40 © 2025 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Economics and Empirical Research Vol. 12, No. 1, 40-51, 2025 ISSN(E) 2409-2622 / ISSN(P) 2518-010X DOI: 10.20448/ajeer.v12i1.6825 © 2025 by the authors; licensee Asian Online Journal Publishing Group Measuring the financial health using the Altman Z-score model: A case study on listed banks in Bangladesh Md. Tamim Hasan1 Rownok Ara2 ( Corresponding Author) 1Department of Business Administration, Northern University Bangladesh. Email: tamim.hasan@nub.ac.bd 2Department of Accounting and Information Systems, Jashore University of Science and Technology, Bangladesh. Email: rownokararr@gmail.com Abstract The study evaluates the financial performance of publicly listed banks in Bangladesh and forecasts potential financial distress using the Altman Z-score model. Based solely on secondary data from annual reports of the banks over the period 2018 to 2023, the analysis reveals alarming findings. Across the listed banks, 34 banks scored an average Z score of below the threshold level, placing them in the “Financial Distress Zone”, indicating a likelihood of financial difficulty or potential bankruptcy in the near future. Notably, 16 banks are not only scored below the threshold level and were placed in the financial distress zone but also recorded negative values, indicating a high risk of imminent financial collapsed. Only two banks namely Union Bank and Uttara Bank achieved average Z scores above the threshold level and were categorized in the “Grey Zone”, suggesting a reduced risk of financial distress in the short-term, though they should still remain conscious about their financial activities. The findings underscore the need for regulatory authorities to implement proactive measures to address financial instability within the banking sector. Additionally, the results offer valuable insights for bank managers, shareholders, investors, lenders, and customers to assess and mitigate financial risks, thereby contributing to informed decisions and promoting financial stability across the industry. Keywords: Altman Z-Score, Bankruptcy, Banking industry, Financial health, Financial distress, Financial risk, Financial performance, Financial stability. JEL Classification: C53; G33; G21; G32; G01. Citation | Hasan, M. T., & Ara, R. (2025). Measuring the financial health using the Altman Z-score model: A case study on listed banks in Bangladesh. Asian Journal of Economics and Empirical Research, 12(1), 40–51. 10.20448/ajeer.v12i1.6825 History: Received: 30 April 2025 Revised: 26 May 2025 Accepted: 12 June 2025 Published: 25 June 2025 Licensed: This work is licensed under a Creative Commons Attribution 4.0 License Publisher: Asian Online Journal Publishing Group Funding: This study received no specific financial support. Institutional Review Board Statement: Not applicable. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Both authors contributed equally to the conception and design of the study. Both authors have read and agreed to the published version of the manuscript. Contents 1. Introduction ...................................................................................................................................................................................... 41 2. Literature Review ............................................................................................................................................................................ 41 3. Materials and Methods ................................................................................................................................................................... 42 4. Result and Discussion ..................................................................................................................................................................... 43 5. Conclusion ......................................................................................................................................................................................... 45 References .............................................................................................................................................................................................. 45 Appendix ................................................................................................................................................................................................ 45 mailto:tamim.hasan@nub.ac.bd mailto:rownokararr@gmail.com https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://www.doi.org/10.20448/ajeer.v12i1.6825 https://orcid.org/0000-0002-7114-0348 https://orcid.org/0009-0005-9323-8839 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 41 © 2025 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study uniquely applies Altman’s Z-score model to all 36 listed Bangladeshi banks from 2018 to 2023, highlighting the sector’s financial distress amid the July 2024 revolution. It offers timely insights into banking vulnerabilities during a period marked by political upheaval, economic disruption, and systematic corruption. 1. Introduction Rapid financial integration, technological development, and demographic shifts over the past 20 years have produced both significant new difficulties and opportunities for national economies (Qamruzzaman, 2014). In such a quickly changing and competitive market, banks and other financial organizations serve as the foundation of the entire economy. They offer funding for economic growth, infrastructure improvements, employment growth, and modernization. Additionally, banks have a significant impact on society by influencing not only individual consumers’ spending but also the expansion of entire financial sectors (Uddin & Kaium, 2015). Bangladeshi banking sector has recently grown in terms of the number of institutions, sophisticated financial tools, asset size, skilled human resources, etc. However, there are several reasons such as default loans, financial errors, money laundering, internal and external scams and many more, this sector of the economy has been facing enormous difficulties. As a result, the banking sector’s total performance is significantly impacted (Khatun, 2018). Moreover, the central bank and financial professionals in in the country are currently concerned about the stability of the financial system. At present, the stability of Bangladeshi banking system and economy is of highest importance, as Bangladesh is going through a lot of political instability due to the ongoing student movement in Bangladesh. Reportedly, the prime minister was forced to resign. In that case, people have become anxious about their savings deposited in different commercial banks in Bangladesh. Besides, the performance of banking sector has been worsening gradually over the years. As a consequence, the study is conducted to examine and predict the financial health of 36 listed banks of Bangladesh over the period from 2018 to 2023. The study aims to evaluate the performance of the banks, forecast the banking industry’s future financial distress, assess the risk of bankruptcy, validate the Altman Z-score model, and forecast future distress using the Altman’s Z-score model. Financial distress is a situation in which a business or individual is unable to generate revenue or income due to its inability to fulfill or pay its financial obligations. The final stage before bankruptcy is typically preceded by a period of financial strain. This is often caused by high fixed costs, illiquid assets, or revenue streams vulnerable to economic downturns (Nath, Biswas, Rashid, & Biswas, 2020). Fisher (1936) often known as Sir Ronald Aylmer Fisher, created the linear discriminant analysis method in 1936. On the other hand, Altman (1968) developed the “Z-Score Model” for bankruptcy prediction in 1968. It is essentially a modified description of R.A. Fisher’s discriminant analysis method. Whether a company will file for bankruptcy within two years or not can be predicted using the Z-score method. The Z-score algorithm makes use of an organization’s income statement and balance sheet figures to assess its financial soundness (Nath et al., 2020). This study uses Altman’s z-score model to forecast the financial health of Bangladesh’s banking sector. The main aim of this study is to use the Altman Z-score Model to forecast the financial health of 36 listed banks of Bangladesh over the period from 2018 to 2023. The secondary data is collected for this study. The rest of this paper is organized as follows. Part 2 describes the literature review of the study. Part 3 and 4 contain the research methodology, the discussions and analysis of the study. Part 5 describes the summary of the study. 2. Literature Review The Altman Z-score, introduced by Altman (1968), is a widely recognized and utilized model for predicting corporate bankruptcy and assessing financial health. Originally developed for manufacturing firms, the Z-score model has undergone various adaptations to be applicable across different industries, including the banking sector. Altman’s pioneering work built upon previous research by Beaver (1966) who used a univariate analysis to predict business failures. In contrast, Altman’s model employs multivariate discriminant analysis, incorporating several financial ratios to improve prediction accuracy. Over time, the model has been refined to better suit the changing dynamics of global financial systems. Altman, Haldeman, & Narayanan (1977) introduced the ZETA model, which extended the original Z-score to predict financial distress up to five years before bankruptcy, making it highly relevant for long-term financial planning. Subsequent revisions, such as the Altman Z-score Plus, have further broadened its applicability to both public and private firms, manufacturing and non-manufacturing entities, and companies across different geographic regions (Altman, 2002). In the context of banking, the Z-score has proven to be a valuable tool for regulators, investors, and managers to gauge financial stability and predict distress. For instance, Chieng (2013) confirmed the validity of the Z-score model in predicting the future distress of European banks, demonstrating its robustness even during the financial crisis. Similarly, studies on Bangladeshi banks have employed the Z-score to compare the financial health of conventional and Shariah-compliant banks, revealing that Islamic banks often exhibit higher financial stability (Saha & Navila, 2018). Despite its widespread use, the Altman Z-score model is not without limitations. Critics point out that the model’s reliance on accounting data, which may be subject to manipulation, and its assumption of linearity in the relationships between variables, can sometimes result in inaccurate predictions (Li & Rahgozar, 2012). Nonetheless, the Z-score remains a crucial metric in financial analysis, particularly for its simplicity and effectiveness in providing early warnings of financial distress. Altman, Hartzell, & Peck (1995) added a constant (+3.25) to the Z-score values to normalize them and make scores of zero or below “equivalent to the default situation”. A confirmatory study using Eurozone banks was undertaken in 2013 to support this updated model. Chieng (2013) chose four distressed banks and used data from the previous five years to demonstrate that the Altman Z-score model can predict future bank distress. The study’s findings supported Altman Z-score’s ability to predict Eurozone banks’ behavior. Additional research has been conducted by Siskos (2014) in this regard. He concluded that utilizing Altman Z-score and Beneish M-score, the Enron’s scandal of 2001 could have been detected, which ultimately contributed to the largest business bankruptcy Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 42 © 2025 by the authors; licensee Asian Online Journal Publishing Group in history (Ahmed, 2015). As a result, this study applies the Z-score model to evaluate the financial health of Bangladeshi listed banks. Parvin, Rahman, and Nitu (2016) compared the Z-scores of state-owned commercial banks (SOCBs) and private commercial banks (PCBs) to predict the financial health of the banking sector using Altman’s Z-score model. The data shows that SOCBs were in better financial health than PCBs. In the empirical analysis of the liquidity, profitability, and solvency, Abdullah (2015) discovered that while 22 banks were insolvent during the financial years from 2009 to 2014, only 7 banks were in a sound financial position. Additionally, Islamic or Sariah-compliant banks performed better than conventional banks. He also noted that state-owned banks have improved compared to previous performance. Mostofa, Rezina, and Hasan (2016) used the Z score model of Altman to predict the financial distress of Bangladesh’s private sector banking industry and found that the model was 72% accurate at predicting bankruptcy two years in advance. However, previous studies have not been conducted on all the listed banks of Bangladesh. Bangladesh is currently experiencing significant financial instability due to political turmoil, as mentioned earlier. Moreover, Bangladeshi financial institutions, including Bangladesh Bank, have been facing widespread loan scams. Furthermore, the stability of the financial system has become a major concern for the central bank and professionals in our country. The current priority is ensuring the stability of Bangladesh’s banking system and economy, especially given the ongoing political unrest triggered by recent student movements. This situation even led to the resignation of the prime minister. In light of these circumstances, this study has been undertaken to assess and predict the financial health of 36 listed banks in Bangladesh over the period from 2018 to 2023. Considering all these issues, the study is conducted to predict the financial health of Bangladeshi listed banks. In conclusion, the Altman Z-score has established itself as an essential tool in assessing the financial health and stability of banks. Its continued relevance, despite evolving market conditions, underscores the model's robustness and adaptability, making it a cornerstone of financial risk assessment in the banking sector. 3. Materials and Methods 3.1. Sample Selection and Data Sources This study focuses on 36 banks that were listed on the Dhaka Stock Exchange (DSE) as of December 2023, covering the period from 2018 to 2023. The financial health of these banks is assessed using ratios such as working capital to total assets, retained earnings to total assets, earnings before interest and tax to total assets, and shareholders’ equity to total liabilities. Most of the data was gathered from the annual reports of the banks. 3.2. Variables’ Definition and Measurements This study adopts Altman’s Z-score model to assess the bankruptcy risk of the listed banks in Bangladesh. The model utilizes four financial ratios, each representing a distinct aspect of a firm’s financial health. Table 1 presents these independent variables, including their formulas and descriptions. Financial evaluations, which primarily rely on financial statements, are among the oldest and most significant methods for assessing business performance (Qamruzzaman, 2014). The main purpose of this study is to forecast the financial stability of Bangladeshi banking sector. Particular attention has been paid to the 36 listed banks of Bangladesh. In addition, arguments presented by various authors regarding financial ratios and indicators used for bankruptcy prediction served as inspiration for this study (Beaver, 1966). The four independent variables in the Altman Z-score model, each representing typical financial ratios, are weighted by coefficients. The following equation for insolvency or potential bankruptcy of non-manufacturing or service businesses has been examined using the Altman Z score model (Altman, 1968). 𝐹𝑜𝑟𝑚𝑢𝑙𝑎: 𝑍 − 𝑆𝑐𝑜𝑟𝑒 𝑏𝑎𝑛𝑘𝑟𝑢𝑝𝑡𝑐𝑦 𝑚𝑜𝑑𝑒𝑙: 𝑍 = 6.56𝑋1 + 3.26𝑋2 + 6.72𝑋3 + 1.05𝑋4 Where, Table 1. Independent variables of Z-score. Variable Formula Description X1 (Current assets − Current liabilities) / Total assets This ratio represents the firm's liquid assets. X2 Retained earnings / Total assets. It displays the age and earning capacity of the company. X3 Earnings before interest and taxes / Total assets In addition to tax and leverage variables, it analyzes operating efficiency. It displays operating income X4 Market value of equity / Book value of total liabilities This ratio shows how the fair market value of a company's share has performed in relation to the book value of the outstanding loan capital. 3.3. Zones of Discriminations The Altman Z-score model categorizes banks into distinct zones based on their financial health, aiding in the assessment of bankruptcy risk. Table 2 presents these zones, portraying the threshold and corresponding interpretations. Table 2. Indicator of Z-score. SL Score Indicator Description 1 Z > 2.6 “Safe” The bank is financially stable, and there is little chance that it will experience financial trouble. The bank’s financial situation is sound, it can be argued. 2 1.1 ≤ Z ≤ 2.6 “Grey” The bank is in the gray area, which suggests there is less chance that it may soon experience financial trouble. 3 Z < 1.1 “Distress” The likelihood that the bank may experience financial difficulty or possibly bankruptcy in the near future is very high. One may say that the bank is in a precarious position. Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 43 © 2025 by the authors; licensee Asian Online Journal Publishing Group Secondary data were collected from annual reports of the banks. The study particularly focuses on the six-year period between 2018 and 2023 using publicly available financial reports. The Z-score model was used to predict the financially distressed and non-distressed banks after various financial ratios were computed for the study’s analysis. The following equation has been examined for bankruptcy or potential insolvency of non-manufacturing or service industries using the Altman Z score model (Altman, 1968). 3.4. Tool Applied 𝑍 − 𝑆𝑐𝑜𝑟𝑒 𝑏𝑎𝑛𝑘𝑟𝑢𝑝𝑡𝑐𝑦 𝑚𝑜𝑑𝑒𝑙: 𝑍 = 6.56𝑋1 + 3.26𝑋2 + 6.72𝑋3 + 1.05𝑋4 • Where, X1= Working capital / Total asset. A common metric for assessing a business’s liquidity, effectiveness, and general health is working capital. Total assets display all bank assets, including short- and long-term investments. A bank’s liquidity and capacity to fulfill short-term obligations to creditors are shown by the WC/TA ratio. • X2= Retained earnings / Total assets. The amount of net earnings carried over to the following years is known as retained earnings. The ratio used to determine a bank’s cumulative profitability is Accumulated Retained Earnings to Total Asset (TA). • X3= Operating earnings / Total assets. EBIT, or Earnings before Interest and Taxes, displays a bank’s operating profit. An organization’s operational efficiency is measured by EBIT to Total Asset. The value of this ratio reveals the firm’s ability to make enough money to cover fixed obligations like interest. • X4= Market value of equity / Total liabilities. This ratio represents the market value of shareholders’ equity relative to total liabilities. In relation to the total liabilities, this ratio showed how the fair market value of the bank’s stock performed. A higher ratio typically indicates a stronger market perception, often reflected in increasing share prices. The higher the values of each of the four ratios required to construct the Z-score, the better. It suggests that a bank’s financial health improves with higher ratios (Parvin et al., 2016). Beaver was a pioneer in the empirical study of bankruptcy risk; yet, the univariate structure of the model that he created is chiefly responsible for his work’s limitations. It only permits the use of one ratio at once (Beaver, 1966). By adding four additional factors to the model in Altman (1968) improved on Beaver’s work and produced a prediction of manufacturing firm failure that was ultimately more accurate. Beaver’s model and Altman’s multi- discriminant analysis (MDA) model differed in the financial ratios selected for optimal prediction accuracy. Altman classified companies into two mutually exclusive categories: bankrupt and non-bankrupt. The Zeta Credit Risk model was created by Altman et al. (1977) as a second-generation discriminant model that “seemed to be quite accurate for up to five years prior to failure” (Altman, 2002). To account for various criteria and the shifting corporate landscape, the z-score model has been revised frequently (Altman, 2002). 4. Result and Discussion Here, Table 3 shows the average calculation of Z score for AB Bank Limited and the calculation for the rest of the banks is shown in the Appendix 1. Table 4 shows the discriminant zones of the listed banks of Bangladesh using Z-score Model. According to this approach, any commercial bank that receives a score higher than 2.6 should be classified as safe. However, if it doesn’t get a score of at least 1.1, it will be placed in the distress zone and more likely to be declared bankrupt. If the Z score falls in the range between 1.1 and 2.6, it should be in the grey area. Table 3. Data analysis of AB bank limited (See appendix for the rest of the listed banks). Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. AB bank 2023 0.015 0.036 0.041 0.022 0.110 0.146 2022 0.008 0.041 0.046 0.023 0.120 2021 0.159 0.047 0.032 0.031 0.270 2020 0.006 0.056 0.023 0.028 0.110 2019 0.008 0.061 0.025 0.019 0.110 2018 0.036 0.070 0.015 0.032 0.150 The present study showed had an average Z-score of less than 1.1 and are placed in the “Distress Zone” except 02 banks namely Union bank and Uttara bank. This indicates that there is a high probability of some banks becoming bankrupt in the near future. However, the average Z-scores of Union Bank and Uttara Bank are 1.30 and 1.56, respectively. Among 36 banks, Union bank and Uttara bank are placed under “Grey Zone”. This indicates that there is less probability of having financial trouble in near future but they also need to be cautious about their financial activities. Table 4. Average Z- score table of 36 listed banks and their financial health. SL no. Bank name Year- 2023 Year- 2022 Year- 2021 Year- 2020 Year- 2019 Year- 2018 Avg. Z score Indicator Std. dev. 1 AB Bank 0.114 0.119 0.268 0.113 0.113 0.152 0.146 Distress 0.061 2 Al-Arafah Bank -2.906 0.191 1.320 -1.269 0.506 0.404 -0.293 Distress 1.532 3 Bank Asia 0.221 0.414 0.453 0.382 0.355 0.418 0.374 Distress 0.082 4 BRAC Bank 1.108 0.853 1.236 0.623 0.796 0.919 0.922 Distress 0.220 5 City Bank -0.311 -0.218 -0.048 -0.216 -0.348 0.121 -0.170 Distress 0.176 6 Dhaka Bank -1.255 -1.176 -1.101 -1.098 -0.754 -0.619 -1.000 Distress 0.253 7 Dutch-Bangla Bank 0.439 0.787 0.580 0.425 0.637 0.574 0.574 Distress 0.134 8 Eastern Bank 0.949 0.935 1.185 0.965 0.978 0.946 0.993 Distress 0.095 9 Exim Bank -0.669 -0.667 -0.609 -0.512 -0.447 -0.488 -0.565 Distress 0.096 10 FSIBL 0.272 0.316 0.307 0.254 0.333 0.174 0.276 Distress 0.058 11 Global Islami Bank 0.559 0.533 0.327 0.406 0.330 0.339 0.416 Distress 0.105 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 44 © 2025 by the authors; licensee Asian Online Journal Publishing Group SL no. Bank name Year- 2023 Year- 2022 Year- 2021 Year- 2020 Year- 2019 Year- 2018 Avg. Z score Indicator Std. dev. 12 ICB Islamic Bank -6.739 -9.214 -4.684 -8.537 -5.759 -5.222 -6.692 Distress 1.835 13 IFIC Bank -0.528 -0.449 -0.068 0.062 0.080 -0.030 -0.156 Distress 0.265 14 Islami Bank -0.358 0.365 0.294 0.428 0.450 0.493 0.279 Distress 0.319 15 Jamuna Bank -0.797 -0.250 -0.509 -0.195 -0.019 0.522 -0.208 Distress 0.449 16 Mercantile Bank 0.363 0.363 0.303 0.409 0.367 0.440 0.374 Distress 0.047 17 Midland Bank -0.540 0.873 0.248 0.062 1.359 0.518 0.420 Distress 0.660 18 Mutual Trust Bank 0.283 0.398 0.336 0.317 0.199 0.246 0.296 Distress 0.070 19 National Bank -5.931 -1.176 0.077 0.061 0.011 -0.585 -1.257 Distress 2.342 20 NCC Bank 0.166 0.367 0.317 0.576 0.310 0.271 0.335 Distress 0.136 21 NRB Bank -1.198 -1.181 -0.789 -0.503 -0.948 -1.027 -0.941 Distress 0.263 22 NRBC Bank -1.708 -1.512 -1.245 -0.955 -0.282 0.387 -0.886 Distress 0.798 23 One Bank -1.527 -1.230 -1.065 -0.972 0.155 0.168 -0.745 Distress 0.727 24 The Premier Bank -0.754 -0.934 -1.023 -0.930 -1.348 -1.534 -1.087 Distress 0.294 25 Prime Bank 0.361 0.066 0.579 1.025 0.744 0.292 0.511 Distress 0.344 26 Pubali Bank 0.600 0.549 -12.031 0.426 0.397 0.439 -1.603 Distress 5.109 27 Rupali Bank -1.507 -1.450 -1.548 -1.389 -1.102 -0.920 -1.319 Distress 0.251 28 SBAC Bank 0.439 0.423 0.600 0.734 0.685 0.814 0.616 Distress 0.159 29 Shahjalal Islami Bank 0.408 0.406 0.463 0.363 0.396 0.318 0.392 Distress 0.049 30 Social Islami Bank 0.178 0.208 0.181 0.188 0.210 0.243 0.201 Distress 0.025 31 Southeast Bank -0.111 -0.404 -0.073 0.372 -0.015 -0.177 -0.068 Distress 0.254 32 Standard Bank 0.294 0.442 0.501 0.582 0.266 0.584 0.445 Distress 0.138 33 Trust Bank -2.038 -1.826 -1.581 -1.948 -1.494 -1.312 -1.700 Distress 0.283 34 UCB 0.366 0.192 0.287 0.530 0.552 0.629 0.426 Distress 0.171 35 Union Bank 1.293 1.363 1.149 1.374 1.644 0.997 1.303 Grey 0.220 36 Uttara Bank 1.856 1.525 1.469 1.806 1.241 1.489 1.564 Grey 0.230 Note: 2.6>Safe, Between 1.1 to 2.6= Grey, and 1.1<=Distress However, the most alarming issue is that 16 banks including Al-Arafah Bank, City Bank, Dhaka Bank, Exim Bank, ICB Islami Bank, IFIC Bank, Jamuna Bank, National Bank, NRB, NRBC, One Bank, The Premier Bank, Pubali Bank, Rupali Bank, Southeast Bank and Trust Bank scored negative point while measuring the financial health. According to the zone of criteria of Altman Z-score, these 16 banks are not only scored below 1.1 and were placed in the financial distress zone but also received negative scores; indicating that these banks might be financially collapsed in the near future as their liquidity, overall working capital, total asset, total liabilities, market value of equity, operating earnings and retained earnings are in a precarious condition. It is surprisingly found that ICB Islami Bank scored a z-score of -6.69 during the study period from 2018 to 2023. Hence, there is a high likelihood that ICB Islami Bank may soon become bankrupt. In that condition, immediate action should be taken by the authority of the ICB Islami Bank to improve their financial condition. All banks were performing poorly during the study period from 2018 to 2023 in terms of financial stability as all of them scored less than 1.1 except two banks. The likelihood that all the banks may experience financial difficulty or possibly bankruptcy in the near future is very high. One may assume that all the banks are in a precarious position. The authoritative body of all the listed banks in Bangladesh must be concerned about their performance. They should take necessary steps for improving their banks’ performance. Otherwise, all of them might face huge financial instability in the near future. A graphical presentation of average Z score and their financial health is attached below. Figure 1. Financial health score of 36 listed banks. This Figure 1 presents the negative discriminant zone in rust and positive zone in navy blue color. Rust colors banks are in very precarious position in terms of financial stability. In this case, every bank should be cautious Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 45 © 2025 by the authors; licensee Asian Online Journal Publishing Group regarding their financial stability. They need to find out how to perform well and take necessary steps to fix their financial health. 5. Conclusion The foundation of the economy is the banking system. As the public’s trust and confidence are essential to the banking industry, the entire financial sector would crumble if the public lacked trust and confidence in the banking sector. Hence, the primary duty of banking industry is to uphold and preserve public confidence (Nath et al., 2020). However, it is found that all the listed banks of Bangladesh are not performing well. The result showed that there is high likelihood that the mentioned banks may fail. The overall Z-scores of 34 banks are below the standard which is an indication of a strong potential for failure within a short time. All the banks should act immediately to allay concerns about their ability to continue operating. This study provides a detailed picture of the financial performance of Bangladeshi listed banks. The findings show that operating effectiveness is gradually declining as a result of an excessive amount of non- performing loans. According to Mostofa et al. (2016) loans are a bank’s asset, but when they are written off as bad loans, it negatively impacts the bank’s financial performance. Both financial trouble and insolvency could result from these actions. As a result, the management of these institutions needs to demonstrate managerial effectiveness while being more cautious with loan issuance. The study predicts only the financial health and bankruptcy of the listed banks in Dhaka stock exchange. However, the study does not provide any indication of how banks that are placed in the financial distress zone, would be able to overcome the financial instability. Hence, further research could be conducted on why these banks will keep on suffering from financial instability and which factors could help them overcome bankruptcy in the future. References Abdullah, M. (2015). 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Uddin, M. M., & Kaium, M. A. (2015). Financial health soundness measurement of private commercial banks in Bangladesh: An observation of selected banks. The Journal of Nepalese Business Studies, 9(1), 20–36. https://cpd.org.bd/ Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 46 © 2025 by the authors; licensee Asian Online Journal Publishing Group Appendix Appendix 1. Data analysis of 36 listed banks of Bangladesh using Z score indicator and value. Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. AB bank 2023 0.015 0.036 0.041 0.022 0.110 0.146 2022 0.008 0.041 0.046 0.023 0.120 2021 0.159 0.047 0.032 0.031 0.270 2020 0.005 0.056 0.023 0.028 0.110 2019 0.008 0.061 0.025 0.018 0.110 2018 0.036 0.070 0.015 0.032 0.150 Data analysis of AB bank limited Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Al-Arafah Islami bank 2023 -3.701 0.102 0.643 0.050 -2.910 -0.293 2022 0.061 0.011 0.066 0.052 0.190 2021 1.169 0.013 0.070 0.068 1.320 2020 -1.423 0.013 0.077 0.063 -1.270 2019 0.300 0.013 0.139 0.054 0.510 2018 0.189 0.018 0.125 0.071 0.400 Data analysis of Al-Arafah Islami bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Bank Asia 2023 0.074 0.023 0.068 0.055 0.220 0.374 2022 0.242 0.020 0.095 0.057 0.410 2021 0.300 0.020 0.069 0.064 0.450 2020 0.244 0.015 0.065 0.058 0.380 2019 0.196 0.016 0.075 0.067 0.350 2018 0.227 0.018 0.101 0.072 0.420 Data analysis of bank Asia Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. BRAC bank 2023 0.795 0.129 0.103 0.081 1.110 0.922 2022 0.508 0.145 0.095 0.105 0.850 2021 0.806 0.163 0.088 0.179 1.240 2020 0.260 0.117 0.093 0.153 0.620 2019 0.354 0.120 0.121 0.201 0.800 2018 0.376 0.121 0.162 0.259 0.920 Data analysis of BRAC bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. City bank 2023 -0.567 0.071 0.132 0.052 -0.310 -0.170 2022 -0.449 0.052 0.122 0.057 -0.220 2021 -0.340 0.063 0.151 0.078 -0.050 2020 -0.459 0.049 0.121 0.074 -0.220 2019 -0.605 0.027 0.162 0.068 -0.350 2018 -0.145 0.021 0.144 0.102 0.120 Data analysis of City bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Dhaka bank 2023 -1.383 0.019 0.072 0.037 -1.250 -1.000 2022 -1.315 0.023 0.076 0.040 -1.180 2021 -1.252 0.022 0.085 0.044 -1.100 2020 -1.231 0.023 0.070 0.040 -1.100 2019 -0.957 0.018 0.145 0.040 -0.750 2018 -0.846 0.017 0.163 0.047 -0.620 Data analysis of Dhaka bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Dutch-Bangla bank 2023 0.067 0.158 0.130 0.085 0.440 0.574 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 47 © 2025 by the authors; licensee Asian Online Journal Publishing Group 2022 0.458 0.132 0.107 0.089 0.790 2021 0.236 0.118 0.106 0.120 0.580 2020 0.100 0.102 0.137 0.085 0.430 2019 0.307 0.088 0.128 0.113 0.640 2018 0.262 0.087 0.131 0.094 0.570 Data analysis of Dutch-Bangla bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Eastern bank 2023 0.657 0.083 0.129 0.079 0.950 0.993 2022 0.651 0.078 0.121 0.084 0.930 2021 0.853 0.079 0.146 0.107 1.190 2020 0.646 0.086 0.133 0.099 0.960 2019 0.704 0.058 0.126 0.091 0.980 2018 0.671 0.057 0.111 0.106 0.950 Data analysis of Eastern bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Exim bank 2023 -0.772 0.012 0.062 0.029 -0.670 -0.565 2022 -0.789 0.012 0.079 0.031 -0.670 2021 -0.708 0.011 0.051 0.038 -0.610 2020 -0.640 0.016 0.074 0.039 -0.510 2019 -0.577 0.017 0.076 0.037 -0.450 2018 -0.633 0.020 0.077 0.049 -0.490 Data analysis of Exim bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. First security Islami bank 2023 0.188 0.005 0.062 0.017 0.270 0.276 2022 0.232 0.005 0.060 0.018 0.320 2021 0.203 0.005 0.073 0.026 0.310 2020 0.164 0.006 0.065 0.019 0.250 2019 0.057 0.007 0.061 0.208 0.330 2018 0.085 0.008 0.056 0.025 0.170 Data analysis of first security Islami bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Global Islami bank 2023 0.396 0.027 0.080 0.056 0.560 0.416 2022 0.349 0.033 0.088 0.063 0.530 2021 0.250 0.024 0.013 0.039 0.330 2020 0.249 0.018 0.100 0.039 0.410 2019 0.222 0.007 0.055 0.045 0.330 2018 0.205 0.017 0.071 0.046 0.340 Data analysis of global Islami bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. ICB Islamic bank limited 2023 -0.428 -6.118 -0.353 0.160 -6.740 -6.692 2022 -3.113 -6.106 -0.161 0.166 -9.210 2021 0.804 -5.414 -0.228 0.154 -4.680 2020 -3.103 -5.455 -0.109 0.131 -8.540 2019 -0.192 -5.410 -0.249 0.093 -5.760 2018 0.112 -5.200 -0.284 0.150 -5.220 Data analysis of ICB Islamic bank limited Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. IFIC bank 2023 -0.676 0.050 0.054 0.044 -0.530 -0.156 2022 -0.616 0.046 0.071 0.049 -0.450 2021 -0.272 0.040 0.085 0.079 -0.070 2020 -0.096 0.038 0.042 0.078 0.060 2019 -0.118 0.050 0.096 0.052 0.080 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 48 © 2025 by the authors; licensee Asian Online Journal Publishing Group 2018 -0.206 0.049 0.069 0.058 -0.030 Data analysis of IFIC bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Islami bank Bangladesh 2023 -0.447 0.005 0.056 0.028 -0.360 0.279 2022 0.273 0.005 0.055 0.032 0.360 2021 0.204 0.005 0.048 0.037 0.290 2020 0.333 0.006 0.053 0.036 0.430 2019 0.315 0.007 0.085 0.044 0.450 2018 0.324 0.007 0.095 0.067 0.490 Data analysis of Islami bank Bangladesh Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Jamuna bank 2023 -0.980 0.026 0.094 0.062 -0.800 -0.208 2022 -0.428 0.036 0.078 0.064 -0.250 2021 -0.728 0.040 0.103 0.076 -0.510 2020 -0.415 0.031 0.121 0.068 -0.200 2019 -0.235 0.022 0.130 0.064 -0.020 2018 0.325 0.022 0.107 0.067 0.520 Data analysis of Jamuna bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Mercantile bank 2023 0.264 0.012 0.046 0.041 0.360 0.374 2022 0.250 0.014 0.056 0.043 0.360 2021 0.148 0.018 0.082 0.055 0.300 2020 0.295 0.015 0.057 0.042 0.410 2019 0.232 0.015 0.075 0.044 0.370 2018 0.264 0.013 0.098 0.065 0.440 Data analysis of mercantile bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Midland bank 2023 -0.807 0.034 0.103 0.130 -0.540 0.420 2022 0.661 0.013 0.092 0.107 0.870 2021 0.005 0.014 0.110 0.119 0.250 2020 -0.204 0.024 0.102 0.140 0.060 2019 1.038 0.021 0.134 0.166 1.360 2018 0.138 0.030 0.162 0.188 0.520 Data analysis of midland bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Mutual trust bank 2023 0.130 0.041 0.065 0.046 0.280 0.296 2022 0.254 0.035 0.061 0.047 0.400 2021 0.163 0.037 0.078 0.058 0.340 2020 0.171 0.026 0.046 0.074 0.320 2019 0.013 0.029 0.081 0.076 0.200 2018 0.036 0.032 0.077 0.101 0.250 Data analysis of mutual trust bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. National bank 2023 -5.579 -0.152 -0.252 0.052 -5.930 -1.257 2022 -0.732 -0.052 -0.450 0.058 -1.180 2021 -0.022 0.044 0.011 0.045 0.080 2020 -0.079 0.013 0.083 0.045 0.060 2019 -0.167 0.019 0.105 0.054 0.010 2018 -0.786 0.021 0.115 0.065 -0.580 Data analysis of national bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. NCC bank 2023 -0.004 0.017 0.102 0.052 0.170 0.335 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 49 © 2025 by the authors; licensee Asian Online Journal Publishing Group 2022 0.180 0.016 0.116 0.055 0.370 2021 0.129 0.021 0.104 0.064 0.320 2020 0.393 0.020 0.105 0.059 0.580 2019 0.136 0.022 0.104 0.049 0.310 2018 0.091 0.017 0.097 0.066 0.270 Data analysis of NCC bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. NRB bank 2023 -1.442 0.039 0.109 0.096 -1.200 -0.941 2022 -1.385 0.042 0.063 0.099 -1.180 2021 -0.985 0.016 0.069 0.111 -0.790 2020 -0.747 0.028 0.116 0.100 -0.500 2019 -1.029 -0.001 -0.015 0.097 -0.950 2018 -1.254 0.034 0.089 0.104 -1.030 Data analysis of NRB bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. NRBC bank 2023 -1.902 0.039 0.083 0.071 -1.710 -0.886 2022 -1.735 0.036 0.110 0.078 -1.510 2021 -1.575 0.043 0.143 0.143 -1.250 2020 -1.279 0.037 0.121 0.167 -0.950 2019 -0.652 0.037 0.153 0.179 -0.280 2018 -0.038 0.041 0.157 0.227 0.390 Data analysis of NRBC bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. One bank 2023 -1.613 0.017 0.034 0.034 -1.530 -0.745 2022 -1.326 0.016 0.048 0.032 -1.230 2021 -1.142 0.013 0.035 0.030 -1.060 2020 -1.059 0.019 0.041 0.027 -0.970 2019 0.057 0.018 0.057 0.024 0.150 2018 0.066 0.016 0.060 0.026 0.170 Data analysis of one bank Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. The premier bank PLC 2023 -0.918 0.037 0.082 0.044 -0.750 -1.087 2022 -1.120 0.035 0.106 0.044 -0.930 2021 -1.213 0.037 0.103 0.049 -1.020 2020 -1.086 0.040 0.078 0.038 -0.930 2019 -1.561 0.047 0.116 0.050 -1.350 2018 -1.745 0.038 0.118 0.054 -1.530 Data analysis of the premier bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Prime bank PLC 2023 0.139 0.070 0.095 0.057 0.360 0.511 2022 -0.143 0.056 0.096 0.057 0.070 2021 0.366 0.043 0.099 0.071 0.580 2020 0.853 0.034 0.075 0.064 1.020 2019 0.562 0.020 0.090 0.073 0.740 2018 0.101 0.020 0.092 0.079 0.290 Data analysis of prime bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Pubali bank PLC 2023 0.368 0.095 0.100 0.037 0.600 -1.603 2022 0.337 0.085 0.084 0.042 0.550 2021 -12.218 0.076 0.061 0.049 -12.030 2020 0.242 0.065 0.070 0.049 0.430 2019 0.206 0.058 0.074 0.058 0.400 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 50 © 2025 by the authors; licensee Asian Online Journal Publishing Group 2018 0.236 0.034 0.098 0.071 0.440 Data analysis of Pubali bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Rupali bank PLC 2023 -1.542 0.003 0.012 0.020 -1.510 -1.319 2022 -1.478 0.003 0.007 0.018 -1.450 2021 -1.583 0.003 0.009 0.023 -1.550 2020 -1.418 0.004 0.005 0.020 -1.390 2019 -1.152 0.004 0.013 0.032 -1.100 2018 -0.967 0.004 0.011 0.032 -0.920 Data analysis of Rupali bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. SBAC bank PLC 2023 0.261 0.010 0.082 0.086 0.440 0.616 2022 0.233 0.012 0.084 0.095 0.420 2021 0.376 0.023 0.067 0.133 0.600 2020 0.460 0.025 0.104 0.145 0.730 2019 0.381 0.024 0.140 0.139 0.680 2018 0.477 0.027 0.156 0.154 0.810 Data Analysis of SBAC bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Shahjalal Islami bank PLC 2023 0.191 0.017 0.135 0.065 0.410 0.392 2022 0.179 0.017 0.144 0.067 0.410 2021 0.263 0.017 0.105 0.079 0.460 2020 0.181 0.013 0.083 0.085 0.360 2019 0.197 0.012 0.097 0.091 0.400 2018 0.119 0.012 0.081 0.106 0.320 Data analysis of Shahjalal Islami bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Social Islami Bank PLC 2023 0.082 0.011 0.056 0.029 0.180 0.201 2022 0.104 0.012 0.060 0.032 0.210 2021 0.081 0.011 0.049 0.039 0.180 2020 0.091 0.009 0.054 0.035 0.190 2019 0.100 0.010 0.061 0.039 0.210 2018 0.104 0.010 0.085 0.045 0.240 Data analysis of social Islami bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Southeast bank PLC 2023 -0.205 0.011 0.046 0.038 -0.110 -0.068 2022 -0.504 0.009 0.052 0.039 -0.400 2021 -0.169 0.011 0.044 0.042 -0.070 2020 0.281 0.009 0.046 0.036 0.370 2019 -0.153 0.027 0.070 0.042 -0.010 2018 -0.333 0.025 0.082 0.049 -0.180 Data analysis of Southeast bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Standard bank PLC 2023 0.188 0.007 0.057 0.042 0.290 0.445 2022 0.345 0.007 0.044 0.045 0.440 2021 0.396 0.010 0.041 0.055 0.500 2020 0.462 0.010 0.066 0.043 0.580 2019 0.121 0.015 0.086 0.044 0.270 2018 0.417 0.015 0.073 0.078 0.580 Data analysis of standard bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Trust bank limited 2023 -2.228 0.033 0.092 0.066 -2.040 -1.700 Asian Journal of Economics and Empirical Research, 2025, 12(1): 40-51 51 © 2025 by the authors; licensee Asian Online Journal Publishing Group 2022 -2.033 0.027 0.109 0.071 -1.830 2021 -1.776 0.032 0.092 0.071 -1.580 2020 -2.128 0.030 0.083 0.067 -1.950 2019 -1.692 0.028 0.107 0.063 -1.490 2018 -1.519 0.025 0.106 0.077 -1.310 Data analysis of trust bank limited Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. United commercial bank PLC 2023 0.257 0.030 0.049 0.029 0.370 0.426 2022 0.069 0.031 0.059 0.032 0.190 2021 0.153 0.034 0.062 0.039 0.290 2020 0.382 0.038 0.071 0.039 0.530 2019 0.399 0.037 0.077 0.039 0.550 2018 0.457 0.034 0.085 0.052 0.630 Data analysis of united commercial bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Union bank PLC 2023 1.161 0.024 0.074 0.034 1.290 1.303 2022 1.225 0.027 0.076 0.037 1.360 2021 1.037 0.025 0.062 0.025 1.150 2020 1.250 0.023 0.071 0.030 1.370 2019 1.527 0.027 0.055 0.035 1.640 2018 0.846 0.027 0.079 0.044 1.000 Data analysis of union bank PLC Bank name Year 6.56 X1 3.26 X2 6.72 X3 1.05 X4 Z Avg. Uttara bank PLC 2023 1.596 0.035 0.153 0.071 1.860 1.564 2022 1.284 0.032 0.140 0.070 1.520 2021 1.266 0.027 0.108 0.068 1.470 2020 1.608 0.023 0.113 0.062 1.810 2019 1.021 0.025 0.130 0.066 1.240 2018 1.287 0.030 0.104 0.068 1.490 Data analysis of Uttara Bank PLC Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. Any queries should be directed to the corresponding author of the article.