44 © 2026 Conscientia Beam. All Rights Reserved. Does liquidity affect the financial health of Indian banks? Sanchal Tarode1 Jagjeevan Kanoujiya2+ Shailesh Rastogi3 Asmita Dani4 Neha Parashar5 1,2,3Symbiosis Institute of Business Management, Nagpur, Symbiosis International (Deemed) University, Pune, India. 1Email: sanchal@priyadarshinimba.com 2Email: jagjeevan24288@yahoo.co.in 3Email: krishnasgdas@gmail.com 4Symbiosis International (Deemed) University, Pune, India. 4Email: director_academics@siu.edu.in 5Symbiosis School of Banking and Finance, Pune, Symbiosis International (Deemed) University, Pune, India. 5Email: nehaparashar10@gmail.com (+ Corresponding author) ABSTRACT Article History Received: 7 August 2024 Revised: 26 September 2025 Accepted: 31 October 2025 Published: 26 November 2025 Keywords Altman Z-score Indian banks Liquidity coverage ratio Net stable funding ratio. The dynamic and volatile economic environment impacts the bank’s performance and raises the risk of bankruptcy. This study deepens the understanding of financial distress in the Indian banking sector through liquidity. Furthermore, the study aims to analyse and understand the relationship between liquidity, liquidity measures and financial distress. The study evaluated the short- and long-term liquidity ratios from 2012 to 2022. Quantile panel discussion analysis (QPDA) was incorporated into this study. Linear and non-linear relationships measure the impact of liquidity on financial distress. The study comprises data from 23 Indian banks and represents the Indian banking landscape. The study investigates the effect of Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) on bank stability. The findings highlight that the LCR improves short- term liquidity but excessive levels can increase bank distress. On the other hand, NSFR raises stress initially but improves stability and reduces distress beyond a certain limit. The results show the need to balance short-term liquidity with long-term funding stability in regulatory policies. The implications of this study contribute to risk management strategies and better decision-making within the baking sector. This study proposes significant insights to the banks, policymakers, regulators and various banking institutions. Contribution/Originality: The study excludes the qualitative factors that can impact liquidity and financial distress. The meticulous analysis of how the impacts of LCR and NSFR on bank stability, understanding of regulatory measures' long-term effects on financial distress, and enhancing the understanding to strengthen the Indian banking sector contribute to the originality of this study. 1. INTRODUCTION The Indian banking industry is critical to the country's economy, providing a foundation for financial stability and prosperity. Bank failures, often more costly to resolve than non-bank failures can substantially impact important stakeholders, including taxpayers (El Diri, King, Spokeviciute, & Williams, 2021). The risk of bank failure poses significant economic costs and societal burdens globally impacting the economic performance of nations and potentially jeopardizing domestic and global economies (Sharma, 2013). The banking sector in India faces bankruptcy risk due to the unique characteristics of the sector including regulatory frameworks, market structures, and economic conditions (Das et al., 2020). The Indian banking scenario has faced intense transformations due to global and Humanities and Social Sciences Letters 2026 Vol. 14, No. 1, pp. 44-58 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v14i1.4548 © 2026 Conscientia Beam. All Rights Reserved. mailto:sanchal@priyadarshinimba.com mailto:jagjeevan24288@yahoo.co.in mailto:krishnasgdas@gmail.com mailto:director_academics@siu.edu.in mailto:nehaparashar10@gmail.com https://orcid.org/0000-0002-7342-7465 https://orcid.org/0000-0002-1696-650X https://orcid.org/0000-0001-6766-8708 https://orcid.org/0000-0003-2308-5951 https://www.doi.org/10.18488/73.v14i1.4548 Humanities and Social Sciences Letters, 2025, 14(1): 44-58 45 © 2026 Conscientia Beam. All Rights Reserved. domestic factors. Banking operations are impacted by economic uncertainty with technological advancements, regulatory norms and continuously changing customer preferences. It is a great challenge to monitor the financial health of a financial institution. Bank regulators must forecast financial distress accurately to mitigate the risk, reduce financial loss, and take the necessary actions. This also helps improve the resource allocation parameter, manage resources effectively and focus on instant bank evaluation (Flannery, 1998). After the worldwide financial crisis in 2008, the curiosity to understand the reasons and aspects that led to financial distress in banks has risen. Advanced warning technologies have been developed to detect financial distress, raise the alarm to the concerned authorities, and avoid any financial crisis-like situations (Soenen & Vander Vennet, 2022). Traditional forecasting models use accounting data from past information which is not relevant in the current times as they may not forecast the future trend (Agarwal & Taffler, 2008). The financial health of banks and forecasting accuracy can be developed by considering the microeconomic indicators, market information, and non- financial factors (Flannery & Bliss, 2019). A study noted the macroeconomic factors and market details (Männasoo & Mayes, 2009) reflecting less information on how non-financial factors impact the bank default risk (Chiaramonte & Casu, 2017). Hence, this study examines how liquidity can predict financial distress in banks. The Basel Committee on Banking Supervision (BCBS) was set up in 1974. It aims to maintain the financial stability of banks worldwide for over fifty years. BCBS has formulated policies and regulations that improve global banking systems. The financial crisis of 2007-09 was caused by excessive borrowings, poor governance, lack of risk management strategies, and inadequate liquidity, after which the Basel III framework was introduced. Therefore, the Basel III norms focus on Liquidity Risk (LR) management to support the banking system. These norms have introduced the key measures which are the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR), which evaluate the liquidity and enhance the LR management in the banks (Pinto, Rastogi, & Agarwal, 2024). Financial distress is a complex issue (Kebede, Tesfaye, & Erana, 2024). Financial distress is the bank’s failure to fulfil its financial responsibilities (Maulida, Moehaditoyo, & Nugroho, 2018) which causes a financial crisis. A lack of cash flow, low profits, and insufficient liquidity lead to financial distress. In banking, insufficient liquidity can lead to a huge crisis (Isayas, 2021). Financial distress is a major challenge and is depicted by a lack of liquidity, profits, operational efficiency and solvency (Jessie & Tannia, 2024). These are the potential warning signs before leading to bankruptcy (Jessie & Tannia, 2024; Kiros, 2020; Kisman & Krisandi, 2019). Banks, regulators, policymakers, investors, and other concerned stakeholders have their prime focus on lowering the financial distress risk by maintaining the required liquidity levels. Hence, it is essential to understand the relationship between liquidity and financial distress to keep banks stable and robust (Gupta & Kashiramka, 2020). Effective liquidity management of the banks is essential as it helps to meet the short-term financial needs and absorb any financial shocks (Adalsteinsson, 2014). Financial distress implicates the declining financial health of the banks which can lead to insolvency or bankruptcy. Thus, it is important to understand the relationship between liquidity and financial distress. Banks must create effective risk management strategies that guarantee financial stability and protect the depositor’s interest. LCR and NSFR are two important regulatory measures to improve the resilience of banks by guaranteeing sufficient liquidity and stable funding. It is important to understand how LCR and NSFR affect financial distress in the context of Indian banks for several reasons. Firstly, poor asset quality has made Indian banks more susceptible to liquidity shocks. This study examines the impacts of LCR which mandates banks to maintain a sufficient level of high-quality liquid assets to meet short- term obligations. Secondly, the NSFR assists in evaluating banks capacity to continue operating in the face of persistent stress to provide long-term funding stability. In a nation where regulatory frameworks and economic cycles are changing quickly, these ratios offer a standard by which to measure how well a bank manages its liquidity. Thus, this study not only contributes to the academic understanding of risks and liquidity but also offers practical insight into enhancing financial stability in emerging markets like India. Various research has mentioned the relationship between liquidity and financial distress in the banking sector worldwide (Bu, 2019; Scannella, 2016). Still, there is a lack of studies highlighting detailed analyses specifically for Humanities and Social Sciences Letters, 2025, 14(1): 44-58 46 © 2026 Conscientia Beam. All Rights Reserved. the Indian banking sector. The available studies lack precise insights into how liquidity measures for LCR and NSFR are related to financial distress indicators like Altman’s Z-scores in Indian banks. Addressing this gap is critical for improving our knowledge of the liquidity-risk dynamics specific to the Indian banking system. This paper attempts to close the aforementioned gap by examining the complex relationship between liquidity measures and financial distress in Indian banks. Altman's Z-score has been used in this study to measure the financial distress and evaluate the impact of LCR and NSFR in various situations. To address these gaps in this study research question is given below. RQ1: Does LCR affect the financial health of the Indian banks? RQ2: Does NSFR affect the financial health of the Indian banks? The complexity of relationships Quantile Regression Panel Data Method (QRPDM) is used. Thus, an advanced economics model and integrated approach have been adopted in this study to understand the impact of liquidity on financial distress. Furthermore, a complete and representative analysis is presented by methodically gathering data from 23 banks, which covered public and private sector institutions over a decade (2012-2022). The selected sample covers around two-thirds of all scheduled commercial banks in India. According to RBI data, it accounts for 86% of total assets held by banks in the nation. This vast dataset is the foundation for thoroughly investigating liquidity- related issues that influence financial health. The study is organized as follows: The first section covers the introduction to the study's title. The second section covers the literature study related to the study. The third section evaluates the existing work and establishes the conceptual framework for forming hypotheses. The fourth section mentions the research methodology details. The result of the study is given in the fifth section. Subsequently, the sixth section examines the findings, emphasizing the study's contributions and implications. Finally, section 7 wraps up the paper by offering the conclusions and limitations. 2. LITERATURE REVIEW The rising occurrence of bankruptcy in Indian banks reflects deep-rooted financial distress within the sector (Branch & Khizer, 2016) highlighting systemic challenges and the need for robust risk management frameworks (Bawa, Goyal, Mitra, & Basu, 2019; Komera & Lukose PJ, 2014). Banking crisis has a direct impact on depositors, investors and economic growth (Bhadury & Pratap, 2018). Banks' interconnection may spread crises, resulting in systemic failures and national or worldwide contagions (Bhattacharya, Boot, & Thakor, 1998). For managers, it is crucial to identify the early warning signs of financial distress in banks. This will help in reducing the impact and necessary steps can be initiated to prevent financial disasters in the banking system. Hence, an effective liquidity management approach is important, particularly for the banking sector due to its financial implications for the nation's economy (Laeven, 2011). The challenges faced by the banks bring out the need for a reliable prediction model that will help in identifying the early signs of financial distress to ensure timely regulatory measures (Benston & Kaufman, 1995). A thorough understanding of liquidity, LCR, NSFR and financial distress is essential for developing this predictive model. 2.1. Financial Distress and Liquidity The ability to quickly buy or sell an asset with minimal cost and impact on its price is termed liquidity. Since the mid-1980s, this concept of liquidity has been important in stock returns. Some investors may require access to their funds on urgency highlighting the need to maintain the stock liquidity and assets. High liquidity helps banks to tackle or avoid financial distress situations and reduce the chances of bankruptcy. High liquidity acts as a protective buffer for the banks functioning in the highly dynamic market scenario (Shahdadi, Rostamy, Sadeghi Sharif, & Ranjbar, 2020). Liquidity has a substantial impact on financial distress (Susanti & Takarini, 2022; Yuriani, Merry, Jennie, Ikhsan, & Rahmi, 2020). This impact is considerably negative (Azizah & Yunita, 2022). According to Dance and Imade Humanities and Social Sciences Letters, 2025, 14(1): 44-58 47 © 2026 Conscientia Beam. All Rights Reserved. (2019) liquidity is essential for the financial health, stability, and resilience of the bank. The ability to meet the immediate financial requirements from the available assets requires an extreme liquidity ratio. The lack of financial funds to meet the current bills shows a low liquidity ratio and financial issues (Sutra & Mais, 2019). Understanding the liquidity levels and concerns can help researchers and regulators to predict and prevent financial crises. A study states that liquidity ratios have a significantly and negative influence on financial distress (Lumbantobing, 2020). Another research summarises that liquidity has a minimal negative impact on financial distress (Amanda & Tasman, 2019). Other studies state that liquidity, when judged by the current ratio does not impact financial distress (Antoniawati & Purwohandoko, 2022; Jannah & Dhiba, 2021; Pratiwi & Sudiyatno, 2022; Susilowati, Suwarti, Puspitasari, & Nurmaliani, 2019). These findings bring out the complexity between liquidity and financial distress. Furthermore, it also directs towards a need for detailed analysis between them in different situations. 2.2. Financial Distress and LCR In 2013, the Basel Committee on Banking Supervision created the LCR. It is the key to ensuring that the banks can sustain the short-term liquidity risk in challenging market situations (Basel Committee on Banking Supervision, 2013). Banks require 30 days of adequate volume of high-quality liquid assets (HQLA) to fulfil the net cash outflows (Hoerova, Mendicino, Nikolov, Schepens, & Van den Heuvel, 2018). The primary objective is to improve the preventive buffer of the banks for challenging market times. The second objective aims for 30 days of sufficient liquid assets to resist liquidity needs in banks. During normal times, the LCR sets a minimum level of 100 per cent. During stressful situations, banks can dip below this level and to ease these issues. HQLA can be used. For effective risk management, banks must regularly monitor the LCR. Understanding the role and impact of LCR is important to manage the liquidity risks in banks, especially in financial distress situations. H1: There is a significant relationship between financial distress and LCR in Indian banks. 2.3. Financial Distress and NSFR NSFR ensures that banks maintain a stable financial profile. It matches the long-term assets with similar liabilities to reduce maturity mismatches (Basel Committee on Banking Supervision, 2013). NSFR aims to prevent the over- reliability of short-term liabilities to support long-term assets. It focuses on the stable financing available over a year compared to the need for stable funding. This lowers the risk of financial distress situations (Mariscal- Cáceres, Cristófol-Rodríguez, & Cerdá-Suárez, 2024). NSFR supports that banks should use stable funding sources, like deposits and extend the maturity of assets. This increases the stability of funding and resists funding shocks. Stable financing is important to maintain financial stability and liquidity risks (Wei, Gong, & Wu, 2017). Hence, it is necessary to explore how financial distress situations impact the NSFR in Indian banks. Understanding this impact will help in evaluating the effectiveness of the liquidity risk management strategies and regulatory frameworks. Figure 1 explains the conceptual model of the study to understand the connectivity of liquidity and financial distress. The following hypothesis is assumed. H2: There is a significant relationship between financial distress and NSFR in Indian banks. Figure 1. Conceptual framework. Humanities and Social Sciences Letters, 2025, 14(1): 44-58 48 © 2026 Conscientia Beam. All Rights Reserved. 3. DATA COLLECTION AND RESEARCH APPROACH 3.1. Detail of Data Collection This paper focuses on the Indian banking sector, which involves extensive data collection. A sample of 23 Indian banks was selected to thoroughly analyse the financial factors. Out of 23 banks, 9 banks are public sector banks and 14 are private sector banks. The selection was based on the accessibility of their financial data. The chosen banks show an effective sample for the study as they represent two-thirds of the commercial banks functioning in India covering both the public and private sectors. According to the RBI dataset, this sample accounts for a substantial 86% of the total assets held by banks operating within India, including scheduled banks of the public and private sector banks, small finance banks, foreign banks operating in India, and payment banks. The data collection encompasses an impressive 93% of assets held by all the scheduled private and public commercial banks combined (Reserve Bank of India, 2023). The data collection spanned a decade from 2012 to 2022 and drew from various authoritative sources, including Bloomberg, the Reserve Bank of India (RBI), and annual reports from the respective banks (Duterme, 2023). This extensive and meticulously gathered dataset serves as the cornerstone for a rigorous and comprehensive analysis of the Indian banking sector in the specified timeframe. 3.2. Variables The variables used to investigate how liquidity affects financial distress are listed below in Table 1. Table 1. List of variables. SN Variables Type Code Definition Citations 1 Altman’s Z-score DV. Z-score 1 Z-score 2 Z-score 3 It is a financial metric used to assess the likelihood of financial distress or bankruptcy for a firm. It’s calculated using multivariate discriminant analysis and provides a single score that helps evaluate a company’s financial health. Altman (1968) 2 Liquidity coverage ratio EV. LCR It represents the minimum threshold for short- term liquidity in banks. It measures the bank’s resilience over thirty days. Hartlage (2012) 3 Net stable funding ratio EV. NSFR It is a ratio that shows long-term liquidity in banks. It is assessed by dividing the available stable funds with the bank by the required stable fund amount. Bouzgarrou, Jouida, and Louhichi (2018) 4 Market capitalization CV. lmcap The sum of shares in a bank is multiplied by the share’s current list price. The log of market capitalization is used to make the result consistent. Kanoujiya, Rastogi, and Bhimavarapu (2022) 5 Return on assets CV. ROA It represents the profitability of the bank and is measured as the ratio of net income and total assets. Shingade, Patil, and Jadhav (2022) Note: DV, EV, and CV are the dependent variable, exogenous variable, and control variable used in the study. 3.3. Methodology and Models The research method used in the study is Panel Data Analysis (PDA). It is used in econometrics and various other fields to analyze data that involves the cross-sectional as well as the time series dimensions. Data collection is carried out from multiple entities (banks is the cross-sectional) at various time points (2012- 2022 is the period). This combination makes the result more comprehensive and robust. It increases efficiency, controls the unobserved heterogeneity, and better handles endogeneity. It is used to study complicated relationships that change over time and involve various groups or individuals (Baltagi, 2008; Hsiao, 2007). The dataset of the study shows non-linear characteristics as confirmed by the Shapiro-Wilk W test for normal data. In such studies, showing non-linear features, non-linear practices need to be pertained to (Kartal, Ali, & Nurgazina, 2022; Kirikkaleli, Kartal, & Adebayo, 2022). These techniques are advantageous in such situations due to their lack of assumptions and requirements that all variables exhibit stationarity in the same order. The quantile Humanities and Social Sciences Letters, 2025, 14(1): 44-58 49 © 2026 Conscientia Beam. All Rights Reserved. regression panel data method (QRPDM) is used to carry out regression. Quantile regression analysis is becoming more popular in research fields because it fixes issues with conventional regression which relies only on the average (mean) as the outcome. It uses the median or other specific values as the outcomes in regression models. Pinto et al. (2024); and Alam, Hussain, and Saqib (2023) assess the liquidity of bank by using static and dynamic model but this study uses QRPDM to understand the relationship between liquidity and financial distress of Indian banks. This approach is supported by various studies (Gowlland, Xiao, & Zeng, 2009; Nguyen, Bakry, & Vuong, 2023; Yeh & Liu, 2023). For example, it can create various regression models for different quantiles like 25/100, 50/100, etc. There are six quantile models ( model 1 - 6) used to evaluate the effect of liquidity on financial distress of Indian banks. Models 7 to 12 represent the quadratic quantile model findings. 𝑍 − 𝑠𝑐𝑜𝑟𝑒1𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 1) 𝑍 − 𝑠𝑐𝑜𝑟𝑒2𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 2) 𝑍 − 𝑠𝑐𝑜𝑟𝑒3𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 3) 𝑍 − 𝑠𝑐𝑜𝑟𝑒1𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 4) 𝑍 − 𝑠𝑐𝑜𝑟𝑒2𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 5) 𝑍 − 𝑠𝑐𝑜𝑟𝑒3𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 6) 𝑍 − 𝑠𝑐𝑜𝑟𝑒1𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 7) 𝑍 − 𝑠𝑐𝑜𝑟𝑒2𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 8) 𝑍 − 𝑠𝑐𝑜𝑟𝑒3𝑖𝑡(𝜏) = 𝛳1𝐿𝐶𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 9) 𝑍 − 𝑠𝑐𝑜𝑟𝑒1𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 10) 𝑍 − 𝑠𝑐𝑜𝑟𝑒2𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 11) 𝑍 − 𝑠𝑐𝑜𝑟𝑒3𝑖𝑡(𝜏) = 𝛳1𝑁𝑆𝐹𝑅2𝑖𝑡 + 𝛳2𝑟𝑜𝑎2𝑖𝑡 + 𝛳3𝑙𝑚𝑐𝑎𝑝𝑖𝑡 + 𝛳4𝑐𝑜𝑛𝑠𝑖𝑡 (Eq 12) In the above equation, the dependent variable of the study is z-score1, z-score2, and z-score3. The exogenous variable is LCR and NSFR. Moreover, lmcap and ROA stand for the log of market capitalization and return on assets, respectively, implemented as control variables for the study. The log is used to reduce the inconsistency that occurs due to intense value concerns (Cepoi, Dragotă, Trifan, & Iordache, 2023). “it” is used to symbolize the panel data term where ‘i’ represents the cross-sectional term, and “t” is the time-series term. Table 2. Results of descriptive statistics. Variables Obv Mean St.D MinV MaxV LCR 252 1.256 0.280 0.604 3.715 NSFR 252 1.358 0.155 0.874 2.038 Z-score 1 252 .761 2.59 -0.954 20.880 Z-score 2 252 3.045 11.087 -4.599 93.262 Z-score 3 252 3.932 12.246 -5.366 96.512 Lmcap 252 4.486 0.696 3.001 5.994 ROA 252 .653 0.915 -6.37 2.18 Note: Var, Obv, Mean, St.D, MinV, and MaxV are variables, observations, mean value, standard deviation, minimum value, and maximum value, respectively. 4. DATA ANALYSIS RESULTS 4.1. Descriptive Statistics Table 2 presents the results of descriptive statistics of various variables used in the study. The variable LCR should be above 1, indicating the fulfillment of the minimum regulatory requirement. The LCR spectrum ranges from 0.604 to 3.715, showing that some Indian banks are not fulfilling the minimum requirement in certain years, whereas others hold liquid assets beyond the minimum requirement in the short run. The mean value stands at 1.256, suggesting that banks have LCR slightly above the minimum regulatory requirement on average. Therefore, the banks’ short-term obligations are covered with high-quality liquid assets. The SD stands at 0.28, indicating that the Humanities and Social Sciences Letters, 2025, 14(1): 44-58 50 © 2026 Conscientia Beam. All Rights Reserved. values are clustered around the mean, and banks maintain the LCR requirement consistently over the years. Similarly, banks maintain long-term liquidity requirements consistently over the years without deviation. The Z-score used to measure financial distress in banks shows that it varies significantly between banks. Some banks show healthy financial conditions with high Z-score values. Some have negative Z-scores, indicating a high risk of financial distress and bankruptcy. Certain variables are kept constant to better understand the relationship between the independent and dependent factors, when accounting for the influence of further factors. This leads to more robust and interpretable conclusions in empirical research. lmcap and ROA stand for the log of market capitalization and return on assets, respectively used as control variables for the study. The value of market capitalization falls in a small range with a relatively small deviation. The average of ROA is positive, showing that the banks are profitable but some firms do have negative ROA indicating losses. 4.2. Multicollinearity and Variance Inflation Factors (VIF) The liquidity variables LCR and NSFR show a statistically significant and positive correlation. It can imply that firms with an adequate short-term liquidity ratio also tend to have more stable funding in the long period. The LCR and lmcap have a negative statistical correlation whereas the NSFR and lmcap have no significant statistical correlation. The correlation values between the variables are below 0.8, so the data do not face any problem of multicollinearity (Gujarati & Porter, 2009). Table 3 also presents the VIF of the factors to gauge the multicollinearity concerns between the independent variables. The variables have a VIF lower than 3, so the issue of multicollinearity does not exist. Low correlations between the independent variables will not likely distort the regression analysis results due to multicollinearity. The VIF reconfirms the correlation coefficient results making it more robust. Table 3. Correlation matrix and variance inflation factor (VIF). Variables LCR NSFR lmcap roa LCR 1.0000 NSFR 0.1330** (0.0345) 1.0000 Lmcap -0.1607* (0.0105) -0.0293 (0.6429) 1.0000 ROA -0.0940 (0.1359) -0.1842* (0.0033) 0.0366 (0.5626) 1.0000 VIF 1.05 1.05 1.03 1.04 1.04 Note: A single star ‘*’ sign over the coefficient presents a 1 % significant level and a double star ‘**’ sign over the coefficient presents a 5 % significant level. The VIF test checks multicollinearity among variables in the model. Table 4. Shapiro-Wilk W test for normal data. Variables Obs. W V z Prob>z H0: Normal data Result Z-score 1 253 0.348 119.414 11.134 0.000 Reject H0 Not distributed normally. Z-score 2 253 0.284 131.136 11.352 0.000 Reject H0 Not distributed normally. Z-score 3 253 0.452 100.384 10.730 0.000 Reject H0 Not distributed normally. Note: The test is performed for the normality of dependent variables. 4.3. Normality The above linearity test was conducted to understand the nature of the dependent factors used in the research. Table 4 displays the results of this test. The findings from the Shapiro-Wilk W test for normal data indicate that there is no substantial indication supporting the acceptance of the null hypothesis. In simpler terms, this suggests that the factors do not adhere to the assumptions of normality and linearity. The data is not distributed. When Humanities and Social Sciences Letters, 2025, 14(1): 44-58 51 © 2026 Conscientia Beam. All Rights Reserved. variables exhibit non-linear characteristics, it is advisable to employ a non-linear method. In this context, the quantile panel data regression emerges as a suitable method for understanding the impact of these variables (Kartal et al., 2022; Kirikkaleli et al., 2022). Table 5. Quantile regression result (Base model). Variables Model 1 (Z-score1) Model 2 (Z-score2) Model 3 (Z-score3) Coeff. S.E. Coeff. S.E. Coeff. S.E. Q (25) LCR 0.129 0.365 0.333 0.873 0.195 2.538 lmcap -0.469* 0.146 -0.592*** 0.350 -4.236* 1.017 ROA 0.448* 0.110 0.915* 0.264 2.202* 0.767 Cons 1.696** 0.871 1.704 2.081 16.894* 6.044 Q (50) LCR -0.330** 0.157 -1.291*** 0.694 -0.190 1.408 Lmcap -0.496* 0.063 -1.883* 0.278 -3.906* 0.564 ROA 0.315* 0.047 0.984* 0.210 2.068* 0.425 Cons 2.983* 0.375 11.199* 1.654 19.586* 3.353 Q (75) LCR -0.714* 0.150 -3.083* 0.767 -3.923* 1.129 Lmcap -0.431* 0.060 -1.962* 0.307 -2.573* 0.452 ROA 0.181* 0.045 0.818* 0.232 0.857* 0.341 Cons 3.595* 0.357 15.417* 1.827 22.076* 2.689 Note: A single star ‘*’ sign over the coefficient presents a 1 % significant level, a double star ‘**’ sign over the coefficient presents a 5 % significant level, and a triple ‘***’ sign over the coefficient presents a 10 % significant level. 4.4. Regression Results Table 5 represents base models 1, 2, and 3 at the 25%, 50% and 75% quantile. Base model 1 studies the quantile regression (QR) between the short-run liquidity variable (LCR) and the financial distress variable (z-score1). The relation is negatively significant at 50% and 75% quantiles. This implies that the short-term liquidity represented by LCR negatively impacts financial distress at higher quantiles. The relationship is not significant at the 25% low quantile. A similar result is derived from base model 2 which studies the relation between LCR and z-score2. At 50% and 75% quantiles, the relationship is negatively significant. At 25% low quantiles, the relationship is insignificant between LCR and z-score3 from the base model 3, showing a negative significance relationship only at the 75% quantile. Table 6. Quantile regression result (Base model). Variables Model 4 (z-score1) Model 5 (z-score2) Model 6 (z-score3) Coeff. S.E. Coeff. S.E. Coeff. S.E. Q (25) NSFR -0.178 0.636 -0.134 1.533 4.532 4.630 Lmcap -0.468* 0.140 -0.698** 0.337 -4.250* 1.018 ROA 0.416* 0.108 0.926* 0.260 2.343* .787 Cons 2.126** 1.096 2.850 2.640 11.038 7.972 Q (50) NSFR 0.061 0.308 1.709 1.299 6.358* 2.507 Lmcap -0.432* 0.067 -1.770* 0.285 -3.735* 0.551 ROA 0.335* 0.052 1.335* 0.221 2.192* 0.426 Cons 2.155* 0.530 6.479* 2.238 10.108** 4.318 Q (75) NSFR 0.596** 0.282 3.739* 1.473 5.001** 2.152 Lmcap -0.424* 0.062 -1.672* 0.323 -2.647* 0.473 ROA 0.258* 0.048 1.305* 0.250 1.295* 0.366 Cons 1.763* 0.486 4.824** 2.537 10.171** 3.705 Note: A single star ‘*’ sign on the coefficient presents a 1 % significant level, a double star ‘**’ sign over the coefficient presents a 5 % significant level. Table 6 presents the analysis of quantile regression results for base models 4, 5, and 6. The study explores the NSFR influence on financial distress variables (z-score1, z-score2, and z-score3) at different quantiles (25%, 50%, and 75%). At the 75% quantile, a significant and positive relationship between NSFR and financial distress (z-score1, z- score2, z-score3) is noted. This suggests that at higher quantiles, greater long-term liquidity has a positive influence Humanities and Social Sciences Letters, 2025, 14(1): 44-58 52 © 2026 Conscientia Beam. All Rights Reserved. on the z-score. This shows that the financial health of the banks improves, and chances of distress are reduced. This relationship is not statistically significant at the 25% and 50% quantiles in models 4 and 5. At these lower quantiles, other factors can show a substantial part in influencing financial distress. The effect of short-run liquidity and long- run liquidity has the opposite effect on financial distress. Table 7. Quantile regression result (Quadratic model). Variables Model 7 (z-score1) Model 8 (z-score2) Model 9 (z-score3) Coeff. S.E. Coeff. S.E. Coeff. S.E. Q (25) LCR2 0.033 0.097 0.066 0.236 0.043 0.694 Lmcap -0.461* 0.143 -0.595*** 0.349 -4.230* 1.027 ROA 0.442* 0.109 0.918* 0.265 2.192* 0.779 Cons 1.770* 0.693 2.038 1.686 17.062* 4.959 Q (50) LCR2 -0.098** 0.042 -0.398** 0.181 -0.037 0.380 Lmcap -0.492* 0.063 -1.851* 0.269 -3.896* 0.562 ROA 0.318* 0.047 0.981* 0.204 2.062* 0.427 Cons 2.691* 0.304 10.065* 1.299 19.365* 2.718 Q (75) LCR2 -0.172* 0.046 -0.376*** 0.230 -0.844* 0.324 Lmcap -0.450* 0.068 -1.856* 0.341 -2.552* 0.480 ROA 0.214* 0.052 0.870* 0.258 1.033* 0.364 Cons 3.016* 0.332 11.598* 1.646 18.183* 2.318 Note: A single star ‘*’ sign over the coefficient presents a 1 % significant level and a double star ‘**’ sign over the coefficient presents a 5 % significant level, and a triple ‘***’ sign over the coefficient presents a 10 % significant level. Table 7 represents quadratic quantile models 7, 8, and 9 of Indian banks. In quadratic models 7 and 8 at the 50% and 75% quantiles, the outcome is negatively significant having a p-value less than 0.05. At the 25 % quantile, the result is insignificant. In model 9, the relationship is only significant at a high quantile of 75%. It implies that initially, with the increase in short-term liquidity (LCR), the value of the z-score increases till it reaches a threshold, and after that, the value decreases. Therefore, increased short-term liquidity will initially improve the financial health of the banks. But if this level of LCR crosses the threshold, i.e., there is much more than required liquidity, in such cases, the financial health of the bank will deteriorate shown by the decreasing value of the z-score. Table 8. Quantile regression result (Quadratic model). Variables Model 10 (z-score1) Model 11 (z-score2) Model 12 (z-score3) Coeff. S.E. Coeff. S.E. Coeff. S.E. Q (25) NSFR2 -0.029 0.208 0.152 0.522 1.602 1.567 Lmcap -0.471* 0.135 -0.639*** 0.338 -4.263* 1.016 ROA 0.431* 0.104 0.933* 0.262 2.346* .787 Cons 1.944* 0.737 2.094 1.844 14.288* 5.533 Q (50) NSFR2 0.052 0.103 0.637 0.438 2.386* 0.860 Lmcap -0.430* 0.067 -1.740* 0.284 -3.772* 0.558 ROA 0.339* 0.052 1.397* 0.220 2.246* 0.432 Cons 2.127* 0.366 7.358* 1.546 14.402* 3.039 Q (75) NSFR2 0.176*** 0.098 1.207* 0.511 1.516** 0.740 Lmcap -0.420* 0.063 -1.692* 0.331 -2.680* 0.480 ROA 0.222* 0.049 1.252* 0.256 1.303* 0.372 Cons 2.258* 0.346 7.701* 1.804 14.208* 2.615 Note: A single star ‘*’ sign over the coefficient presents a 1 % significant level, a double star ‘**’ sign over the coefficient presents a 5 % significant level, and a triple ‘***’ sign over the coefficient presents a 10 % significant level. Table 8 represents quadratic quantile models 10, 11, and 12 of Indian banks. In quadratic models 10 and 11 at the 75% quantile, the outcome is positively significant having a p-value less than 0.05. At the 25% and 50% quantile, the outcome is insignificant. In model 12, the relationship is significant at 50% and 75% quantiles. It implies that initially, the value of the z-score decreases till it reaches a threshold, and the value increases with the increase in Humanities and Social Sciences Letters, 2025, 14(1): 44-58 53 © 2026 Conscientia Beam. All Rights Reserved. long-term liquidity (NSFR). Therefore, increased long-term liquidity will initially negatively affect the financial health of the banks. But if this level of NSFR crosses the threshold, i.e., there is more than required liquidity. In such cases, the financial health of the bank will improve as shown by the increasing value of the z-score. These insights are valuable for risk management and strategic decision-making within the banking sector. 4.5. Endogeneity and Robustness The outcomes of the endogeneity test are displayed in Table 9. Two tests were performed to assess the issue of endogeneity: the Durbin Chi2 and Wu Hausman tests, utilizing lag3 values of the variables. Both tests yielded insignificant p-values, which support the null hypothesis that there is no endogeneity. In other words, it suggests that none of the explaining endogenous variables are present. We can conclude that our models are not affected by endogeneity. Multiple models were investigated in this study to ensure the robustness of our findings. We examined liquidity in banks using both the LCR and NSFR. Additionally, financial distress was assessed using two variations of Altman’s Z-scores (Z-score1, Z-score2, and Z-score3). We employed quantile regression and quadratic quantile regression methods to explore the influence of liquidity on financial distress. Consequently, results are robust. Obtained coefficients from quantile regression and quadratic quantile regression methods further validated the significance of the findings. Table 9. Endogeneity. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Durbin Chi-2 0.348 (0.554) 0.263 (0.607) 0.065 (0.798) 0.078 (0.779) 0.009 (0.924) 0.000 (0.999) Wu-Hausman test 0.340 (0.560) 0.256 (0.612) 0.063 (0.801) 0.076 (0.782) 0.008 (0.925) 0.000 (0.999) Note: The value in the parenthesis is the p-value. 5. DISCUSSION 5.1. Hypothesis Testing The Basel III regulatory framework introduced the liquidity coverage ratio and the net stable funding ratio as critical measures to enhance banking stability. These ratios aim to ensure that financial institutions maintain sufficient liquidity to withstand short-term stress and promote long-term funding stability. This study examines the impact of LCR and NSFR on banks' distress levels and stability. The findings from this study indicate a negative significance of LCR on bank stability. Initially, a higher LCR contributes positively to liquidity management by ensuring that banks are prepared for short-term obligations. However, beyond a certain point, an excessively high LCR can increase banks' distress levels. Therefore, the first hypothesis, that LCR significantly improves bank stability is only partially accepted. This outcome suggests that while maintaining a robust liquidity buffer is essential, an overly conservative approach can result in inefficient capital allocation, reduced profitability, and heightened operational stress. On the other hand, NSFR initially increases stress levels up to a certain threshold. This initial increase in stress can be attributed to the adjustments banks must make to align their funding structures with regulatory requirements. Once this threshold is surpassed, the NSFR significantly improves stability and reduces stress levels. Hence, the second hypothesis state that NSFR significantly reduces bank distress over the long term is accepted. This stabilisation effect underscores the long-term benefits of maintaining a balanced funding structure, enhancing the bank's ability to withstand financial shocks and promoting sustainable growth. Mariscal-Cáceres et al. (2024); Susanti and Takarini (2022); Yuriani et al. (2020) and Wei et al. (2017) find that liquidity has a positive effect on financial distress. The result of these studies supports the outcome of this study as NSFR positively affect the NSFR at a higher level. Amanda and Tasman (2019) summarises that liquidity has a minimal negative impact on financial distress which supports the finding of this study as LCR affects the distress level Humanities and Social Sciences Letters, 2025, 14(1): 44-58 54 © 2026 Conscientia Beam. All Rights Reserved. of banks. Other studies state that liquidity does not impact financial distress (Antoniawati & Purwohandoko, 2022; Jannah & Dhiba, 2021; Pratiwi & Sudiyatno, 2022; Susilowati et al., 2019) support the finding that NSFR and LCR have no effect on the distress of banks at a low level. 5.2. Implications This study holds significant implications for various aspects of banking regulation and risk management. It highlights the importance of adjusting the LCR and NSFR requirements to balance short-term liquidity needs with long-term stability goals. When designing and implementing liquidity requirements, regulators and policymakers should take into account the threshold effects shown in this study to prevent unforeseen outcomes. Similarly, banks should aim to build the right balance between keeping appropriate liquidity buffers and efficient capital usage. Bank managers can use the findings of the study to formulate liquidity management strategies. This will help in maintaining the required levels of LCR and NSFR to reduce the financial distress risks. Banks can avoid financial distress situations with regular monitoring of the liquidity balance and following the regulatory norms. Reviews and adjustments of liquidity plans are regularly necessary to ensure the good financial health of the bank in any market scenario. The stakeholders and investors will benefit, helping them towards investment decisions with improved transparency and risk management tools. Investors should consider the liquidity measures LCR and NSFR over the traditional financial measurement factors while evaluating the bank stocks. Banks with a balanced liquidity management approach are likely to remain more stable in varied market scenarios and less prone to financial distress situations. For investors and depositors, banks with a balanced liquidity ratio can be considered as they will be capable of meeting short-term financial needs. Analysts can use the study’s findings and methodology to improvise their bank stability and prediction models. Policymakers and regulators can get in-depth insights into effective liquidity management strategies to contribute towards the overall financial system stability. Policymakers should also promote regulations that require accurate disclosure of liquidity conditions and financial health metrics. This can also improve market discipline and investor confidence. The study significantly contributes to inform decision-making, risk mitigation, and regulatory improvements in the banking sector. 6. CONCLUSION This study aimed to examine the effects of the LCR and NSFR on bank stability. The most important findings reveal that LCR boosts short-term liquidity while excessive levels can increase bank distress. On the other hand, NSFR initially raises stress but enhances stability and reduces distress beyond a certain threshold. These findings support our objectives by highlighting the critical balance required between short-term liquidity and long-term funding stability. In terms of importance, these findings underscore the necessity for banks and regulators not merely to comply with LCR and NSFR requirements but to optimize them. It is important because it provides a detailed understanding of how these ratios function under different conditions. The possible application of these findings extends beyond banking. Non-banking financial companies can also apply these insights to improve their liquidity management practices. Furthermore, these principles can be adapted to other sectors where balancing short-term and long-term financial stability is crucial, thus broadening the scope and relevance of this study. This paper broadens the understanding of liquidity management and its impact on financial stability within the Indian banking sector. Complex relationships were examined using robust quantitative methods. It provides valuable insights for improving bank risk management strategies and decision-making processes. These insights hold significant relevance for policymakers, regulators, and financial institutions in developing more robust liquidity risk management frameworks, thereby boosting overall financial resilience. This study offers important contributions. It is also important to acknowledge its scope and limitations. The study focuses on an in-depth investigation of liquidity metrics and financial distress. Using an extensive dataset from 23 Indian banks over a decade, it primarily considers variations in Altman's Z-score as proxies. Humanities and Social Sciences Letters, 2025, 14(1): 44-58 55 © 2026 Conscientia Beam. All Rights Reserved. 6.1. Limitations and Future Scope The study’s primary foundation is quantitative data, which may not adequately account for qualitative factors like management styles and market perception which can also have an impact on a bank’s funding stability and liquidity. Rigorous statistical techniques were used to minimize biases but it is crucial to recognize the qualitative aspect and unobserved variables may still impact the analysed relationships. Addressing these limitations is essential for framing future research directions in this critical area of financial analysis and risk management. Future research should consider including additional indicators and integrating qualitative data for a more comprehensive analysis within banks by using qualitative analysis to expand on the findings of this study and gain a deeper understanding of the interaction between regulatory compliance, market conditions, and management decisions. Furthermore, the study’s concentration on a particular time period and group of banks may limit the finding’s applicability to other contexts or categories of NBFC. Researchers may also investigate the long-term effects of these policies, particularly under various regulatory frameworks and economic cycles to provide a more complete picture. Additionally, comparison analyses with banks from other developing nations may provide an essential context for understanding these results' applicability. Future studies could also look into how LCR and NSFR affect non-banking financial institutions since these organizations are essential to the larger financial ecosystem. Funding: This study received no specific financial support. Institutional Review Board Statement: Not applicable. Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: All authors contributed equally to the conception and design of the study. All authors have read and agreed to the published version of the manuscript. REFERENCES Adalsteinsson, G. (2014). The liquidity risk management guide: From policy to pitfalls. Hoboken, NJ: John Wiley & Sons. Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551. https://doi.org/10.1016/j.jbankfin.2007.07.014 Alam, T., Hussain, I., & Saqib, A. (2023). Net stable funding and liquidity coverage influence on Islamic bank financial stability: Evidence from Malaysian Islamic banking. International Journal of Economics, Management and Accounting, 31(1), 153–176. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x Amanda, Y., & Tasman, A. (2019). The effect of liquidity, leverage, sales growth and company size on financial distress in manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the period 2015-2017. Jurnal Ecogen, 2(3), 453-462. https://doi.org/10.24036/jmpe.v2i3.7417 Antoniawati, A., & Purwohandoko, P. (2022). Analysis of the effect of profitability, liquidity, and leverage on financial distress in transportation companies listed on the IDX in 2018-2020. Jurnal Ilmu Manajemen, 10(1), 28-38. https://doi.org/10.26740/jim.v10n1.p28-38 Azizah, R. N., & Yunita, I. (2022). The influence of liquidity, leverage, activity and profitability ratios on financial distress conditions using the Altman Z-Score model. Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA), 6(1), 756-773. Baltagi, B. H. (2008). Econometric analysis of panel data. In (Vol. 4, pp. 135-145). Chichester: Wiley. Basel Committee on Banking Supervision. (2013). The liquidity coverage ratio and liquidity risk monitoring tools. Basel: Bank for International Settlements. Bawa, J. K., Goyal, V., Mitra, S. K., & Basu, S. (2019). An analysis of NPAs of Indian banks: Using a comprehensive framework of 31 financial ratios. IIMB Management Review, 31(1), 51-62. https://doi.org/10.1016/j.iimb.2018.08.004 Benston, G. J., & Kaufman, G. G. (1995). Is the banking and payments system fragile? Journal of Financial Services Research, 9(3), 209–240. https://doi.org/10.1007/BF01051747 https://doi.org/10.1016/j.jbankfin.2007.07.014 https://doi.org/10.1111/j.1540-6261.1968.tb00843.x https://doi.org/10.24036/jmpe.v2i3.7417 https://doi.org/10.26740/jim.v10n1.p28-38 https://doi.org/10.1016/j.iimb.2018.08.004 https://doi.org/10.1007/BF01051747 Humanities and Social Sciences Letters, 2025, 14(1): 44-58 56 © 2026 Conscientia Beam. All Rights Reserved. Bhadury, S., & Pratap, B. (2018). India's bad loan conundrum: Recurrent concern for banking system stability and the way forward. In W. A. Barnett & B. S. Sergi (Eds.), Banking and finance issues in emerging markets. In (Vol. 25, pp. 123–161). Bingley, UK: Emerald Group Publishing. Bhattacharya, S., Boot, A. W. A., & Thakor, A. V. (1998). The economics of bank regulation. Journal of Money, Credit and Banking, 30(4), 745–770. https://doi.org/10.2307/2601127 Bouzgarrou, H., Jouida, S., & Louhichi, W. (2018). Bank profitability during and before the financial crisis: Domestic versus foreign banks. Research in International Business and Finance, 44, 26-39. https://doi.org/10.1016/j.ribaf.2017.05.011 Branch, B., & Khizer, A. (2016). Bankruptcy practice in India. International Review of Financial Analysis, 47, 1-6. https://doi.org/10.1016/j.irfa.2016.06.004 Bu, Y. (2019). Research on the impact of financing liquidity on risk-taking of commercial banks. Paper presented at the In MATEC Web of Conferences (Vol. 267, p. 04012). EDP Sciences. Cepoi, C. O., Dragotă, V., Trifan, R., & Iordache, A. (2023). Probability of informed trading during the COVID-19 pandemic: The case of the Romanian Stock Market. Financial Innovation, 9(1), 34. https://doi.org/10.1186/s40854-022-00415-9 Chiaramonte, L., & Casu, B. (2017). Capital and liquidity ratios and financial distress. Evidence from the European banking industry. The British Accounting Review, 49(2), 138-161. https://doi.org/10.1016/j.bar.2016.04.001 Dance, M., & Imade, S. (2019). Financial ratio analysis in predicting financial conditions distress in Indonesia Stock Exchange. Russian Journal of Agricultural and Socio-Economic Sciences, 86(2), 155-165. Das, A., Agarwal, A. K., Jacob, J., Mohapatra, S., Hishikar, S., Bangar, S., . . . Sinha, U. K. (2020). Insolvency and bankruptcy reforms: The way forward. Vikalpa: The Journal for Decision Makers, 45(2), 115-131. https://doi.org/10.1177/0256090920953988 Duterme, T. (2023). Bloomberg and the GameStop saga: The fear of stock market democracy. Economy and Society, 52(3), 373-398. https://doi.org/10.1080/03085147.2023.2189819 El Diri, M., King, T., Spokeviciute, L., & Williams, J. (2021). Hands in the cookie jar: Exploiting loan loss provisions under bank financial distress. Economics Letters, 209, 110098. https://doi.org/10.1016/j.econlet.2021.110098 Flannery, M. J. (1998). Using market information in prudential bank supervision: A review of the U.S. empirical evidence. Journal of Money, Credit and Banking, 30(3), 273–305. https://doi.org/10.2307/2601102 Flannery, M. J., & Bliss, R. R. (2019). Market discipline in regulation: Pre and post crisis. Gowlland, C., Xiao, Z., & Zeng, Q. (2009). Beyond the central tendency: Quantile regression as a tool in quantitative investing. The Journal of Portfolio Management, 35(3), 106–119. https://doi.org/10.3905/jpm.2009.35.3.106 Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. New York: McGraw-Hill. Gupta, J., & Kashiramka, S. (2020). Financial stability of banks in India: Does liquidity creation matter? Pacific-Basin Finance Journal, 64, 101439. https://doi.org/10.1016/j.pacfin.2020.101439 Hartlage, A. W. (2012). The Basel III liquidity coverage ratio and financial stability. Michigan Law Review, 111(3), 453–483. Hoerova, M., Mendicino, C., Nikolov, K., Schepens, G., & Van den Heuvel, S. (2018). Benefits and costs of liquidity regulation: ECB Working Paper. Hsiao, C. (2007). Panel data analysis—advantages and challenges. Test, 16(1), 1-22. https://doi.org/10.1007/s11749-007-0046-x Isayas, Y. N. (2021). Financial distress and its determinants: Evidence from insurance companies in Ethiopia. Cogent Business & Management, 8(1), 1951110. https://doi.org/10.1080/23311975.2021.1951110 Jannah, A. M., & Dhiba, Z. F. (2021). The influence of ownership structure, liquidity and leverage on financial distress in manufacturing companies in BEI. Jurnal Akuntansi, Keuangan Dan Perpajakan, 4(1), 14-23. Jessie, J., & Tannia, T. (2024). The effect of liquidity, activity, profitability, and leverage on the financial distress of properties and real estate companies in 2019-2022. Dinasti International Journal of Management Science, 5(3), 420-429. Kanoujiya, J., Rastogi, S., & Bhimavarapu, V. M. (2022). Competition and distress in banks in India: An application of panel data. Cogent Economics & Finance, 10(1), 2122177. https://doi.org/10.1080/23322039.2022.2122177 https://doi.org/10.2307/2601127 https://doi.org/10.1016/j.ribaf.2017.05.011 https://doi.org/10.1016/j.irfa.2016.06.004 https://doi.org/10.1186/s40854-022-00415-9 https://doi.org/10.1016/j.bar.2016.04.001 https://doi.org/10.1177/0256090920953988 https://doi.org/10.1080/03085147.2023.2189819 https://doi.org/10.1016/j.econlet.2021.110098 https://doi.org/10.2307/2601102 https://doi.org/10.3905/jpm.2009.35.3.106 https://doi.org/10.1016/j.pacfin.2020.101439 https://doi.org/10.1007/s11749-007-0046-x https://doi.org/10.1080/23311975.2021.1951110 https://doi.org/10.1080/23322039.2022.2122177 Humanities and Social Sciences Letters, 2025, 14(1): 44-58 57 © 2026 Conscientia Beam. All Rights Reserved. Kartal, M. T., Ali, U., & Nurgazina, Z. (2022). Asymmetric effect of electricity consumption on CO2 emissions in the USA: Analysis of end-user electricity consumption by nonlinear quantile approaches. Environmental Science and Pollution Research, 29(55), 83824-83838. https://doi.org/10.1007/s11356-022-21715-8 Kebede, T. N., Tesfaye, G. D., & Erana, O. T. (2024). Determinants of financial distress: Evidence from insurance companies in Ethiopia. Journal of Innovation and Entrepreneurship, 13(1), 17. https://doi.org/10.1186/s13731-024-00369-5 Kirikkaleli, D., Kartal, M. T., & Adebayo, T. S. (2022). Time and frequency dependency of foreign exchange rates and country risk: Evidence from Turkey. Bulletin of Monetary Economics and Banking, 25(1), 37-54. https://doi.org/10.21098/bemp.v25i1.1838 Kiros, Y. (2020). Loan repayment performance of micro and small enterprises: Evidence from Somali Region, Ethiopia. Developing Country Studies, 10(9), 1-13. https://doi.org/10.7176/dcs10-9-01 Kisman, Z., & Krisandi, D. (2019). How to predict financial distress in the wholesale sector: Lesson from Indonesian Stock Exchange. Journal of Economics and Business, 2(3), 569–585. https://doi.org/10.31014/AIOR.1992.02.03.109 Komera, S., & Lukose PJ, J. (2014). Corporate bankruptcy, soft budget constraints, and business group affiliation: Evidence from Indian firms. Review of Pacific Basin Financial Markets and Policies, 17(03), 1450016. Laeven, L. (2011). Banking crises: A review. Annual Review of Financial Economics, 3(1), 17-40. https://doi.org/10.1146/annurev- financial-102710-144816 Lumbantobing, R. (2020). The effect of financial ratios on the possibility of financial distress in selected manufacturing companies which listed in Indonesia stock exchange. Paper presented at the 6th Annual International Conference on Management Research (AICMaR 2019) (pp. 60-63). Atlantis Press. Männasoo, K., & Mayes, D. G. (2009). Explaining bank distress in Eastern European transition economies. Journal of Banking & Finance, 33(2), 244-253. https://doi.org/10.1016/j.jbankfin.2008.07.016 Mariscal-Cáceres, J., Cristófol-Rodríguez, C., & Cerdá-Suárez, L. M. (2024). Regulatory implications of the supervision and management of liquidity risk: An analysis of recent developments in Spanish financial institutions. Journal of Risk and Financial Management, 17(2), 46. https://doi.org/10.3390/jrfm17020046 Maulida, I. S., Moehaditoyo, S. H., & Nugroho, M. (2018). Financial ratio analysis to predict financial distress in manufacturing companies listed on the Indonesian stock exchange 2014-2016. Jurnal Ilmiah Administrasi Bisnis Dan Inovasi, 2(1), 180- 194. Nguyen, H. M., Bakry, W., & Vuong, T. H. G. (2023). COVID-19 pandemic and herd behavior: Evidence from a frontier market. Journal of Behavioral and Experimental Finance, 38, 100807. https://doi.org/10.1016/j.jbef.2023.100807 Pinto, G., Rastogi, S., & Agarwal, B. (2024). Does promoters’ holding influence the liquidity risk of banks? Journal of Financial Regulation and Compliance, 32(2), 211-229. https://doi.org/10.1108/JFRC-09-2023-0144 Pratiwi, E. Y., & Sudiyatno, B. (2022). The effect of liquidity, leverage, and profitability on financial distress. Fair Value: Jurnal Ilmiah Akuntansi Dan Keuangan, 5(3), 1324-1332. Reserve Bank of India. (2023). Liabilities and assets of scheduled commercial banks. Mumbai: Reserve Bank of India. Scannella, E. (2016). Theory and regulation of liquidity risk management in banking. International Journal of Risk Assessment and Management, 19(1-2), 4-21. https://doi.org/10.1504/ijram.2016.074433 Shahdadi, K. M., Rostamy, A. A. A., Sadeghi Sharif, S. J., & Ranjbar, M. H. (2020). Intellectual capital, liquidity, and bankruptcy likelihood. The Journal of Corporate Accounting & Finance, 31(4), 21-32. https://doi.org/10.1002/jcaf.22460 Sharma, N. (2013). Altman model and financial soundness of Indian banks. International Journal of Accounting and Finance, 3(2), 55- 60. Shingade, S., Patil, S., & Jadhav, P. (2022). Financial performance analysis of banks: A study of selected public and private sector banks in India. International Journal of Research in Finance and Marketing, 12(3), 45–58. Soenen, N., & Vander Vennet, R. (2022). Determinants of European banks’ default risk. Finance Research Letters, 47, 102557. https://doi.org/10.1016/j.frl.2021.102557 https://doi.org/10.1007/s11356-022-21715-8 https://doi.org/10.1186/s13731-024-00369-5 https://doi.org/10.21098/bemp.v25i1.1838 https://doi.org/10.7176/dcs10-9-01 https://doi.org/10.31014/AIOR.1992.02.03.109 https://doi.org/10.1146/annurev-financial-102710-144816 https://doi.org/10.1146/annurev-financial-102710-144816 https://doi.org/10.1016/j.jbankfin.2008.07.016 https://doi.org/10.3390/jrfm17020046 https://doi.org/10.1016/j.jbef.2023.100807 https://doi.org/10.1108/JFRC-09-2023-0144 https://doi.org/10.1504/ijram.2016.074433 https://doi.org/10.1002/jcaf.22460 https://doi.org/10.1016/j.frl.2021.102557 Humanities and Social Sciences Letters, 2025, 14(1): 44-58 58 © 2026 Conscientia Beam. All Rights Reserved. Susanti, W., & Takarini, N. (2022). The influence of liquidity, profitability, leverage, and activity in predicting financial distress in retail trade subsector companies listed on the IDX. Ekonomis: Journal of Economics and Business, 6(2), 488-497. Susilowati, Y., Suwarti, T., Puspitasari, E., & Nurmaliani, F. A. (2019). The effect of liquidity, leverage, profitability, operating capacity, and managerial agency cost on financial distress of manufacturing companies listed in Indonesian stock exchange. Paper presented at the 2019 International Conference on Organizational Innovation (ICOI 2019) (pp. 651-656). Atlantis Press. Sutra, F. M., & Mais, R. G. (2019). Factors influencing financial distress using the Altman z-score approach in mining companies listed on the Indonesia stock exchange in 2015-2017. Jurnal Akuntansi Dan Manajemen, 16(01), 34-72. Wei, X., Gong, Y., & Wu, H.-M. (2017). The impacts of Net Stable Funding Ratio requirement on Banks’ choices of debt maturity. Journal of Banking & Finance, 82, 229-243. https://doi.org/10.1016/j.jbankfin.2017.02.006 Yeh, I.-C., & Liu, Y.-C. (2023). Exploring the growth value equity valuation model with data visualization. Financial Innovation, 9(1), 2. https://doi.org/10.1186/s40854-022-00400-2 Yuriani, Y., Merry, M., Jennie, J., Ikhsan, M., & Rahmi, N. U. (2020). The influence of ownership structure, liquidity, leverage, and activity (TATO) on financial distress of consumer goods industry companies listed on the Indonesia Stock Exchange. COSTING: Journal of Economic, Business and Accounting, 4(1), 208-218. https://doi.org/10.31539/costing.v4i1.1325 Views and opinions expressed in this article are the views and opinions of the author(s), Humanities and Social Sciences Letters shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. https://doi.org/10.1016/j.jbankfin.2017.02.006 https://doi.org/10.1186/s40854-022-00400-2 https://doi.org/10.31539/costing.v4i1.1325