Are Indonesian construction companies financially distressed? A prediction using artificial neural networks
-
DOIhttp://dx.doi.org/10.21511/imfi.20(2).2023.04
-
Article InfoVolume 20 2023, Issue #2, pp. 41-52
- Cited by
- 844 Views
-
226 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Construction companies are very dependent on the projects carried out by a company. Therefore, measuring whether a company is distressed or non-distressed can be done by looking at the ratios derived from the components of the financial statements from both the balance sheet and the company’s profit and loss. This study offers a new method for measuring financial distress in companies with Artificial Neural Networks (ANN). The model provided comes from several financial ratios in 17 construction companies listed on the Indonesia Stock Exchange. The model is expected to produce the best model by showing the lowest prediction error rate. The results showed that the best ANN model has 25 inputs, 20 hidden layer neurons, and 1 best model output. The model obtained will be tested directly on the sample used; the results are that 6 construction companies in Indonesia have financial distress and 11 non-distress problems. This result proves that the best model obtained can predict the level of financial distress of companies with a small error rate to produce 6 companies identified as financially distressed. This result can be a warning for companies to increase revenue by adding new projects to get out of financial distress status. Traditional financial distress models such as Altman, Zmijewski, Springate, and Fulmer, which have become researchers’ guidelines for measuring financial distress, can be added to the ANN 25-20-1 model as a comparison to strengthen the research results.
- Keywords
-
JEL Classification (Paper profile tab)C15, C45, G32, G33, M41
-
References43
-
Tables7
-
Figures1
-
- Figure 1. ANN architecture
-
- Table 1. Operation of variables
- Table 2. Confusion matrix
- Table 3. Descriptive statistics of training data sample
- Table 4. Comparison of combined MSE
- Table 5. Training data output
- Table 6. Confusion matrix output training data
- Table 7. Results of financial distress
-
- Adisa, J. A., Ojo, S. O., Owolawi, P. A., & Pretorius, A. B. (2019). Financial Distress Prediction: Principle Component Analysis and Artificial Neural Networks. Proceedings – 2019 International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2019 (pp. 1-6).
- Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 22(4), 589-609.
- Alamsyah, A., Kristanti, N., & Kristanti, F. T. (2021). Early warning model for financial distress using Artificial Neural Network. IOP Conference Series: Materials Science and Engineering, 1098(5), 052103.
- Argenti, J. (1976). Corporate Collapse; the Causes and Symptoms. McGraw Hill.
- Beaver, W. H. (1996). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 71-11.
- Chen, H.-J., Huang, S. Y., & Lin, C.-S. (2009). Alternative Diagnosis of Corporate Bankruptcy: A Neuro Fuzzy Approach. Expert Systems with Applications, 36(4), 7710-7720.
- Chen, W. Sen, & Du, Y. K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2 PART 2), 4075-4086.
- Dam, K. W. (2006). Equity Markets, the Corporation, and Economic Development (John M. Olin Program in Law and Economics Working Paper, 280).
- Enumah, S. J., & Chang, D. C. (2021). Predictors of Financial Distress Among Private U.S. Hospitals. Journal of Surgical Research, 267(267), 251-259.
- Fasya, N. S., & Rikumahu, B. (2021). Analysis of Financial Distress Prediction Using Artificial Neural Network in Retail Companies Registered in Indonesia Stock Exchange. International Journal of Advanced Research in Economics and Finance, 3(1), 121-128.
- Frydman, H., Altman, E. I., & Kao, D.-L. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 40(1), 269-291.
- Hendel, I. (1996). Competition under Financial Distress. Journal of Industrial Economics, 44(3), 309-324.
- Hessels, Z. J., Sameeksha, A., & Jolanda, D. (2008). Entrepreneurship, Economic Development and Institutions. Small Business Economics.
- Hoque, M. M., & Rahman, M. T. (2020). Landfill Area Estimation Based On Solid Waste Collection Prediction Using Ann Model and Final Waste Disposal Options. Journal of Cleaner Production.
- Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting Corporate Financial Distress Based on Integration of Support Vector Machine and Logistic Regression. Expert Systems with Applications, 33(2), 434-440.
- Inam, F., Inam, A., Mian, M. A., Sheikh, A. A., & Awan, H. M. (2018). Forecasting bankruptcy for organizational sustainability in Pakistan: using artificial neural networks, logit regression, and discriminant analysis. Journal of Economic and Administrative Sciences, 35, 183-201.
- Irimiadieguez, A. I., Blancooliver, A., & Vazquezcueto, M. J. (2015). A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models. Procedia. Economics and Finance, 9-14.
- Jabeur, S. B. (2017). Bankruptcy Prediction Using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services, 197-202.
- Jiang, H., Ching, W., Yiu, K. F., & Qiu, Y. (2018). Stationary Mahalanobis Kernel Svm for Credit Risk Evaluation. Applied Soft Computing, 407-417.
- Klieštik, T., Valášková, K., Lazaroiu, G., Kováˇcová, M., & Vrbka, J. (2020). Remaining Financially Healthy and Competitive: The Role of Financial Predictors. Journal of Competitiveness, 12, 74-92.
- Kosmidou, K., Pasiouras, F., Zopounidis, C., & Doumpos, M. (2006). A multivariate analysis of the financial characteristics of foreign and domestic banks in the UK. Omega International Journal of Management Science, 34(2), 189-195.
- Koyuncugil, A. S., & Ozgulbas, N. (2012). Financial Early Warning System Model and Data Mining Application for Risk Detection. Expert Systems with Applications, 39(6), 6238-6253.
- Kristianto, H., & Rikumahu, B. (2019). A cross model telco industry financial distress prediction in Indonesia: Multiple discriminant analysis, logit and artificial neural network. 2019 7th International Conference on Information and Communication Technology, ICoICT 2019, 1-5.
- Laitinen, E. (1991). Financial Ratios and Different Failure Processes. Journal of Business Finance and Accounting, 18, 649-674.
- Lane, W. R., Looney, S. W., & Wansley, l W. (1986). An Application of the Cox Proportional Hazards Model to Bank Failure. Journal of Banking and Finance, 249-276.
- Levine, R. (1997). Financial Development and Economic Growth: views and Agenda. Econ. Lit., 35(2), 688-726.
- Li, Z. Y. (2015). Enterprise financial distress prediction based on backward propagation neural network: An empirical study on the Chinese listed equipment manufacturing enterprises. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 77(1), 27-38.
- Li, Z., Crook, J., Andreeva, G., & Tang, Y. (2021). Predicting the risk of financial distress using corporate governance measures. Pacific Basin Finance Journal, 68(February), 101334.
- Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16-18), 3507-3516.
- Marso, S., & El Merouani, M. (2020). Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm. Procedia Computer Science, 170(2019), 1134-1140.
- Mishraz, N., Ashok, S., & Tandon, D. (2021). Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models. Global Business Review, 1-19.
- Muparuri, L., & Gumbo, V. (2022). On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates. Sustainability Analytics and Modeling, 2(October 2021), 100006.
- Nik, P. A., MansourJusoh, Shaari, A. H., & Sarmdi, T. (2016). Predicting the Probability of Financial Crisis in Emerging Countries Using an Early Warning System: Artificial Neural Network. Journal of Economic Cooperation and Development, 1.
- Odom, M., & Sharda, R. (1990). A Neural Network for Bankruptcy Prediction. IEEE 1990, II, 163-168.
- Ohlson, J. A. (1980). Financial Ratios and Probabilistic Prediction of Bankcruptucy. Journal of Accounting Research, 18(1), 109-131.
- Ooghe, H., & Prijcker, S. De. (2008). Failure Processes and Causes of Company Bankruptcy: A Typology. Management Decision, 46, 223-242.
- Pranita, K. R., & Kristanti, F. T. (2020). Analisis Financial Distress Menggunakan Analisis Survival. Nominal: Barometer Riset Akuntansi and Manajemen, 9(2), 62-79.
- Salehi, M., Mousavi Shiri, M., & Bolandraftar Pasikhani, M. (2016). Predicting corporate financial distress using data mining techniques: An application in Tehran Stock Exchange. International Journal of Law and Management, 58(2), 216-230.
- Sun, J., Li, H., H., Q.-H., & He, K.-Y. (2013). Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56.
- Sun, W., & Xu, Y. (2016). Financial Security Evaluation of the Electric Power Industry in China Based on a Back Propagation Neural Network Optimized by Genetic Algorithm. Energy, 101, 366-379.
- Sun, X., & Lei, Y. (2021). Research on financial early warning of mining listed companies based on BP neural network model. Resources Policy, 73(4), 102223.
- Wu, D., Ma, X., & Olson, D. L. (2022). Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems, 159(May), 113814.
- Zhou, Z., Xiao, T., Chen, X., & Wang, C. (2016). A Carbon Risk Prediction Model for Chinese Heavy-Polluting Industrial Enterprises Based On Support Vector Machine. Chaos Solitons & Fractals, 304-315.