Are Indonesian construction companies financially distressed? A prediction using artificial neural networks

Abstract

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.

Analisis Financial Distress pada Perusahaan Konstruksi selama PSBB-PPKM era COVID-19 di Indonesia

“…Titik Kristanti et al (2023) menyatakan bahwa memprediksi financial distress telah menjadi topik yang dicari dalam perekonomian sejak lama karena mampu menggambarkan kondisi kesehatan keuangan perusahaan. Prediksi financial distress memiliki dampak signifikan terhadap setiap keputusan yang diambil oleh para stakeholder (Mishraz et al, 2021).…”

Predicting Financial Distress in the BIST Industrials Index: Evaluating Traditional Models and Clustering Techniques

“…This indicates that the new model provides earlier warning signals compared to the others. Kristanti et al (2023) predicted financial distress using artificial neural networks on data from 17 construction firms listed on the Indonesian Stock Exchange. The analysis revealed financial distress in 6 firms and stability in 11 firms.…”

The Effect Of Financial Distress And Liquidity On Audit Report Lag

“…In other words, financial distress is a condition where a company experiences financial difficulties in fulfilling its obligations. Reveals the characteristics of companies experiencing financial distress (Kristanti et al, 2023): drastic changes in the composition of assets and liabilities on the balance sheet, negative cash flow, and high relative value between debt and assets.…”