Altman Z-Score Analysis in Predicting Financial Distress and Its Impact on Firm Value
DOI:
https://doi.org/10.32832/neraca.v21i2.24482Keywords:
Altman Z-Score, financial distress, firm value, Tobin's Q, transportation sectorAbstract
This study investigates the ability of the Altman Z-Score to predict financial distress and its impact on the value of the firm in transportation sector companies listed on the Indonesia Stock Exchange (IDX) during the 2022-2024 period. Using a quantitative verification approach with panel data regression analysis, 93 observations were analyzed. The dependent variable is firm value proxied by Tobin's Q, while the independent variable is financial distress measured through the Altman Z-Score (Z"-Score) for non-manufacturing firms. Control variables include liquidity, cash flow from operations (CFO), and debt-to-equity ratio (DER). Results show that Altman Z-Score has a significant positive effect on Tobin's Q (coefficient 0.061, p=0.038), indicating that healthier financial condition corresponds to higher firm value. Liquidity and CFO also positively and significantly affect firm value, while DER negatively and significantly affects it. Simultaneously, all variables significantly explain 23.7% of Tobin's Q variation (F=8.155, p=0.000). These findings support signaling theory and confirm that financial distress information serves as a meaningful signal to investors in evaluating firm value in Indonesia's emerging capital market.
References
Aidi, A., Setiawan, T. W., Junaidi, A., Budiwaluyo, A., & Agoestyowati, R. (2022). Analysis of financial health level using the Z-Score (Altman) method in transportation companies listed on the Indonesia Stock Exchange for the 2019-2021 period. Ilomata International Journal of Tax and Accounting, 3(4), 461-473. https://doi.org/10.52728/ijtc.v3i4.605
Akerlof, G. A. (1970). The market for lemons: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488-500. https://doi.org/10.2307/1879431
Altman, E. I. (2000). Predicting financial distress of companies: Revisiting the Z-Score and ZETA models. In Handbook of research methods and applications in empirical finance (pp. 1-26). Edward Elgar. https://doi.org/10.4337/9780857936097.00009
Altman, E. I. (2018). Applications of distress prediction models: What have we learned after 50 years from the Z-Score models? International Journal of Financial Engineering, 5(3), 1-25. https://doi.org/10.1142/S2424786318500084
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z-Score model. Journal of International Financial Management & Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053
Avramov, D., Cheng, S., & Metzker, L. (2021). Machine learning vs. economic restrictions: Evidence from stock return predictability. Journal of Financial Economics, 142(3), 117-141. https://doi.org/10.1016/j.jfineco.2021.07.004
Bauer, R., & Hann, D. (2019). Corporate environmental management and credit risk. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1660470
Berger, P. G., Li, F., & Wong, M. F. (2020). The impact of stakeholder orientation on corporate tax avoidance. Journal of Accounting and Economics, 70(1), 101316. https://doi.org/10.1016/j.jacceco.2020.101316
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468-519. https://doi.org/10.1007/s11142-020-09563-8
Bonsall, S. B., Holzman, E. R., & Miller, B. P. (2017). Managerial ability and credit risk assessment. Management Science, 63(5), 1425-1449. https://doi.org/10.1287/mnsc.2015.2403
Bursa Efek Indonesia. (2024). Indonesia stock exchange statistics 2023. BEI.
Button, K. (2022). Transport economics (4th ed.). Edward Elgar Publishing.
Chen, J., & Wang, Y. (2022). Financial distress prediction in emerging markets: The role of corporate governance. Journal of International Financial Markets, Institutions and Money, 78, 101567. https://doi.org/10.1016/j.intfin.2022.101567
Chen, L., Li, Y., & Zhang, J. (2023). Signal credibility and corporate financial distress: Evidence from social media. Journal of Corporate Finance, 78, 102345. https://doi.org/10.1016/j.jcorpfin.2022.102345
Chung, K. H., & Pruitt, S. W. (2019). A simple approximation of Tobin's Q. Financial Management, 23(3), 70-74. https://doi.org/10.2307/3665623
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39-67. https://doi.org/10.1177/0149206310388419
Dewi, M., Foanto, G. N., & Christiawan, Y. J. (2021). Profitability, liquidity, and firm value: Does financial distress have a mediating effect? In Proceedings of the 6th International Conference on Tourism, Economics, Accounting, Management, and Social Science (TEAMS 2021) (pp. 437-445). Atlantis Press. https://doi.org/10.2991/aebmr.k.211124.062
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
Fama, E. F. (1991). Efficient capital markets II. The Journal of Finance, 46(5), 1575-1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
Gharaibeh, O. K., & Khaled, A. A. (2020). Determinants of firm value as measured by Tobin's Q: Empirical evidence from the Saudi Stock Exchange (TADAWUL). International Journal of Finance and Accounting, 9(4), 1-13.
Graham, J. R., Hazarika, S., & Narasimhan, K. (2022). Financial distress in the transportation industry: The role of leverage and operating efficiency. Journal of Transport Economics and Policy, 56(2), 115-135.
Griffin, J. M., & Lemmon, M. L. (2002). Book-to-market equity, distress risk, and stock returns. The Journal of Finance, 57(5), 2317-2336. https://doi.org/10.1111/1540-6261.00497
Gujarati, D. N., & Porter, D. C. (2023). Basic econometrics (6th ed.). McGraw-Hill Education.
Habib, A., Costa, M. D., Huang, H. J., Bhuiyan, M. B. U., & Sun, L. (2020). Determinants and consequences of financial distress: Review of the empirical literature. Accounting & Finance, 60(S1), 1023-1075.
Huang, H., & Wang, Q. (2023). Financial distress and firm value in the transportation sector: Evidence from emerging markets. Journal of Transport Economics, 45(1), 88-107.
Kristanti, F. T., & Herwany, A. (2021). Corporate bankruptcy prediction in Indonesia: An application of the Altman Z-Score model. International Journal of Finance & Economics, 26(4), 5638-5651. https://doi.org/10.1002/ijfe.2083
Maringgih, D., & Oktaviani, R. M. (2025). Financial distress prediction in emerging markets using machine learning for financial sustainability. SN Business & Economics, 6, 1296. https://doi.org/10.1007/s43621-025-02199-1
Migdad, M., Sayekti, Y., & Abdullah, N. A. P. (2025). Effectiveness analysis for financial distress prediction in the transportation sector during the pandemic. Journal of Economics, Finance and Management Studies, 8(6), 3580-3590. https://doi.org/10.47191/jefms/v8-i6-17
Morris, R. D. (1987). Signalling, agency theory and accounting policy choice. Accounting and Business Research, 18(69), 47-56. https://doi.org/10.1080/00014788.1987.9729347
Otoritas Jasa Keuangan. (2024). Statistik pasar modal Indonesia 2023. OJK.
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355-374. https://doi.org/10.2307/1882010
Wooldridge, J. M. (2023). Econometric analysis of cross section and panel data (3rd ed.). MIT Press.
World Bank. (2023). Global economic prospects: Navigating the post-pandemic economy. World Bank Publications.
