’s operational efficiency and financial performance. In the literature, firm performance is often measured using market-based indicators such as Tobin’s Q. However, in this study accounting-based performance indicators are preferred due to the long analysis period and the use of quarterly data, which may create limitations in terms of consistency and comparability of market-based data across periods. In particular, in emerging markets, market values tend to exhibit higher volatility and may not always be fully synchronized with financial statements. Therefore, the use of accounting-based performance measures is considered methodologically more appropriate. This approach is widely adopted in financial performance analyses conducted in emerging market contexts. Financial flexibility (FLEX) is included in the model as the dependent variable. Financial flexibility is measured using a composite indicator that simultaneously reflects the firm’s liquidity capacity and borrowing level. This structure captures the multidimensional nature of the concept by considering both the firm’s financial buffer and its financial obligations. Firm size (SIZE) and financial leverage (LEV) are included in the model as control variables. Firm size is calculated as the natural logarithm of total assets. The logarithmic transformation reduces the effect of scale differences and helps normalize the distribution of the variable. The financial leverage ratio is calculated as total debt divided by total assets and represents the firm’s financial risk level and capital structure. In the literature, firm size and leverage are widely recognized as important determinants of both financial flexibility and firm performance. Therefore, these variables are controlled in the model in order to analyze the main relationships more accurately. The variables used in the analysis include measures of financial flexibility, profitability, firm size, and leverage (Table 2 ). Table 2 Definitions and measurement of variables Variable Symbol Measurement Description Data Source Financial Flexibility FLEX (Cash and Cash Equivalents / Total Assets) − (Total Debt / Total Assets) A composite indicator that reflects both the firm's liquidity capacity and borrowing level. Quarterly financial statements of firms (KAP) Return on Assets ROA Net Income / Total Assets Measures the firm's ability to generate profit from its assets. Income statement and balance sheet Return on Equity ROE Net Income / Shareholders’ Equity Measures the profitability of shareholders’ investments. Income statement and balance sheet Net Profit Margin NPM Net Income / Net Sales Indicates operating efficiency and profitability of sales. Income statement Control Variables Firm Size SIZE ln(Total Assets) Represents the scale of the firm. Financial statements Leverage Ratio LEV Total Debt / Total Assets Represents the firm’s capital structure and level of financial risk. Financial statements 3.3. Measurement of Financial Flexibility There are different approaches in the literature regarding the measurement of financial flexibility. Some researchers represent financial flexibility solely through the cash holding ratio (Opler et al., 1999 ; Arslan-Ayaydin et al., 2014 ). This approach focuses on firms’ precautionary cash holding behavior in order to cope with financial shocks. Another approach defines financial flexibility in terms of borrowing capacity or low leverage levels, emphasizing the firm’s ability to access external financing in the future (Myers, 1984 ; Gamba and Triantis, 2008 ). However, measuring financial flexibility using single-dimensional indicators may not fully capture the multidimensional nature of the concept. Recent studies suggest that financial flexibility can be better represented through composite indicators that simultaneously consider liquidity reserves and borrowing capacity (Byoun, 2008 ; Denis and McKeon, 2012 ; Akbar et al., 2021 ; Shojaee and Mirzaei, 2024 ). This perspective assumes that financial flexibility depends not only on the current level of cash holdings but also on the firm's potential borrowing capacity in the future. Accordingly, financial flexibility in this study is measured using the following composite indicator: \\(\\:{\\text{FLEX}}_{it}=\\frac{{\\text{Cash and Cash Equivalents}}_{it}}{{\\text{Total Assets}}_{it}}-\\frac{{\\text{Total Debt}}_{it}}{{\\text{Total Assets}}_{it}}\\) This formulation simultaneously considers the firm's liquidity capacity and debt level. The first component represents the firm's financial buffer capacity, while the second component reflects its financial obligations. Thus, financial flexibility increases with higher cash reserves and lower leverage, and decreases when firms exhibit lower liquidity and higher debt levels. The main reason for adopting this measurement approach is that financial flexibility should not be considered merely as a static liquidity indicator but rather as a strategic aspect of capital structure decisions. The use of a composite indicator allows financial flexibility to be represented in a more balanced and comprehensive manner, enabling a more accurate analysis of its relationship with firm performance. Furthermore, measuring financial flexibility using a composite indicator helps reduce potential measurement errors compared to single-variable proxies and better reflects the dynamic nature of the concept. This approach is particularly appropriate for emerging markets, where differences in financial structures across firms tend to be more pronounced. 3.4. Model Specification and Estimation Method This study examines the relationship between financial flexibility and firm performance for non-financial firms included in the BIST30 index using a panel data analysis approach. The panel data framework allows the simultaneous consideration of both the time-series and cross-sectional dimensions of the data and enables the control of unobserved firm-specific effects. Consequently, panel data models provide more consistent and efficient estimates compared to analyses based solely on time-series or cross-sectional data (Baltagi, 2005 ). Within the research framework, financial flexibility (FLEX) is treated as the dependent variable, while variables representing firm performance and financial structure are included as explanatory variables. Accordingly, the baseline panel data model estimated in this study is specified as follows: $$\\:{FLEX}_{\\_it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{ROA}_{\\_it}+{\\beta\\:}_{2}{ROE}_{\\_it}+{\\beta\\:}_{3}{NPM}_{\\_it}+{\\beta\\:}_{4}{SIZE}_{\\_it}+{\\beta\\:}_{5}{LEV}_{\\_it}+{u}_{\\_it}$$ Where \\(\\:i\\) denotes firms and \\(\\:t\\:\\) denotes time. ROA, ROE, and NPM represent profitability indicators, SIZE represents firm size, and LEV denotes the financial leverage ratio. The coefficients \\(\\:\\beta\\:\\) capture the marginal effects of the explanatory variables on financial flexibility, while \\(\\:{u}_{it}\\) represents the error term. The signs of the estimated coefficients are interpreted in line with theoretical expectations suggested in the financial flexibility literature. Empirical analyses were conducted following a systematic econometric procedure. First, descriptive statistics were calculated in order to evaluate the basic characteristics of the dataset. Considering the potential interdependence among cross-sectional units in panel data, cross-sectional dependence was tested using the Pesaran (2004; 2015 ) CD test. Since cross-sectional dependence was detected, the stationarity properties of the variables were examined using the Pesaran ( 2007 ) CIPS test, which is a second-generation panel unit root test that accounts for cross-sectional dependence. In the model estimation stage, Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models were estimated separately. To determine the most appropriate model specification, the F test, Breusch–Pagan Lagrange Multiplier test, and Hausman test were employed. In addition, the classical assumptions regarding the error terms were tested for the selected model, and the presence of autocorrelation, heteroskedasticity, and cross-sectional dependence was identified. Therefore, coefficient estimates were recalculated using Driscoll–Kraay (1998) robust standard errors, which are robust to heteroskedasticity, serial correlation, and cross-sectional dependence. Potential endogeneity issues are widely discussed in panel data analyses in the literature. Although potential endogeneity issues are acknowledged in panel data analysis, the model specification and variable structure employed in this study help mitigate such concerns. The empirical findings obtained from these analyses are presented in the following section. 4. Empirical Findings This section first presents the descriptive statistics of the variables used in the study. Subsequently, the results of the cross-sectional dependence test and panel unit root tests are reported. Finally, the findings obtained from the panel data estimations are presented. 4.1. Descriptive Statistics Table 3 presents the descriptive statistics of the variables used in the empirical analysis. The results indicate considerable variation across firms and over time. The dataset consists of a balanced panel of 25 firms over the period 2010Q1–2025Q2, resulting in a total of 1,550 observations. These statistics provide preliminary insights into the distribution and variability of the variables used in the study. Table 3 Descriptive statistics Variable Mean Median Min Max Std. Dev. n FLEX -0.4383 -0.4636 -0.8508 0.6252 0.2478 1550 ROA 0.0208 0.0175 -0.1780 0.2908 0.0273 1550 ROE 0.0565 0.0434 -3.6251 6.1505 0.2348 1550 NPM 0.1496 0.0888 -6.1275 24.091 0.7179 1550 SIZE 18.6742 17.9459 10.7862 26.5805 3.0033 1550 LEV 0.5835 0.6095 0.0747 0.9990 0.1923 1550 The descriptive statistics indicate that the average value of the financial flexibility variable (FLEX) is negative (–0.4383). This finding suggests that, on average, the firms in the sample exhibit higher leverage levels relative to their liquidity buffers over the sample period. The wide range between the minimum and maximum values also indicates substantial variation in financial flexibility across firms. When the profitability indicators are examined, both ROA and ROE exhibit positive average values. However, the wide ranges observed in ROE and NPM suggest the presence of considerable fluctuations over time and the existence of unusually high or low performance levels for some firms. In particular, the relatively high maximum value of the NPM variable indicates the presence of outliers during certain periods. The average value of the firm size variable (SIZE) confirms that the sample consists primarily of large-scale firms. Meanwhile, the average leverage ratio (LEV) indicates that approximately 58% of firms’ assets are financed through debt. Overall, the standard deviation values of the variables suggest substantial variation both across firms and over time. This finding provides methodological support for the use of panel data analysis, which simultaneously accounts for both cross-sectional and temporal variation. 4.2. Cross-Sectional Dependence and Unit Root Tests In panel data analysis, it is essential to determine whether cross-sectional dependence exists among the units in the panel in order to obtain reliable estimates. If cross-sectional dependence is present, first-generation panel unit root tests may produce inconsistent results. Therefore, second-generation panel unit root tests are recommended in such cases. Accordingly, the presence of cross-sectional dependence in the panel dataset was examined using the Pesaran (2004; 2015 ) Cross-Sectional Dependence (CD) test. H₀ There is no cross-sectional dependence among the panel units. H₁ There is cross-sectional dependence among the panel units. Table 4 Cross-sectional dependence test (Pesaran CD) Test Statistic p-value Result Pesaran CD Z = 6.221 < 0.001 Cross-sectional dependence exists Table 4 reports the results of the Pesaran CD test for cross-sectional dependence. The test statistic is z = 6.221 with a probability value of p < 0.001. Therefore, the null hypothesis is rejected, indicating the presence of cross-sectional dependence among the firms in the panel. In other words, firms in the sample appear to be simultaneously affected by common macroeconomic conditions, financial market developments, and sectoral shocks. The existence of cross-sectional dependence implies that second-generation panel unit root tests that account for this dependence should be employed when examining the stationarity properties of the variables. Therefore, the stationarity of the variables was tested using the Pesaran ( 2007 ) CIPS (Cross-sectionally Augmented IPS) panel unit root test. The hypotheses of the CIPS test are defined as follows: H₀ The series contain a unit root (non-stationary). H₁ The series are stationary. Table 5 Panel unit root test results (Pesaran CIPS) Variable CIPS Statistic Lag p-value Stationarity FLEX -2.1150 1 0.10 I(0)* ROA -3.9721 1 0.01 I(0)*** ROE -3.9549 1 0.01 I(0)*** NPM -3.9266 1 0.01 I(0)*** SIZE -1.9796 1 0.10 I(0)* LEV -2.5648 1 0.10 I(0)* Note : Tests are conducted under a model including both constant and trend. *, **, *** denote significance levels of 10%, 5%, and 1% , respectively. Table 5 presents the results of the Pesaran CIPS panel unit root test. The findings indicate that ROA, ROE, and NPM are stationary at the 1% significance level, while FLEX, SIZE, and LEV are stationary at the 10% significance level. These results suggest that all variables used in the study are stationary at their level values, implying that the series do not contain unit roots. After confirming the stationarity of the variables at their levels, a correlation analysis was conducted to examine the pairwise relationships among the variables before proceeding with the panel regression analysis. 4.3. Correlation Analysis The correlation matrix indicates a strong negative relationship between financial flexibility and leverage, while the relationship between financial flexibility and profitability remains positive but moderate. The detailed coefficients are reported in Table 6 . Table 6 Correlation matrix FLEX FLEX ROA ROE NPM SIZE LEV 1.000 - - - - - ROA 0.377 1.000 - - - - ROE 0.023 0.327 1.000 - - - NPM 0.102 0.265 0.071 1.000 - - SIZE -0.084 -0.073 0.028 -0.035 1.000 - LEV -0.895 -0.286 -0.009 -0.085 0.008 1.000 The correlation results indicate that financial flexibility (FLEX) is positively related to return on assets (ROA) and strongly negatively related to leverage (LEV). This suggests that higher levels of debt may reduce financial flexibility. The relationships among the other variables are generally weak to moderate. Since the absolute values of the correlation coefficients are generally below 0.80, the results suggest that there is no serious multicollinearity problem in the model. To further examine the potential presence of multicollinearity, Variance Inflation Factor (VIF) values were calculated for all variables. The results show that all VIF values are below the commonly accepted threshold of 5, confirming that multicollinearity is not a significant concern and supporting the reliability of the estimated results. Based on these findings, panel data analysis was employed to analyze the relationships among the variables by considering both the time and cross-sectional dimensions simultaneously. The most commonly used panel data models in this context are Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models. 4.4. Model Selection and Diagnostic Tests In order to determine the appropriate panel data model, a series of model selection tests were conducted. The most commonly used panel data models in the literature include Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models (Baltagi, 2005 ). To determine the most appropriate model among these alternatives, the F test, Breusch–Pagan Lagrange Multiplier (LM) test, and Hausman test were applied sequentially. In addition, the assumptions related to the error terms that may affect the validity of the selected model were examined using the Wooldridge test for autocorrelation and the Modified Wald test for heteroskedasticity. As a starting point for the panel data analysis, the pooled OLS model was estimated. The results obtained from the balanced panel structure (n = 25, T = 62, N = 1,550) indicate that the model is statistically significant overall (F = 1436.79; p < 0.01). However, since this model does not account for unobserved firm-specific heterogeneity, it is considered only as a baseline reference model. Table 7 reports the results of the model selection and diagnostic tests. Table 7 Model selection and diagnostic tests Test Null Hypothesis (H₀) Statistic p-value Decision F Test (Individual Effects) No individual effects (Pooled OLS is appropriate) F = 65.805 < 0.01 H₀ rejected → FE/RE Breusch–Pagan LM Test No random effects (Pooled OLS is appropriate) χ² = 10485 < 0.01 H₀ rejected → RE Hausman Test Random effects estimator is consistent and efficient χ² = 4.804 0.440 H₀ not rejected → RE Wooldridge Test No autocorrelation F = 227.59 < 0.01 H₀ rejected The diagnostic test results indicate that estimating the panel data structure using the pooled regression model is not appropriate. The results of both the F test and the Breusch–Pagan LM test reveal that individual effects are statistically significant, suggesting that either the fixed effects or the random effects model should be preferred. The choice between the fixed effects and random effects models was evaluated using the Hausman test, and the results indicate that the random effects model is consistent and efficient. However, the Wooldridge test reveals the presence of autocorrelation in the error terms. Therefore, in order to address potential econometric issues such as autocorrelation, heteroskedasticity, and possible cross-sectional dependence, the model coefficients were estimated using robust standard errors that are resistant to these problems. 4.5. Panel Regression Results Accordingly, the model coefficients were re-estimated using the Driscoll–Kraay robust standard error estimator. The Driscoll–Kraay method provides consistent estimates that are robust to heteroskedasticity, serial correlation, and cross-sectional dependence. Table 8 reports the panel regression results based on the Driscoll–Kraay estimator. Table 8 Panel regression results (Driscoll–Kraay robust standard errors) Variables Coefficient Std. Error t-Statistic p-value ROA 0.7046 0.1615 4.3628 0.0000*** ROE -0.0133 0.0057 -2.3498 0.0189** NPM 0.0023 0.0014 1.5951 0.1109 SIZE -0.0047 0.0011 -4.3995 0.0000*** LEV -0.9606 0.0254 -37.8378 0.0000*** Notes : Robust standard errors based on the Driscoll–Kraay estimator. *** p < 0.01, ** p < 0.05, * p < 0.10 The estimation results reported in Table 8 indicate that return on assets (ROA) has a positive and statistically significant effect on financial flexibility at the 1% level. This finding suggests that higher profitability improves firms’ financial flexibility. The return on equity (ROE) variable is found to be negative and statistically significant at the 5% level. This result indicates that increases in equity profitability have a limited but negative effect on financial flexibility. The coefficient of the net profit margin (NPM) variable is not statistically significant. This finding suggests that net profit margin does not have a meaningful impact on financial flexibility. The firm size (SIZE) variable is negative and significant at the 1% level. This result implies that financial flexibility tends to decrease as firm size increases. Finally, the leverage ratio (LEV) variable is found to be negative and highly significant. This result indicates that an increase in the level of indebtedness significantly reduces financial flexibility. These results provide robust empirical evidence on the determinants of financial flexibility for firms operating in the BIST30 index. Overall, the results obtained using Driscoll–Kraay robust standard errors appear to be robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This strengthens the reliability of the empirical findings reported in this study. To further evaluate the robustness of the findings, the model was also examined under alternative specifications. The results indicate that the signs and statistical significance levels of the coefficients remain largely consistent, suggesting that the findings are not sensitive to model assumptions and are empirically robust. 5. Discussion of Findings The empirical findings obtained using Driscoll–Kraay robust standard errors indicate that financial flexibility in BIST30 firms is primarily determined by profitability capacity and capital structure. In particular, the positive and highly significant coefficient of the return on assets (ROA) variable suggests that as firms’ operational efficiency increases, their financial maneuverability also expands. This result is consistent with the pecking order theory proposed by Myers ( 1984 ), which argues that internal funding capacity is one of the main determinants of financial flexibility. Moreover, it supports the findings of Opler et al. ( 1999 ) and Denis and McKeon ( 2012 ) regarding the positive relationship between cash reserves and investment capacity. The negative and significant coefficient of the return on equity (ROE) variable represents a finding that has been reported more rarely in the literature. This result may indicate that firms with higher equity returns tend to adopt more aggressive dividend policies or pursue financing strategies involving higher levels of risk. Therefore, higher ROE does not necessarily imply greater financial flexibility. In this context, the result may reflect a trade-off between performance maximization and financial resilience. The fact that the net profit margin (NPM) variable is not statistically significant suggests that financial flexibility is not solely determined by sales profitability. Instead, financial flexibility appears to be more closely related to balance sheet structure, financing policies, and liquidity management. Thus, financial flexibility can be considered not only a result of operational performance but also an outcome of strategic financial decisions. The negative relationship between firm size and financial flexibility may indicate that larger firms tend to maintain lower liquidity buffers because they have easier access to external financing sources. This finding is consistent with empirical studies suggesting that large firms, particularly in emerging markets, tend to rely more heavily on external borrowing. The strong and negative effect of the leverage ratio represents one of the most robust and economically meaningful findings of the study. The decline in financial flexibility as indebtedness increases clearly demonstrates the inverse relationship between financial fragility and financial flexibility. This finding is also consistent with the results of Arslan-Ayaydin et al. ( 2014 ), who show that firms with lower leverage levels tend to be more resilient during crisis periods. In emerging markets characterized by higher macroeconomic volatility, maintaining lower leverage levels may therefore represent a strategically important financial policy. Overall, the findings suggest that financial flexibility should not be interpreted merely as a passive outcome of performance indicators. Rather, it reflects a strategic financial policy shaped by balance sheet management, financing decisions, and capital structure preferences. These findings also highlight the importance of maintaining a balanced capital structure in emerging markets characterized by high macroeconomic volatility. 6. Conclusion and Policy Implications This study examines the determinants of financial flexibility using a long-term quarterly panel dataset of BIST30 firms. The findings indicate that financial flexibility is primarily explained by return on assets and leverage ratio. While improvements in operational profitability strengthen financial flexibility, increases in indebtedness significantly constrain firms’ financial maneuverability. In this respect, the study provides new empirical evidence regarding the determinants of financial flexibility in emerging markets. The results demonstrate that financial flexibility should not be considered solely as a liquidity management strategy, but rather as a strategic outcome of firms’ capital structure decisions. In particular, higher leverage levels significantly restrict financial flexibility, indicating the existence of a structural inverse relationship between financial fragility and financial flexibility. Overall, the findings highlight the strategic importance of financial flexibility for firms operating in volatile and uncertain economic environments. Contributions of the Study This study contributes to the literature in three main ways. First, financial flexibility is measured using a composite indicator that incorporates both liquidity and leverage components, rather than relying on single-dimensional measures such as cash holdings. Second, the study extends the empirical literature by employing long-term quarterly panel data from an emerging market context, namely Turkey. Third, the use of Driscoll–Kraay robust standard errors allows the estimation results to remain robust in the presence of cross-sectional dependence and serial correlation. Policy and Managerial Implications The findings of this study provide several important implications for financial managers. First, leverage decisions should not be evaluated solely from the perspective of capital cost , but also by considering their long-term effects on financial flexibility . While higher leverage may support growth and investment opportunities in the short run, it may significantly reduce financial maneuverability during periods of economic stress. In contrast, firms with stronger internal funding capacity are more likely to demonstrate greater financial resilience, particularly during periods of macroeconomic uncertainty. Limitations and Future Research This study measures firm performance using accounting-based indicators. Future research may extend the analysis by incorporating market-based performance measures, such as Tobin’s Q. In addition, examining crisis periods through subsample analyses may provide deeper insights into the cyclical dynamics of financial flexibility. Furthermore, employing dynamic panel data models, such as the System-GMM estimator, may help address potential endogeneity issues more comprehensively. Declarations Ethical Approval and Consent to Participate Not applicable. This study uses publicly available secondary financial data and does not involve human participants. Consent for Publication Not applicable. The manuscript does not contain individual person’s data or identifiable materials. Funding There was no funding for this research. Author Contribution AST conceived the study, designed the research framework, collected and organized the data, performed the empirical analysis, interpreted the results, and wrote the manuscript. The author reviewed and approved the final version of the manuscript. Acknowledgement Not applicable. Data Availability The data used in this study were obtained from the Public Disclosure Platform (KAP) and are publicly available at https://www.kap.org.tr. Processed data are available from the author upon reasonable request. References Akbar, S., Akbar, A., Tang, X., and Qureshi, M. A. (2021). Does financial flexibility help firms survive during financial crises? Journal of Corporate Finance, 68, 101937. https://doi.org/10.1016/j.jcorpfin.2021.101937 Akkaya, G.C. and Kantar, L., 2018. Financial flexibility and firm performance: Evidence from Borsa Istanbul. Borsa Istanbul Review, 18(3), pp. 1–10. American Institute of Certified Public Accountants (AICPA), 1993. Improving Business Reporting: A Customer Focus. New York: AICPA. Arslan, O. and Karan, M.B., 2009. Do debt maturity structures affect firm performance? Evidence from Turkey. Emerging Markets Finance and Trade, 45(4), pp. 36–48. https://doi.org/10.2753/REE1540-496X450403 Arslan-Ayaydin, O., Florackis, C. and Ozkan, A., 2014. Financial flexibility, corporate investment and performance: Evidence from financial crises. Review of Quantitative Finance and Accounting, 42(2), pp. 211–250. https://doi.org/10.1007/s11156-012-0340-x Baltagi, B.H., 2005. Econometric Analysis of Panel Data. 3rd ed. Chichester: John Wiley and Sons. Bonaimé, A., Hankins, K. and Jordan, B., 2020. Financial flexibility, risk management, and payout policy. Journal of Corporate Finance, 64, 101662. https://doi.org/10.1016/j.jcorpfin.2020.101662 Butt, A., Shahzad, A., Ghaffar, B. and Bilal, S., 2023. Financial flexibility and firm behavior: Evidence from emerging markets. Research Journal for Societal Issues, 5(1), pp. 382–394. https://doi.org/10.56976/rjsi.v5i1.128 Byoun, S., 2008. How and when do firms adjust their capital structures toward targets? Journal of Finance, 63(6), pp. 3069–3096. https://doi.org/10.1111/j.1540-6261.2008.01421.x Campello, M., Graham, J.R. and Harvey, C.R., 2010. The real effects of financial constraints: Evidence from a financial crisis. Journal of Financial Economics, 97(3), pp. 470–487. https://doi.org/10.1016/j.jfineco.2010.02.009 Dang, V.A., Kim, M. and Shin, Y., 2018. Asymmetric adjustment toward optimal capital structure: Evidence from a crisis. International Review of Financial Analysis, 56, pp. 226–242. https://doi.org/10.1016/j.irfa.2017.12.004 Denis, D.J. and McKeon, S.B., 2012. Debt financing and financial flexibility: Evidence from proactive leverage increases. Review of Financial Studies, 25(6), pp. 1897–1929. https://doi.org/10.1093/rfs/hhs005 Driscoll, J.C. and Kraay, A.C., 1998. Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), pp. 549–560. https://doi.org/10.1162/003465398557825 Financial Accounting Standards Board (FASB), 1984. Concepts Statement No. 5: Recognition and Measurement in Financial Statements of Business Enterprises. Norwalk, CT: FASB. Fitch Ratings, 2015. Corporate Rating Criteria. Available at: https://www.fitchratings.com Gao, H., Harford, J. and Li, K., 2020. Determinants of corporate cash policy: Insights from recent research. Journal of Financial Economics, 137(2), pp. 349–368. https://doi.org/10.1016/j.jfineco.2020.04.003 Gamba, A. and Triantis, A., 2008. The value of financial flexibility. Journal of Finance, 63(5), pp. 2263–2296. https://doi.org/10.1111/j.1540-6261.2008.01397.x Habib, A., Hasan, M.M. and Al-Hadi, A., 2021. Financial flexibility and corporate investment: Evidence from global firms. Journal of International Financial Markets, Institutions and Money, 72, 101337. https://doi.org/10.1016/j.intfin.2021.101337 Li, Z., Hyung, D.E. and Lee, D.Y., 2025. Financial flexibility and corporate financing efficiency. International Review of Financial Analysis, 98, 103892. https://doi.org/10.1016/j.irfa.2025.103892 Myers, S.C., 1984. The capital structure puzzle. Journal of Finance, 39(3), pp. 575–592. https://doi.org/10.1111/j.1540-6261.1984.tb03646.x Opler, T., Pinkowitz, L., Stulz, R. and Williamson, R., 1999. The determinants and implications of corporate cash holdings. Journal of Financial Economics, 52(1), pp. 3–46. https://doi.org/10.1016/S0304-405X(99)00003-3 Public Disclosure Platform (KAP), 2025. Available at: https://www.kap.org.tr Pesaran, M.H., 2007. A simple panel unit root test in the presence of cross-sectional dependence. Journal of Applied Econometrics, 22(2), pp. 265–312. https://doi.org/10.1002/jae.951 Pesaran, M.H., 2015. Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6–10), pp. 1089–1117. https://doi.org/10.1080/07474938.2014.956623 Shojaee, S. and Mirzaei, I., 2024. The impact of financial flexibility on the resilience of small and medium enterprises in the face of economic shocks. Business, Marketing, and Finance Open, 1(2), pp. 150–159. https://doi.org/10.61838/bmfopen.1.2.13 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9538172\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":637745047,\"identity\":\"1835113a-3dbe-452d-8158-67695af98390\",\"order_by\":0,\"name\":\"Ayse Soy Temur\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBADOYYDEAZjA7FajJG0MBOnJbGBaC38M9IfPvxRcTi970b6ww8fGGxkNxzgP/YBnxaJGznGxjxnDufOvJGQLDmDIc14wwFm5hl4rbmRwybN2HY4d8ONhGPMPAyHE0Fa8OqQv5H+/OfPtsPpBjcS25j/MPwnrMXgRoIZA2/b4QSDG8lsQKUHCGsxPPPGWJrnTLrhzDPPmCV7DJKNZx5mNsarRe54+sOPPyqs5fmAjA8/Kuxk+443PsarhUEgAUQ2w9wJxARjkv8AiKwjpGwUjIJRMApGMgAADDBPM/xh+bsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Duzce University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ayse\",\"middleName\":\"Soy\",\"lastName\":\"Temur\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-27 08:03:47\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9538172/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9538172/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109342621,\"identity\":\"dad582c3-0dfe-436f-9cc7-ce424a220683\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 19:26:33\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":394326,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9538172/v1/ff670b40-9047-4729-92e9-7fad94451fb9.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Financial Flexibility and Firm Performance: Evidence from an Emerging Market\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eFinancial flexibility refers to a firm's ability to preserve its financial structure in the face of unexpected shocks and to exploit emerging investment opportunities. Recent evidence suggests that financial flexibility strengthens firms\\u0026rsquo; financial resilience and helps sustain performance during periods of uncertainty (Campello et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Akbar et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Rising macroeconomic volatility and global crises have highlighted the importance of being strong not only in profitability terms but also in terms of financial adaptability. In this context, financial flexibility reflects firms\\u0026rsquo; capacity to respond through liquidity management, borrowing capacity, and financing strategies. Recent studies also show that financial flexibility improves financing efficiency and resource allocation decisions (Butt et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Li et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Xu et al., 2024).\\u003c/p\\u003e \\u003cp\\u003eFinancial flexibility has been defined from different perspectives in the literature. The Financial Accounting Standards Board (FASB, 1984) defines financial flexibility as the ability to change the timing and amount of cash flows in response to unexpected opportunities and economic shocks. Similarly, the American Institute of Certified Public Accountants (AICPA, 1993) describes financial flexibility as the ability to adjust expected cash inflows and cash outflows. Fitch Ratings (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), a credit rating agency, defines financial flexibility as a firm\\u0026rsquo;s capacity to meet its financial obligations while maintaining its credit quality and managing financial pressures.\\u003c/p\\u003e \\u003cp\\u003eIn the academic literature, financial flexibility is generally discussed within the framework of liquidity management and borrowing capacity. Myers (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e) defines financial flexibility as a firm's ability to utilize liquidity and borrowing capacity to exploit temporary investment opportunities or absorb financial shocks. Opler, Pinkowitz, Stulz, and Williamson (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e) emphasize the importance of maintaining sufficient cash reserves to meet unexpected financing needs and to take advantage of investment opportunities. Gamba and Triantis (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e) describe financial flexibility as the ability to obtain financing at low cost and to respond effectively to uncertainty, while Denis and McKeon (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) highlight that financial flexibility is shaped by both cash reserves and borrowing capacity. Arslan-Ayaydin et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) demonstrate that financially flexible firms are more successful in maintaining their operations and capturing strategic opportunities, particularly during crisis periods.\\u003c/p\\u003e \\u003cp\\u003eThere are also studies examining the relationship between financial flexibility and firm performance in the context of Turkey. Research conducted in Turkey indicates that financial flexibility has significant effects on firms\\u0026rsquo; investment behavior and performance, especially during financial crisis periods. For instance, Arslan-Ayaydin et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) show that financially flexible firms are able to maintain higher investment capacity during crisis periods. Other studies focusing on Turkey also indicate that financial structure decisions, particularly borrowing policies and liquidity management, play a decisive role in firm performance. These findings suggest that financial flexibility is an important factor that enhances firms\\u0026rsquo; financial resilience in emerging markets.\\u003c/p\\u003e \\u003cp\\u003eDespite the growing body of literature on financial flexibility, existing studies predominantly focus on developed markets or rely on limited time horizons and single-measure approaches. Moreover, the dynamic interaction between firm-specific financial characteristics and financial flexibility remains underexplored in emerging market contexts. In particular, there is a lack of comprehensive empirical evidence based on long-term quarterly data that captures structural changes across different economic periods. This gap is particularly relevant for emerging economies characterized by higher financial constraints and macroeconomic volatility.\\u003c/p\\u003e \\u003cp\\u003eTo address this gap, this study examines the relationship between financial flexibility and firm performance using quarterly panel data from an emerging market context. The empirical sample consists of 25 non-financial firms included in the BIST30 index over the period 2010Q1\\u0026ndash;2025Q2. Firm performance is represented by return on assets (ROA), return on equity (ROE), and net profit margin (NPM), while firm size and leverage are included as control variables. By employing a composite measure of financial flexibility that jointly captures liquidity strength and indebtedness, the study provides new evidence on firm-level financial resilience in emerging markets.\\u003c/p\\u003e \\u003cp\\u003eThis study contributes to the literature in several ways. First, it extends existing evidence on financial flexibility and firm performance in an emerging market context. Second, it employs a comprehensive panel data framework with robust estimation techniques that account for cross-sectional dependence and related econometric issues. Third, financial flexibility is measured using a composite indicator that jointly captures liquidity and borrowing capacity, offering a broader perspective than single-measure approaches.\\u003c/p\\u003e \\u003cp\\u003eThe remainder of the paper is organized as follows. The second section reviews the literature on financial flexibility and firm performance. The third section describes the data set, variables, and research methodology. The fourth section presents the empirical findings. The fifth section discusses the results, and the final section concludes the study and provides policy implications.\\u003c/p\\u003e\"},{\"header\":\"2. Conceptual Framework and Literature Review\",\"content\":\"\\u003cp\\u003eThe concept of financial flexibility has been examined from various perspectives in the literature and is generally associated with firms\\u0026rsquo; ability to adapt to unexpected economic shocks and to take advantage of emerging investment opportunities. For this reason, financial flexibility is considered an important financial characteristic that contributes to firms\\u0026rsquo; ability to maintain financial resilience during periods of economic uncertainty and to ensure long-term financial sustainability. In recent years, increasing global economic uncertainty, fluctuations in financial markets, and experiences from financial crises have highlighted the importance of firms being strong not only in terms of profitability but also in terms of financial resilience. This situation has increased the need to examine the role of financial flexibility in firm performance more comprehensively. Recent studies also show that financial flexibility represents an important strategic factor that enhances firms\\u0026rsquo; resilience against economic crises and financial constraints (Campello et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Akbar et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn the literature, financial flexibility is often associated with firms\\u0026rsquo; investment decisions. Financially flexible firms are considered to be better able to evaluate emerging investment opportunities and are less affected by financial constraints. Opler et al. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e) demonstrate that firms with high cash reserves are better able to take advantage of investment opportunities and mitigate the adverse effects of financial constraints. Similarly, Denis and McKeon (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) argue that financial flexibility arises not only from cash reserves but also from borrowing capacity, and that these two components together enhance firms\\u0026rsquo; ability to adapt to changing market conditions. More recent studies also highlight that financial flexibility is closely related to capital structure policies, cash management, and investment decisions (Dang et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Gao, Harford and Li, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAnother issue frequently examined in the literature is the impact of financial flexibility on firm performance. Firm performance is commonly measured using accounting-based profitability indicators. In this context, return on assets (ROA) reflects the ability of assets to generate income, return on equity (ROE) represents the return generated for shareholders, and net profit margin (NPM) indicates profitability relative to sales. While some studies find a positive relationship between financial flexibility and firm performance, others suggest that this relationship may vary depending on sectoral characteristics, financing policies, and macroeconomic conditions. Recent studies indicate that the effects of financial flexibility on investment behavior, risk management, and firm performance become more pronounced during periods of economic uncertainty (Bonaim\\u0026eacute;, Hankins and Jordan, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Habib, Hasan and Al-Hadi, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eEmpirical studies generally indicate a positive relationship between financial flexibility and firm performance. Financially flexible firms are better able to exploit investment opportunities, face fewer financing constraints, and are more resilient to economic shocks (Gamba and Triantis, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Denis and McKeon, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Akbar et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Using panel data analysis, Arslan-Ayaydin et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) show that financially flexible firms exhibit higher performance particularly during crisis periods. Similarly, Gamba and Triantis (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e) demonstrate that firms with strong liquidity positions and better access to low-cost financing tend to achieve superior financial performance. Myers (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e) provides a theoretical explanation of financial flexibility by defining it as firms\\u0026rsquo; ability to maintain financial stability while exploiting temporary investment opportunities.\\u003c/p\\u003e \\u003cp\\u003ePanel data analysis is widely used in studies examining this relationship. Panel data methods allow researchers to control for unobserved heterogeneity across firms and time effects. Empirical studies using panel data generally find that cash holdings have a positive effect on firm performance, whereas leverage tends to have a negative impact (Opler et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Arslan-Ayaydin et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). These findings suggest that financial flexibility may provide a strategic advantage for firms operating in emerging markets characterized by high macroeconomic volatility.\\u003c/p\\u003e \\u003cp\\u003eHowever, empirical evidence on the relationship between financial flexibility and firm performance in emerging economies remains relatively limited. This issue is particularly important in countries such as Turkey, where macroeconomic volatility is relatively high. In this context, the present study aims to contribute to the literature by examining the relationship between financial flexibility and firm performance for firms listed in the BIST30 index using panel data analysis. In addition, based on the pecking order theory and the financial flexibility literature, it is expected that profitability and financial structure indicators may play a determining role in financial flexibility. Based on this theoretical framework and the existing empirical findings, the following research hypotheses are developed.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eResearch Hypotheses\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH\\u003csub\\u003e1\\u003c/sub\\u003e\\u003c/strong\\u003e \\u003cp\\u003eReturn on assets (ROA) has a positive and significant effect on financial flexibility.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/strong\\u003e \\u003cp\\u003eReturn on equity (ROE) has a significant effect on financial flexibility.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/strong\\u003e \\u003cp\\u003eNet profit margin (NPM) has a positive and significant effect on financial flexibility.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/strong\\u003e \\u003cp\\u003eFirm size (SIZE) has a significant effect on financial flexibility.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/strong\\u003e \\u003cp\\u003eLeverage ratio (LEV) has a negative and significant effect on financial flexibility.\\u003c/p\\u003e \\u003c/p\\u003e\"},{\"header\":\"3. Data, Variables and Measurement\",\"content\":\"\\u003cp\\u003eThis section provides information on the data used in the study and the research methodology.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Data Set\\u003c/h2\\u003e \\u003cp\\u003eThis study employs a quarterly panel data set covering the period 2010:Q1\\u0026ndash;2025:Q2. The data set consists of publicly disclosed financial statements of non-financial firms listed on Borsa Istanbul (BIST) and included in the BIST30 index. The financial statement data used in the analysis were obtained from the Public Disclosure Platform (KAP), which is the official reporting system through which publicly traded companies in Turkey disclose their financial reports.\\u003c/p\\u003e \\u003cp\\u003eA balanced panel data structure was constructed within the scope of the study, and a total of 62 quarterly observations for 25 firms were analyzed.\\u003c/p\\u003e \\u003cp\\u003eFirms operating in the banking and financial sectors were excluded from the sample. The primary reason for this exclusion is that the balance sheet structures of these sectors differ structurally from other industries due to their high leverage levels and sector-specific regulatory frameworks. In financial institutions, borrowing levels are inherently high as part of their business model, and regulatory requirements play a decisive role in determining capital adequacy. These characteristics reduce the comparability of financial flexibility indicators across sectors. Therefore, in order to maintain sample homogeneity and prevent the analysis results from being influenced by structural differences across sectors, financial firms were excluded from the study.\\u003c/p\\u003e \\u003cp\\u003eThe use of quarterly data enables a more precise observation of the responses of financial flexibility to macroeconomic shocks and short-term financial fluctuations. Particularly in emerging markets where financial volatility tends to be high, annual data may fail to adequately capture changes in firms\\u0026rsquo; financial structures. In this context, the use of higher-frequency data allows for a more detailed analysis of the dynamics of financial flexibility.\\u003c/p\\u003e \\u003cp\\u003eAll analyses in this study were conducted using the R programming language. As the empirical methodology, a panel data analysis approach was adopted, which allows simultaneous examination of both the time-series and cross-sectional dimensions of the data. Within the panel data framework, Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models were estimated. To determine the most appropriate model specification, the F test, Breusch\\u0026ndash;Pagan Lagrange Multiplier test, and Hausman test were applied.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, in order to address potential autocorrelation, heteroskedasticity, and cross-sectional dependence problems detected in the error terms, Driscoll\\u0026ndash;Kraay (1998) robust standard errors were employed to obtain corrected coefficient estimates.\\u003c/p\\u003e \\u003cp\\u003eThe sample consists of 25 non-financial firms listed in the BIST 30 index (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFirms included in the sample\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCompany\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStock Code\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSektor\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnadolu Efes Biracılık ve Malt Sanayii A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAEFES\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eManufacturing, Food, Beverages and Tobacco\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAselsan Elektronik Sanayi ve Ticaret A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eASELS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTechnology, Defense\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBİM Birlesik Magazalar A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBIMAS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWholesale and Retail Trade\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026Ccedil;imsa \\u0026Ccedil;imento Sanayi ve Ticaret A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCIMSA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eStone and Soil Based Manufacturing\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEmlak Konut Gayrimenkul Yatırım Ortaklıgı A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEKGYO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eReal Estate Investment Trust\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEnka İnsaat ve Sanayi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eENKAI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eConstruction and Public Works\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEregli Demir ve \\u0026Ccedil;elik Fabrikaları T.A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEREGL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBasic Metal Industry\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFord Otomotiv Sanayi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFROTO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMetal Goods, Machinery, Electrical Devices and Transportation Vehicles\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGubre Fabrikaları T.A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGUBRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChemicals, Pharmaceuticals, Rubber and Plastic Products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHacı Omer Sabancı Holding A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSAHOL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHoldings and Investment Companies\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKardemir Karabuk Demir \\u0026Ccedil;elik Sanayi ve Ticaret A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKRDMD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBasic Metal Industry\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKo\\u0026ccedil; Holding A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKCHOL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHoldings and Investment Companies\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKoza Altın İsletmeleri A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKOZAL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMining and Quarrying\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMigros Ticaret A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMGROS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWholesale and Retail Trade\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePegasus Hava Tasimaciligi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePGSUS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTransportation and Storage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePetkim Petrokimya Holding A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePETKM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChemicals, Pharmaceuticals, Rubber and Plastic Products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSasa Polyester Sanayi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSASA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChemicals, Pharmaceuticals, Rubber and Plastic Products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTav Havalimanlari Holding A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTAVHL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHoldings and Investment Companies\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTofas Turk Otomobil Fabrikasi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTOASA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMetal Goods, Machinery, Electrical Devices and Transportation Vehicles\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTurkcell İletisim Hizmetleri A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTCELL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eInformation and Communication, Telecommunications\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTupras-Turkey Petrol Rafinerileri A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTUPRS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChemicals, Pharmaceuticals, Rubber and Plastic Products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTurk Hava Yollari A.O.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTHYAO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTransportation and Storage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTurkey Sise ve Cam Fabrikalari A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSISE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHoldings and Investment Companies\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTurk Telekomunikasyon A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTTKOM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eInformation and Communication, Telecommunications\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUlker Biskuvi Sanayi A.S.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eULKER\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFood, Beverage and Tobacco\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cb\\u003eNote\\u003c/b\\u003e: \\u003cem\\u003eBanks and financial institutions were excluded from the analysis due to the absence of certain key variables, such as inventories, in their financial statements. The table is constructed based on the firms included in the BIST30 index as of August 2025.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Variables and Measurement\\u003c/h2\\u003e \\u003cp\\u003eIn this study, firm performance is measured using accounting-based indicators, namely return on assets (ROA), return on equity (ROE), and net profit margin (NPM). ROA reflects the extent to which a firm efficiently utilizes its assets, ROE represents the return generated for shareholders, and NPM captures operating profitability and sales efficiency. When considered together, these three indicators allow for a multidimensional evaluation of a firm\\u0026rsquo;s operational efficiency and financial performance.\\u003c/p\\u003e \\u003cp\\u003eIn the literature, firm performance is often measured using market-based indicators such as Tobin\\u0026rsquo;s Q. However, in this study accounting-based performance indicators are preferred due to the long analysis period and the use of quarterly data, which may create limitations in terms of consistency and comparability of market-based data across periods. In particular, in emerging markets, market values tend to exhibit higher volatility and may not always be fully synchronized with financial statements. Therefore, the use of accounting-based performance measures is considered methodologically more appropriate. This approach is widely adopted in financial performance analyses conducted in emerging market contexts.\\u003c/p\\u003e \\u003cp\\u003eFinancial flexibility (FLEX) is included in the model as the dependent variable. Financial flexibility is measured using a composite indicator that simultaneously reflects the firm\\u0026rsquo;s liquidity capacity and borrowing level. This structure captures the multidimensional nature of the concept by considering both the firm\\u0026rsquo;s financial buffer and its financial obligations.\\u003c/p\\u003e \\u003cp\\u003eFirm size (SIZE) and financial leverage (LEV) are included in the model as control variables. Firm size is calculated as the natural logarithm of total assets. The logarithmic transformation reduces the effect of scale differences and helps normalize the distribution of the variable. The financial leverage ratio is calculated as total debt divided by total assets and represents the firm\\u0026rsquo;s financial risk level and capital structure. In the literature, firm size and leverage are widely recognized as important determinants of both financial flexibility and firm performance. Therefore, these variables are controlled in the model in order to analyze the main relationships more accurately.\\u003c/p\\u003e \\u003cp\\u003eThe variables used in the analysis include measures of financial flexibility, profitability, firm size, and leverage (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDefinitions and measurement of variables\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSymbol\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMeasurement\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eData Source\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFinancial Flexibility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFLEX\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(Cash and Cash Equivalents / Total Assets) \\u0026minus; (Total Debt / Total Assets)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eA composite indicator that reflects both the firm's liquidity capacity and borrowing level.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQuarterly financial statements of firms (KAP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReturn on Assets\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eROA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNet Income / Total Assets\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMeasures the firm's ability to generate profit from its assets.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIncome statement and balance sheet\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReturn on Equity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eROE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNet Income / Shareholders\\u0026rsquo; Equity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMeasures the profitability of shareholders\\u0026rsquo; investments.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIncome statement and balance sheet\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNet Profit Margin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNPM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNet Income / Net Sales\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIndicates operating efficiency and profitability of sales.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIncome statement\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eControl Variables\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFirm Size\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSIZE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eln(Total Assets)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRepresents the scale of the firm.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFinancial statements\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeverage Ratio\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLEV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTotal Debt / Total Assets\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRepresents the firm\\u0026rsquo;s capital structure and level of financial risk.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFinancial statements\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Measurement of Financial Flexibility\\u003c/h2\\u003e \\u003cp\\u003eThere are different approaches in the literature regarding the measurement of financial flexibility. Some researchers represent financial flexibility solely through the cash holding ratio (Opler et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Arslan-Ayaydin et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). This approach focuses on firms\\u0026rsquo; precautionary cash holding behavior in order to cope with financial shocks. Another approach defines financial flexibility in terms of borrowing capacity or low leverage levels, emphasizing the firm\\u0026rsquo;s ability to access external financing in the future (Myers, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e; Gamba and Triantis, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHowever, measuring financial flexibility using single-dimensional indicators may not fully capture the multidimensional nature of the concept. Recent studies suggest that financial flexibility can be better represented through composite indicators that simultaneously consider liquidity reserves and borrowing capacity (Byoun, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Denis and McKeon, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Akbar et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Shojaee and Mirzaei, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This perspective assumes that financial flexibility depends not only on the current level of cash holdings but also on the firm's potential borrowing capacity in the future.\\u003c/p\\u003e \\u003cp\\u003eAccordingly, financial flexibility in this study is measured using the following composite indicator:\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e \\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\text{FLEX}}_{it}=\\\\frac{{\\\\text{Cash and Cash Equivalents}}_{it}}{{\\\\text{Total Assets}}_{it}}-\\\\frac{{\\\\text{Total Debt}}_{it}}{{\\\\text{Total Assets}}_{it}}\\\\)\\u003c/span\\u003e \\u003c/span\\u003eThis formulation simultaneously considers the firm's liquidity capacity and debt level. The first component represents the firm's financial buffer capacity, while the second component reflects its financial obligations. Thus, financial flexibility increases with higher cash reserves and lower leverage, and decreases when firms exhibit lower liquidity and higher debt levels.\\u003c/p\\u003e \\u003cp\\u003eThe main reason for adopting this measurement approach is that financial flexibility should not be considered merely as a static liquidity indicator but rather as a strategic aspect of capital structure decisions. The use of a composite indicator allows financial flexibility to be represented in a more balanced and comprehensive manner, enabling a more accurate analysis of its relationship with firm performance. Furthermore, measuring financial flexibility using a composite indicator helps reduce potential measurement errors compared to single-variable proxies and better reflects the dynamic nature of the concept. This approach is particularly appropriate for emerging markets, where differences in financial structures across firms tend to be more pronounced.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. Model Specification and Estimation Method\\u003c/h2\\u003e \\u003cp\\u003eThis study examines the relationship between financial flexibility and firm performance for non-financial firms included in the BIST30 index using a panel data analysis approach. The panel data framework allows the simultaneous consideration of both the time-series and cross-sectional dimensions of the data and enables the control of unobserved firm-specific effects. Consequently, panel data models provide more consistent and efficient estimates compared to analyses based solely on time-series or cross-sectional data (Baltagi, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWithin the research framework, financial flexibility (FLEX) is treated as the dependent variable, while variables representing firm performance and financial structure are included as explanatory variables. Accordingly, the baseline panel data model estimated in this study is specified as follows:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{FLEX}_{\\\\_it}={\\\\beta\\\\:}_{0}+{\\\\beta\\\\:}_{1}{ROA}_{\\\\_it}+{\\\\beta\\\\:}_{2}{ROE}_{\\\\_it}+{\\\\beta\\\\:}_{3}{NPM}_{\\\\_it}+{\\\\beta\\\\:}_{4}{SIZE}_{\\\\_it}+{\\\\beta\\\\:}_{5}{LEV}_{\\\\_it}+{u}_{\\\\_it}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eWhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:i\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes firms and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:t\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003edenotes time. ROA, ROE, and NPM represent profitability indicators, SIZE represents firm size, and LEV denotes the financial leverage ratio. The coefficients \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\beta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e capture the marginal effects of the explanatory variables on financial flexibility, while \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{u}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the error term. The signs of the estimated coefficients are interpreted in line with theoretical expectations suggested in the financial flexibility literature.\\u003c/p\\u003e \\u003cp\\u003eEmpirical analyses were conducted following a systematic econometric procedure. First, descriptive statistics were calculated in order to evaluate the basic characteristics of the dataset. Considering the potential interdependence among cross-sectional units in panel data, cross-sectional dependence was tested using the Pesaran (2004; \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) CD test. Since cross-sectional dependence was detected, the stationarity properties of the variables were examined using the Pesaran (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) CIPS test, which is a second-generation panel unit root test that accounts for cross-sectional dependence.\\u003c/p\\u003e \\u003cp\\u003eIn the model estimation stage, Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models were estimated separately. To determine the most appropriate model specification, the F test, Breusch\\u0026ndash;Pagan Lagrange Multiplier test, and Hausman test were employed. In addition, the classical assumptions regarding the error terms were tested for the selected model, and the presence of autocorrelation, heteroskedasticity, and cross-sectional dependence was identified.\\u003c/p\\u003e \\u003cp\\u003eTherefore, coefficient estimates were recalculated using Driscoll\\u0026ndash;Kraay (1998) robust standard errors, which are robust to heteroskedasticity, serial correlation, and cross-sectional dependence.\\u003c/p\\u003e \\u003cp\\u003ePotential endogeneity issues are widely discussed in panel data analyses in the literature. Although potential endogeneity issues are acknowledged in panel data analysis, the model specification and variable structure employed in this study help mitigate such concerns.\\u003c/p\\u003e \\u003cp\\u003eThe empirical findings obtained from these analyses are presented in the following section.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Empirical Findings\",\"content\":\"\\u003cp\\u003eThis section first presents the descriptive statistics of the variables used in the study. Subsequently, the results of the cross-sectional dependence test and panel unit root tests are reported. Finally, the findings obtained from the panel data estimations are presented.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Descriptive Statistics\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents the descriptive statistics of the variables used in the empirical analysis. The results indicate considerable variation across firms and over time. The dataset consists of a balanced panel of 25 firms over the period 2010Q1\\u0026ndash;2025Q2, resulting in a total of 1,550 observations. These statistics provide preliminary insights into the distribution and variability of the variables used in the study.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDescriptive statistics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMin\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMax\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eStd. Dev.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003en\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFLEX\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.4383\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.4636\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.8508\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.6252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2478\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eROA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0208\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0175\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.1780\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.2908\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0273\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eROE\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0565\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0434\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.6251\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.1505\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2348\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNPM\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1496\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0888\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-6.1275\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.091\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.7179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIZE\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.6742\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.9459\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.7862\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.5805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.0033\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLEV\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.5835\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.6095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0747\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9990\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1923\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1550\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe descriptive statistics indicate that the average value of the financial flexibility variable (FLEX) is negative (\\u0026ndash;0.4383). This finding suggests that, on average, the firms in the sample exhibit higher leverage levels relative to their liquidity buffers over the sample period. The wide range between the minimum and maximum values also indicates substantial variation in financial flexibility across firms.\\u003c/p\\u003e \\u003cp\\u003eWhen the profitability indicators are examined, both ROA and ROE exhibit positive average values. However, the wide ranges observed in ROE and NPM suggest the presence of considerable fluctuations over time and the existence of unusually high or low performance levels for some firms. In particular, the relatively high maximum value of the NPM variable indicates the presence of outliers during certain periods.\\u003c/p\\u003e \\u003cp\\u003eThe average value of the firm size variable (SIZE) confirms that the sample consists primarily of large-scale firms. Meanwhile, the average leverage ratio (LEV) indicates that approximately 58% of firms\\u0026rsquo; assets are financed through debt.\\u003c/p\\u003e \\u003cp\\u003eOverall, the standard deviation values of the variables suggest substantial variation both across firms and over time. This finding provides methodological support for the use of panel data analysis, which simultaneously accounts for both cross-sectional and temporal variation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Cross-Sectional Dependence and Unit Root Tests\\u003c/h2\\u003e \\u003cp\\u003eIn panel data analysis, it is essential to determine whether cross-sectional dependence exists among the units in the panel in order to obtain reliable estimates. If cross-sectional dependence is present, first-generation panel unit root tests may produce inconsistent results. Therefore, second-generation panel unit root tests are recommended in such cases.\\u003c/p\\u003e \\u003cp\\u003eAccordingly, the presence of cross-sectional dependence in the panel dataset was examined using the Pesaran (2004; \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) Cross-Sectional Dependence (CD) test.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH₀\\u003c/strong\\u003e \\u003cp\\u003eThere is no cross-sectional dependence among the panel units.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH₁\\u003c/strong\\u003e \\u003cp\\u003eThere is cross-sectional dependence among the panel units.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCross-sectional dependence test (Pesaran CD)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTest\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eStatistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eResult\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePesaran CD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eZ\\u0026thinsp;=\\u0026thinsp;6.221\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCross-sectional dependence exists\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e reports the results of the Pesaran CD test for cross-sectional dependence. The test statistic is z\\u0026thinsp;=\\u0026thinsp;6.221 with a probability value of p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. Therefore, the null hypothesis is rejected, indicating the presence of cross-sectional dependence among the firms in the panel. In other words, firms in the sample appear to be simultaneously affected by common macroeconomic conditions, financial market developments, and sectoral shocks.\\u003c/p\\u003e \\u003cp\\u003eThe existence of cross-sectional dependence implies that second-generation panel unit root tests that account for this dependence should be employed when examining the stationarity properties of the variables. Therefore, the stationarity of the variables was tested using the Pesaran (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) CIPS (Cross-sectionally Augmented IPS) panel unit root test.\\u003c/p\\u003e \\u003cp\\u003eThe hypotheses of the CIPS test are defined as follows:\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH₀\\u003c/strong\\u003e \\u003cp\\u003eThe series contain a unit root (non-stationary).\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eH₁\\u003c/strong\\u003e \\u003cp\\u003eThe series are stationary.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePanel unit root test results (Pesaran CIPS)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCIPS Statistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLag\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eStationarity\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFLEX\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-2.1150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-3.9721\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-3.9549\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNPM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-3.9266\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIZE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.9796\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLEV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-2.5648\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eI(0)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003cb\\u003eNote\\u003c/b\\u003e: Tests are conducted under a model including both constant and trend.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e*, **, *** denote significance levels of \\u003cb\\u003e10%, 5%, and 1%\\u003c/b\\u003e, respectively.\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e presents the results of the Pesaran CIPS panel unit root test. The findings indicate that ROA, ROE, and NPM are stationary at the 1% significance level, while FLEX, SIZE, and LEV are stationary at the 10% significance level. These results suggest that all variables used in the study are stationary at their level values, implying that the series do not contain unit roots.\\u003c/p\\u003e \\u003cp\\u003eAfter confirming the stationarity of the variables at their levels, a correlation analysis was conducted to examine the pairwise relationships among the variables before proceeding with the panel regression analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. Correlation Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe correlation matrix indicates a strong negative relationship between financial flexibility and leverage, while the relationship between financial flexibility and profitability remains positive but moderate. The detailed coefficients are reported in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelation matrix\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eFLEX\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFLEX\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eROA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eROE\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNPM\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSIZE\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLEV\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.377\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.327\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNPM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.265\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIZE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.084\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.073\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.035\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLEV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.895\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.085\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe correlation results indicate that financial flexibility (FLEX) is positively related to return on assets (ROA) and strongly negatively related to leverage (LEV). This suggests that higher levels of debt may reduce financial flexibility. The relationships among the other variables are generally weak to moderate.\\u003c/p\\u003e \\u003cp\\u003eSince the absolute values of the correlation coefficients are generally below 0.80, the results suggest that there is no serious multicollinearity problem in the model.\\u003c/p\\u003e \\u003cp\\u003eTo further examine the potential presence of multicollinearity, Variance Inflation Factor (VIF) values were calculated for all variables. The results show that all VIF values are below the commonly accepted threshold of 5, confirming that multicollinearity is not a significant concern and supporting the reliability of the estimated results.\\u003c/p\\u003e \\u003cp\\u003eBased on these findings, panel data analysis was employed to analyze the relationships among the variables by considering both the time and cross-sectional dimensions simultaneously. The most commonly used panel data models in this context are Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Model Selection and Diagnostic Tests\\u003c/h2\\u003e \\u003cp\\u003eIn order to determine the appropriate panel data model, a series of model selection tests were conducted. The most commonly used panel data models in the literature include Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects, and Random Effects models (Baltagi, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). To determine the most appropriate model among these alternatives, the F test, Breusch\\u0026ndash;Pagan Lagrange Multiplier (LM) test, and Hausman test were applied sequentially. In addition, the assumptions related to the error terms that may affect the validity of the selected model were examined using the Wooldridge test for autocorrelation and the Modified Wald test for heteroskedasticity.\\u003c/p\\u003e \\u003cp\\u003eAs a starting point for the panel data analysis, the pooled OLS model was estimated. The results obtained from the balanced panel structure (n\\u0026thinsp;=\\u0026thinsp;25, T\\u0026thinsp;=\\u0026thinsp;62, N\\u0026thinsp;=\\u0026thinsp;1,550) indicate that the model is statistically significant overall (F\\u0026thinsp;=\\u0026thinsp;1436.79; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). However, since this model does not account for unobserved firm-specific heterogeneity, it is considered only as a baseline reference model. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e reports the results of the model selection and diagnostic tests.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eModel selection and diagnostic tests\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTest\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNull Hypothesis (H₀)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStatistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDecision\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eF Test (Individual Effects)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo individual effects (Pooled OLS is appropriate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eF\\u0026thinsp;=\\u0026thinsp;65.805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eH₀ rejected \\u0026rarr; FE/RE\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBreusch\\u0026ndash;Pagan LM Test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo random effects (Pooled OLS is appropriate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; = 10485\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eH₀ rejected \\u0026rarr; RE\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHausman Test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRandom effects estimator is consistent and efficient\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; = 4.804\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.440\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eH₀ not rejected \\u0026rarr; RE\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWooldridge Test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo autocorrelation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eF\\u0026thinsp;=\\u0026thinsp;227.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eH₀ rejected\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe diagnostic test results indicate that estimating the panel data structure using the pooled regression model is not appropriate. The results of both the F test and the Breusch\\u0026ndash;Pagan LM test reveal that individual effects are statistically significant, suggesting that either the fixed effects or the random effects model should be preferred.\\u003c/p\\u003e \\u003cp\\u003eThe choice between the fixed effects and random effects models was evaluated using the Hausman test, and the results indicate that the random effects model is consistent and efficient. However, the Wooldridge test reveals the presence of autocorrelation in the error terms.\\u003c/p\\u003e \\u003cp\\u003eTherefore, in order to address potential econometric issues such as autocorrelation, heteroskedasticity, and possible cross-sectional dependence, the model coefficients were estimated using robust standard errors that are resistant to these problems.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5. Panel Regression Results\\u003c/h2\\u003e \\u003cp\\u003eAccordingly, the model coefficients were re-estimated using the Driscoll\\u0026ndash;Kraay robust standard error estimator. The Driscoll\\u0026ndash;Kraay method provides consistent estimates that are robust to heteroskedasticity, serial correlation, and cross-sectional dependence. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e reports the panel regression results based on the Driscoll\\u0026ndash;Kraay estimator.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePanel regression results (Driscoll\\u0026ndash;Kraay robust standard errors)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCoefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStd. Error\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003et-Statistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.7046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.1615\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.3628\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0000***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eROE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.0133\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.3498\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0189**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNPM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.5951\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.1109\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIZE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.0047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0011\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-4.3995\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0000***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLEV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.9606\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0254\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-37.8378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0000***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eNotes\\u003c/span\\u003e: Robust standard errors based on the Driscoll\\u0026ndash;Kraay estimator.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e*** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, ** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, * p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.10\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe estimation results reported in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e indicate that return on assets (ROA) has a positive and statistically significant effect on financial flexibility at the 1% level. This finding suggests that higher profitability improves firms\\u0026rsquo; financial flexibility.\\u003c/p\\u003e \\u003cp\\u003eThe return on equity (ROE) variable is found to be negative and statistically significant at the 5% level. This result indicates that increases in equity profitability have a limited but negative effect on financial flexibility.\\u003c/p\\u003e \\u003cp\\u003eThe coefficient of the net profit margin (NPM) variable is not statistically significant. This finding suggests that net profit margin does not have a meaningful impact on financial flexibility.\\u003c/p\\u003e \\u003cp\\u003eThe firm size (SIZE) variable is negative and significant at the 1% level. This result implies that financial flexibility tends to decrease as firm size increases.\\u003c/p\\u003e \\u003cp\\u003eFinally, the leverage ratio (LEV) variable is found to be negative and highly significant. This result indicates that an increase in the level of indebtedness significantly reduces financial flexibility. These results provide robust empirical evidence on the determinants of financial flexibility for firms operating in the BIST30 index.\\u003c/p\\u003e \\u003cp\\u003eOverall, the results obtained using Driscoll\\u0026ndash;Kraay robust standard errors appear to be robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This strengthens the reliability of the empirical findings reported in this study.\\u003c/p\\u003e \\u003cp\\u003eTo further evaluate the robustness of the findings, the model was also examined under alternative specifications. The results indicate that the signs and statistical significance levels of the coefficients remain largely consistent, suggesting that the findings are not sensitive to model assumptions and are empirically robust.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Discussion of Findings\",\"content\":\"\\u003cp\\u003eThe empirical findings obtained using Driscoll\\u0026ndash;Kraay robust standard errors indicate that financial flexibility in BIST30 firms is primarily determined by profitability capacity and capital structure. In particular, the positive and highly significant coefficient of the return on assets (ROA) variable suggests that as firms\\u0026rsquo; operational efficiency increases, their financial maneuverability also expands. This result is consistent with the pecking order theory proposed by Myers (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e), which argues that internal funding capacity is one of the main determinants of financial flexibility. Moreover, it supports the findings of Opler et al. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e) and Denis and McKeon (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) regarding the positive relationship between cash reserves and investment capacity.\\u003c/p\\u003e \\u003cp\\u003eThe negative and significant coefficient of the return on equity (ROE) variable represents a finding that has been reported more rarely in the literature. This result may indicate that firms with higher equity returns tend to adopt more aggressive dividend policies or pursue financing strategies involving higher levels of risk. Therefore, higher ROE does not necessarily imply greater financial flexibility. In this context, the result may reflect a trade-off between performance maximization and financial resilience.\\u003c/p\\u003e \\u003cp\\u003eThe fact that the net profit margin (NPM) variable is not statistically significant suggests that financial flexibility is not solely determined by sales profitability. Instead, financial flexibility appears to be more closely related to balance sheet structure, financing policies, and liquidity management. Thus, financial flexibility can be considered not only a result of operational performance but also an outcome of strategic financial decisions.\\u003c/p\\u003e \\u003cp\\u003eThe negative relationship between firm size and financial flexibility may indicate that larger firms tend to maintain lower liquidity buffers because they have easier access to external financing sources. This finding is consistent with empirical studies suggesting that large firms, particularly in emerging markets, tend to rely more heavily on external borrowing.\\u003c/p\\u003e \\u003cp\\u003eThe strong and negative effect of the leverage ratio represents one of the most robust and economically meaningful findings of the study. The decline in financial flexibility as indebtedness increases clearly demonstrates the inverse relationship between financial fragility and financial flexibility. This finding is also consistent with the results of Arslan-Ayaydin et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), who show that firms with lower leverage levels tend to be more resilient during crisis periods. In emerging markets characterized by higher macroeconomic volatility, maintaining lower leverage levels may therefore represent a strategically important financial policy.\\u003c/p\\u003e \\u003cp\\u003eOverall, the findings suggest that financial flexibility should not be interpreted merely as a passive outcome of performance indicators. Rather, it reflects a strategic financial policy shaped by balance sheet management, financing decisions, and capital structure preferences. These findings also highlight the importance of maintaining a balanced capital structure in emerging markets characterized by high macroeconomic volatility.\\u003c/p\\u003e\"},{\"header\":\"6. Conclusion and Policy Implications\",\"content\":\"\\u003cp\\u003eThis study examines the determinants of financial flexibility using a long-term quarterly panel dataset of BIST30 firms. The findings indicate that financial flexibility is primarily explained by return on assets and leverage ratio. While improvements in operational profitability strengthen financial flexibility, increases in indebtedness significantly constrain firms\\u0026rsquo; financial maneuverability. In this respect, the study provides new empirical evidence regarding the determinants of financial flexibility in emerging markets.\\u003c/p\\u003e \\u003cp\\u003eThe results demonstrate that financial flexibility should not be considered solely as a liquidity management strategy, but rather as a strategic outcome of firms\\u0026rsquo; capital structure decisions. In particular, higher leverage levels significantly restrict financial flexibility, indicating the existence of a structural inverse relationship between financial fragility and financial flexibility. Overall, the findings highlight the strategic importance of financial flexibility for firms operating in volatile and uncertain economic environments.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eContributions of the Study\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis study contributes to the literature in three main ways.\\u003c/p\\u003e \\u003cp\\u003eFirst, financial flexibility is measured using a composite indicator that incorporates both liquidity and leverage components, rather than relying on single-dimensional measures such as cash holdings.\\u003c/p\\u003e \\u003cp\\u003eSecond, the study extends the empirical literature by employing long-term quarterly panel data from an emerging market context, namely Turkey.\\u003c/p\\u003e \\u003cp\\u003eThird, the use of Driscoll\\u0026ndash;Kraay robust standard errors allows the estimation results to remain robust in the presence of cross-sectional dependence and serial correlation.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePolicy and Managerial Implications\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe findings of this study provide several important implications for financial managers. First, leverage decisions should not be evaluated solely from the perspective of \\u003cb\\u003ecapital cost\\u003c/b\\u003e, but also by considering their long-term effects on \\u003cb\\u003efinancial flexibility\\u003c/b\\u003e. While higher leverage may support growth and investment opportunities in the short run, it may significantly reduce financial maneuverability during periods of economic stress. In contrast, firms with stronger internal funding capacity are more likely to demonstrate greater financial resilience, particularly during periods of macroeconomic uncertainty.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eLimitations and Future Research\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis study measures firm performance using accounting-based indicators. Future research may extend the analysis by incorporating market-based performance measures, such as Tobin\\u0026rsquo;s Q. In addition, examining crisis periods through subsample analyses may provide deeper insights into the cyclical dynamics of financial flexibility. Furthermore, employing dynamic panel data models, such as the System-GMM estimator, may help address potential endogeneity issues more comprehensively.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\" \\u003cp\\u003e \\u003cstrong\\u003eEthical Approval and Consent to Participate\\u003c/strong\\u003e \\u003cp\\u003eNot applicable. This study uses publicly available secondary financial data and does not involve human participants.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e \\u003cp\\u003eNot applicable. The manuscript does not contain individual person\\u0026rsquo;s data or identifiable materials.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThere was no funding for this research.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAST conceived the study, designed the research framework, collected and organized the data, performed the empirical analysis, interpreted the results, and wrote the manuscript. The author reviewed and approved the final version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe data used in this study were obtained from the Public Disclosure Platform (KAP) and are publicly available at https://www.kap.org.tr. Processed data are available from the author upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAkbar, S., Akbar, A., Tang, X., and Qureshi, M. A. (2021). Does financial flexibility help firms survive during financial crises? Journal of Corporate Finance, 68, 101937. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jcorpfin.2021.101937\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jcorpfin.2021.101937\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAkkaya, G.C. and Kantar, L., 2018. Financial flexibility and firm performance: Evidence from Borsa Istanbul. 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The impact of financial flexibility on the resilience of small and medium enterprises in the face of economic shocks. Business, Marketing, and Finance Open, 1(2), pp. 150\\u0026ndash;159. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.61838/bmfopen.1.2.13\\u003c/span\\u003e\\u003cspan address=\\\"10.61838/bmfopen.1.2.13\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Financial flexibility, Firm performance, Panel data, Capital structure, Emerging markets\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9538172/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9538172/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFinancial flexibility is a critical corporate capability that enables firms to withstand financial shocks and exploit investment opportunities. This study examines the determinants of financial flexibility and its relationship with firm performance using quarterly panel data from an emerging market context. The empirical sample consists of 25 non-financial firms included in the BIST30 index over the period 2010Q1–2025Q2. The analysis employs cross-sectional dependence tests, second-generation unit root procedures, panel model selection tests, and Driscoll–Kraay robust estimation. The findings indicate that return on assets positively affects financial flexibility, whereas leverage has a strong negative effect. Firm size is also negatively associated with flexibility, while net profit margin is statistically insignificant. The results suggest that profitability enhances firms’ financial resilience, whereas excessive indebtedness constrains financial maneuverability. The study contributes to the literature by providing long-term panel evidence from an emerging market using a composite measure of financial flexibility that jointly captures liquidity strength and indebtedness.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePurpose\\u003c/strong\\u003e This study aims to examine how profitability and capital structure decisions influence financial flexibility in an emerging market context. It also seeks to provide new firm-level evidence from Turkey using listed companies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDesign/methodology\\u003c/strong\\u003e The study uses quarterly panel data for 25 non-financial BIST30 firms covering the period 2010Q1–2025Q2. A comprehensive panel econometric framework is employed, including model selection procedures and Driscoll–Kraay robust standard errors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFindings\\u003c/strong\\u003e Return on assets has a positive and statistically significant effect on financial flexibility, while leverage has a strong negative effect. Firm size is negatively associated with flexibility, whereas net profit margin is not statistically significant.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePractical implications\\u003c/strong\\u003e Managers should consider leverage decisions not only in terms of financing costs but also in relation to preserving long-term financial flexibility. Strong internal profitability may improve firms’ resilience during uncertain economic conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOriginality/value\\u003c/strong\\u003e This study contributes to the literature by using a long-term quarterly panel dataset from an emerging market. It also measures financial flexibility through a composite indicator that jointly reflects liquidity strength and indebtedness. The evidence provides new insights into firm-level financial resilience in volatile markets.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eJEL Classification: \\u003c/strong\\u003eC33, G30, G31, G32\\u003c/p\\u003e\",\"manuscriptTitle\":\"Financial Flexibility and Firm Performance: Evidence from an Emerging Market\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-15 19:26:23\",\"doi\":\"10.21203/rs.3.rs-9538172/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"bddca1f8-8ea6-427a-bf87-3109c7d358ff\",\"owner\":[],\"postedDate\":\"May 15th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"reviewerAgreed\",\"content\":\"232435306065088839759667878517411148508\",\"date\":\"2026-05-18T20:11:34+00:00\",\"index\":22,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"10\",\"date\":\"2026-05-06T13:16:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-30T13:33:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-30T13:33:01+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-15T19:26:23+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-15 19:26:23\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9538172\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9538172\",\"identity\":\"rs-9538172\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}