The Influence of Financial Ratios and Macroeconomic Indicators in Predicting Financial Distress (Empirical Study in the Consumer Goods Sector Companies)
Abstract
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.
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Agrawal, K., & Maheshwari, Y. (2014). Default risk modelling using macroeconomic variables. Journal
of Indian Business Research, 6(4), 270–285. doi.org/10.1108/JIBR-04-2014-0024
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.
doi.org/10.11113/jt.v64.1181
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.
doi.org/10.1016/j.jbankfin.2008.08.006
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. https://doi.org/10.1177/0149206310388419
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. doi.org/10.1016/j.jretconser.2016.10.002
Fawzi, N. S., Kamaluddin, A., & Sanusi, Z. M. (2015). Monitoring Distressed Companies through Cash
Flow Analysis. Procedia Economics and Finance, 28(December), 136–144.
doi.org/10.1016/s2212-5671(15)01092-8
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. doi.org/10.1016/j.iref.2011.05.004
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.
doi.org/10.4156/jdcta.vol5.issue6.44
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. doi.org/10.14414/tiar.v7i2.1383
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.
doi.org/10.1108/03074351111134745
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.
doi:10.24843/EJMUNUD.2018.v07.i11.p11
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. doi.org/10.1142/S0219091509001691
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.
doi.org/10.1080/23322039.2018.1545739
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).
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