Multiperiod Logit on Survival Analysis of Financial Distress in Manufacturing Company

 Abstract views: 66


  • Wilda Yulia Rusyida UIN KH Abdurrahman Wahid Pekalongan
  • Anas Yoga Nugroho UIN KH Abdurrahman Wahid Pekalongan



Multiperiod Logit, Survival Analysis, Financial Distrees, Manufacturing Company


The company is required to be able to maintain its survival so that the company's goals can be achieved properly. Financial distress is one of the factors that causes the company to be unable to maintain its viability so that the company's goals are not achieved. The factors that cause the company to be in a state of distress are internal and external factors. This is descriptive quantitative research which used 16 financial ratios, IHSG and BI rate. This research method is a quantitative method using time series data with a multiperiod logit model. Determination of the sample using purposive sampling so that there are 79 samples used in this study. Based on the results of the description of the Kaplan Meier curve, log rank test, multiperiod logit model with variable selection, it means that companies survive and financial distress have prominent differences in profitability ratios and market measure ratios. Meanwhile, based on the results of the partial test 4 out of 5 financial ratios, the results of the selection of variables have a significant effect on financial distress. The five best companies to invest in with a minimum hazard opportunity value are companies with issuer codes SKBM, IGAR, PBRX, PSDN and UNIC.


Download data is not yet available.


H. D. Platt and M. B. Platt, “Development of a class of stable predictive variables: the case of bankruptcy prediction,” J. Bus. Financ. Account., vol. 17, no. 1, pp. 31–51, 1990.

L. S. Almilia and K. Kristijadi, “Analisis rasio keuangan untuk memprediksi kondisi financial distress perusahaan manufaktur yang terdaftar di bursa efek Jakarta,” J. Akunt. dan Audit. Indones., vol. 7, no. 2, 2003.

T. Shumway, “Forecasting bankruptcy more accurately: A simple hazard model,” J. Bus., vol. 74, no. 1, pp. 101–124, 2001.

R. A. Cole and Q. Wu, “Predicting bank failures using a simple dynamic hazard model,” in 22nd Australasian Finance and Banking Conference, 2009, pp. 16–18.

F. T. Kristanti, S. Rahayu, and A. N. Huda, “The determinant of financial distress on Indonesian family firm,” Procedia-Social Behav. Sci., vol. 219, pp. 440–447, 2016.

F. T. Kristanti, N. Effendi, A. Herwany, and E. Febrian, “Does corporate governance affect the financial distress of Indonesian company? A survival analysis using Cox hazard model with time-dependent covariates,” Adv. Sci. Lett., vol. 22, no. 12, pp. 4326–4329, 2016.

F. Anderson, “Statistics by Example: Hands on Approach Using R and/or Excel.” CreateSpace Independent Publishing Platform, 2016.

G. Anuraga, A. Indrasetianingsih, and M. Athoillah, “Pelatihan Pengujian Hipotesis Statistika Dasar dengan Software R,” BUDIMAS J. Pengabdi. Masy., vol. 3, no. 2, 2021.

M. Athoillah, I. Irawan, M., and M. Imah, Elly, “Study Comparison of SVM-, K-NN- and Backpropagation-Based Classifier for Image Retrieval,” J. Ilmu Komput. dan Inf. (Journal Comput. Sci. Information), 2015.

D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, vol. 398. John Wiley & Sons, 2013.

D. R. Cox, Analysis of Survival Data. CRC Press, 2018.




How to Cite

Rusyida, W. Y. ., & Nugroho, A. Y. . (2023). Multiperiod Logit on Survival Analysis of Financial Distress in Manufacturing Company. J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 16(1), 354–370.