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Financial Distress Prediction Through Cash Flow Ratios Analysis

Author

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  • Amrizah Kamaluddin
  • Norhafizah Ishak
  • Nor Farizal Mohammed

Abstract

The purpose of this study to examine the relationship of cash flow ratios in predicting financial distress companies, with industrial and consumer product companies in Bursa Malaysia as the sample. The study on financial distress is critical as it can lead to bankruptcy, which may adversely affect the economy of the country. Therefore it is worth exploring any indicators that can identify the possibility of financial distress in the company. The tools enable to address the potential problems that can mitigate from distressed financial position. Most prior studies in Malaysia focus on traditional financial ratios, while this study exploits the strength of cash flow ratios. The liquidity ratio, solvency ratio, efficiency ratio and profitability ratio utilized in this study are derived from the statement of cash flows. The Altman Z-score is used to measure the level of the financial distress. The findings show mixed relationships between solvency ratio and financial distress and a negative significant relationship between profitability ratio and financial distress, whilst efficiency ratio has no relationship with the financial distress. These results suggest that cash flow ratios are reliable tools to predict financial distress for Malaysian context. The study is useful in giving insights to the stakeholders in their decision making.

Suggested Citation

  • Amrizah Kamaluddin & Norhafizah Ishak & Nor Farizal Mohammed, 2019. "Financial Distress Prediction Through Cash Flow Ratios Analysis," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(3), pages 63-76, May.
    • Handle: RePEc:jfr:ijfr11:v:10:y:2019:i:3:p:63-76
    DOI: 10.5430/ijfr.v10n3p63
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    2. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    3. Jiang-Chuan Huang & Hueh-Chen Lin & Daniel Huang, 2022. "The Effect of Operating Cash Flow on the Likelihood and Duration of Survival for Marginally Distressed Firms in Taiwan," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    4. Asyrofa Rahmi & Hung-Yuan Lu & Deron Liang & Dinda Novitasari & Chih-Fong Tsai, 2023. "Role of Comprehensive Income in Predicting Bankruptcy," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 689-720, August.
    5. Puji Handayati & Tomy Rizky Izzalqurny & Slamet Fauzan & Nurus Shobah, 2022. "The phenomenon of financial distress of manufacturing companies in Indonesia during the Covid-19 Pandemic," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(9), pages 166-173, December.
    6. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    7. Martínez Vargas, Agustín María & Edilson Hernández, Marcos & Velásquez Cerón, Omar, 2022. "Evaluación de las decisiones financieras operacionales que generan flujo de caja en las MIPYMES [Evaluation of Operational Financial Decisions that Generate Cash Flow in Msmes]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 34(1), pages 60-82, December.

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