The Influence of Financial Ratios and Macroeconomic Indicators in Predicting Financial Distress (Empirical Study in the Consumer Goods Sector Companies)

Mega Yuliastari, Najmudin Najmudin, Meutia Karunia Dewi


The purpose of this research is to analyze and find empirical evidence of the effect of financial ratios that
are proxied by Current asset turnover, Asset turnover, Days sales in receivables, Cash flow to total debt,
Total liabilities to total assets, and macroeconomic indicators that are proxied by inflation and BI interest
rates on financial distress. This study uses an associative causal approach and the data used in the
secondary data. The object used in this study is consumer goods sector companies listed on Indonesia
Stock Exchange during the period 2014-2018. The sample of this study was 36 companies. The data
analysis technique used is logistic regression. The research finding shows that current asset turnover,
asset turnover, and cash flow to total debt have an impact on financial distress. While the day's sales in
receivables, total liabilities to total assets, the sensitivity of inflation, and sensitivity of BI Rates have no
influence on financial distress. Therefore, company management needs to prioritize policies and be able
to use current assets, total assets, and total debt proportionately and control operational costs more
efficiently in order to increase the company's revenue and net profit, and then the company is able to pay
installments and interest costs from the debt.
Keywords: financial distress; financial ratios; macroeconomic indicators; sensitivity of inflation;
sensitivity of BI rates; logistic regression.

Full Text:



Agrawal, K., & Maheshwari, Y. (2014). Default risk modelling using macroeconomic variables. Journal

of Indian Business Research, 6(4), 270–285.

Alifiah, M. N. (2014). Prediction of financial distress companies in the trading and services sector in

Malaysia using macroeconomic variables. Procedia-Social and Behavioral Sciences, 129, 90-98.

doi: 10.1016/j.sbspro.2014.03.652

, Salamudin, N., & Ahmad, I. (2013). Prediction of financial distress companies in the consumer

products sector in Malaysia. Jurnal Teknologi (Sciences and Engineering), 64(1), 85–91.

Bauer, J., & Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction

approaches? A comprehensive test. Journal of Banking & Finance, 40, 432-442.

Bhunia, A., Islam, S., Khan, U., & Mukhuti, S. (2011). Prediction of Financial Distress -A Case Study of

Indian Companies. Asian Journal of Business Management, 3(3), 210–218.

Boediono. (1992). Ekonomi Moneter, Edisi 3. Yogyakarta: BPFE

Bonfim, D. (2009). Credit risk drivers: Evaluating the contribution of firm level information and of

macroeconomic dynamics. Journal of Banking and Finance, 33(2), 281–299.

Boentoro, D. O. (2015). Corporate failure prediction: a study of public listed companies in Indonesia

Stock Exchange (IDX) (Doctoral dissertation, universitas atma jaya yogyakarta).

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.

Suliyanto. (2011). Ekonometrika Terapan: Teori dan Aplikasi dengan SPSS. Yogyakarta: ANDI.

(2018) Metode penelitian bisnis: Untuk skripsi, Tesis, Dan Disertasi. Yogyakarta: ANDI.

Elloumi, F., & Gueyie, J. P. (2001). Financial Distress and Corporate Governance: a survival analysis.

Corporate Governance, 15-23.

Fallahpour, S., Lakvan, E. N., & Zadeh, M. H. (2017). Using an ensemble classifier based on sequential

floating forward selection for financial distress prediction problem. Journal of Retailing and

Consumer Services, 34(October 2016), 159–167.

Fawzi, N. S., Kamaluddin, A., & Sanusi, Z. M. (2015). Monitoring Distressed Companies through Cash

Flow Analysis. Procedia Economics and Finance, 28(December), 136–144.

Figlewski, S., Frydman, H., & Liang, W. (2012). Modeling the effect of macroeconomic factors on

corporate default and credit rating transitions. International Review of Economics and Finance,

(1), 87–105.

Gumanti, T. A. (2009). Teori Sinyal Dalam Manajemen Keuangan. Manajemen Dan Usahawan

Indonesia, (September), 1–29.

Hosmer, D. W., and S. Lemeshow., (2000). Applied Logistic Regression. Second Edition, John Willey &

Sons, New York.

Jiming, Li., & Weiwei, Du. (2011). An empirical study on the corporate financial distress prediction

based on logistic model: Evidence from China‘s manufacturing Industry. International Journal of

Digital Content Technology and Its Applications, 5(6), 368–379.

Khaliq, A., Altarturi, B. H. M., Thaker, H. M. T., Harun, M. Y., & Nahar, N. (2014). Identifying

Financial distress firms: a case study of Malaysia‘s government linked companies (GLC).

International Journal of Economics, Finance and Management, 3(3).

Kumalasari, R. D. (2014). The Effect of Fundamental Variables and Macro Variables on the Probability

of Companies to Suffer Financial Distress A Study on Textile Companies Registered in BEI,

(34), 275–285.

Marlin, Yulpa. (2017). Pengaruh Current Ratio, Debt To Total Assets Ratio Dan Total Assets Turn Over

Terhadap Kondisi Financial Distress (Studi Pada Perusahaan Sub Sektor Batu Bara Yang

Terdaftar Di BEI), eJournal Administrasi Bisnis, 2017, 5 (4): 855-866.

Moleong, L. C. (2018). Pengaruh Real Interest Rate dan Leverage Terhadap Financial Distress. MODUS

Vol. 30 (1): 71-86.

Oktarina, D. (2018). Macroeconomic Indicators and Corporate Financial Ratios in Predicting Financial

Distress. The Indonesian Accounting Review, 7(2), 219–230.

Ong, S. W., Choong Yap, V., & Khong, R. W. L. (2011). Corporate failure prediction: a study of public

listed companies in Malaysia. Managerial Finance, 37(6), 553–564.

Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: reflections on choice-based

sample bias. Journal of economics and finance, 26(2), 184-199.

Priyatnasari, S., & Hartono, U. (2019). Rasio keuangan, makroekonomi dan financial distress : studi pada

perusahaan perdagangan, jasa dan investasi di indonesia. Jurnal Ilmu Manajemen, 7, 1005–1016.

Rasminiati, N., & Artini, L. (2018). Prediksi Kondisi Keuangan Pada Perusahaan Sektor Pertambangan

Di Bursa Efek Indonesia. E-Jurnal Manajemen, 7(11), 6100 - 6128.


Roslan, N. H. B. (2014) Determinants of financial distress among manufacturing companies in Malaysia.

Doctoral dissertation, School of Business, Universiti Utara Malaysia.

Suriyani, N. K., & Sudiartha, G. M. (2018). Pengaruh tingkat suku bunga, inflasi dan nilai tukar terhadap

return saham di Bursa Efek Indonesia. E-Jurnal Manajemen Universitas Udayana, 7(6).

Tsai, B. H., Lee, C. F., & Sun, L. (2009). The impact of auditors‘ opinions, macroeconomic and industry

factors on financial distress prediction: An empirical investigation. Review of Pacific Basin

Financial Markets and Policies, 12(3), 417–454.

Uğurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress in an emerging market: the

case of Turkey. Cross Cultural Management: An International Journal.

Waqas, H., & Md-Rus, R. (2018). Predicting financial distress: Importance of accounting and firmspecific

market variables for Pakistan‘s listed firms. Cogent Economics and Finance, 6(1), 1–16.

Wulandari, T. (2017). Pengaruh Rasio Keuangan Terhadap Kondisi Financial Distress Perusahaan Textile

Dan Garment Yang Terdaftar Di Bursa Efek Indonesia. Jurnal Mutiara Akuntansi, 2(2), 18-32.

Yap, B. C. F., Munuswamy, S., & Mohamed, Z. (2012). Evaluating company failure in Malaysia using

financial ratios and logistic regression. Asian Journal of Finance & Accounting, 4(1), 330-344.

Zhou, Y., & Elhag, T. M. (2007). Apply logit analysis in bankruptcy prediction. In Proceedings of the 7th

WSEAS International Conference on Simulation, Modelling and Optimization (pp. 302-308).

World Scientific, Engineering Academy, and Society (WSEAS).


  • There are currently no refbacks.