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The 2011 floods’ impact on the Thai industrial estates’ financial stability: a ratio analysis with policy recommendations

Author

Listed:
  • Kanitsorn Terdpaopong

    (Rangsit University)

  • Robert C. Rickards

    (German Police University)

  • Penprapak Manapreechadeelert

    (Rajamangala University of Technology Suvarnabhumi)

Abstract

This paper employs ratio analysis to investigate the financial stability of companies located in seven of Thailand’s industrial estates following the 2011 floods. Those seven industrial estates contain 651 companies. Ranked by size, they are: (1) Rojana; (2) Navanakorn; (3) Hi-Tech; (4) Bang Pa-in; (5) Factory Land; (6) Saharattananakhon; and (7) Bangkadi Industrial Estates. All of them were hit particularly hard by flooding in 2011. After omitting 75 companies that failed to report their financial statements, 43 companies that closed down after the floods, and 19 companies that first registered businesses after 2011, 514 companies (78.96% of the total number of companies on the 7 industrial estates) were selected and included in this study. Its objective is to investigate the financial stability of those companies after the floods and to see how quickly they were able to recover from the disaster. The sample’s financial data were divided into two different periods: the flood year (2011) and post-flood years (2012–2015). After reviewing commonly used indicators, 12 financial ratios were selected to measure changes in the companies’ post-flood financial position. We tested the normality of the data and decided to employ a nonparametric independent test at 90%, 95%, and 99% confidence intervals. To our surprise, the key finding of this paper is that the leverage and, to a certain extent, the liquidity of the affected companies did not constitute issues for them. Their current ratios and working capital ratios looked fine statistically. However, due to the cessation of production during the flooding, they lacked deliverable inventories. As a result, these companies faced inventory and accounts receivable problems. The historic floods also affected the profitability of the companies, making 2011 the year with the markedly lowest profitability during the 2011–2015 period.

Suggested Citation

  • Kanitsorn Terdpaopong & Robert C. Rickards & Penprapak Manapreechadeelert, 2020. "The 2011 floods’ impact on the Thai industrial estates’ financial stability: a ratio analysis with policy recommendations," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 1991-2014, March.
  • Handle: RePEc:spr:endesu:v:22:y:2020:i:3:d:10.1007_s10668-018-0274-0
    DOI: 10.1007/s10668-018-0274-0
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    1. Kanagaretnam, Kiridaran & Zhang, Gaiyan & Zhang, Sanjian Bill, 2016. "CDS pricing and accounting disclosures: Evidence from U.S. bank holding corporations around the recent financial crisis," Journal of Financial Stability, Elsevier, vol. 22(C), pages 33-44.
    2. S. Pathak & M. M. Ahmad, 2018. "Role of government in flood disaster recovery for SMEs in Pathumthani province, Thailand," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(2), pages 957-966, September.
    3. Zhichao Zhang & Li Xie & Xiangyun Lu & Zhuang Zhang, 2016. "Determinants Of Financial Distress In Large Financial Institutions: Evidence From U.S. Bank Holding Companies," Contemporary Economic Policy, Western Economic Association International, vol. 34(2), pages 250-267, April.
    4. Myungsun Kim & William Kross, 2005. "The Ability of Earnings to Predict Future Operating Cash Flows Has Been Increasing—Not Decreasing," Journal of Accounting Research, Wiley Blackwell, vol. 43(5), pages 753-780, December.
    5. Kenneth Lorek & G. Willinger, 2009. "New evidence pertaining to the prediction of operating cash flows," Review of Quantitative Finance and Accounting, Springer, vol. 32(1), pages 1-15, January.
    6. Demerjian, Peter R. & Owens, Edward L., 2016. "Measuring the probability of financial covenant violation in private debt contracts," Journal of Accounting and Economics, Elsevier, vol. 61(2), pages 433-447.
    7. Laitinen, Erkki K. & Suvas, Arto, 2016. "Financial distress prediction in an international context: Moderating effects of Hofstede’s original cultural dimensions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 98-118.
    8. Almamy, Jeehan & Aston, John & Ngwa, Leonard N., 2016. "An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK," Journal of Corporate Finance, Elsevier, vol. 36(C), pages 278-285.
    9. M. Kondala Rao, 2016. "SPEL Analysis of Financial Statements of Selected Public Sector Steel Manufacturing Companies – India," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 7(3), pages 14-30, September.
    10. Loredana Cultrera & Xavier Brédart, 2016. "Bankruptcy prediction: the case of Belgian SMEs," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(1), pages 101-119, February.
    11. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    12. Ahn, Seoungpil & Denis, David J. & Denis, Diane K., 2006. "Leverage and investment in diversified firms," Journal of Financial Economics, Elsevier, vol. 79(2), pages 317-337, February.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    14. Khaldoun M. Al-Qaisi, 2013. "The Effect of the Financial Crisis on the Jordanian Industrial Sector," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 2(1), pages 43-47, January.
    15. Feng, Cheng-Min & Wang, Rong-Tsu, 2000. "Performance evaluation for airlines including the consideration of financial ratios," Journal of Air Transport Management, Elsevier, vol. 6(3), pages 133-142.
    16. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    17. Aivazian, Varouj A. & Ge, Ying & Qiu, Jiaping, 2005. "The impact of leverage on firm investment: Canadian evidence," Journal of Corporate Finance, Elsevier, vol. 11(1-2), pages 277-291, March.
    18. Danny Marks & Frank Thomalla, 2017. "Responses to the 2011 floods in Central Thailand: Perpetuating the vulnerability of small and medium enterprises?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(2), pages 1147-1165, June.
    19. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    20. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    21. Yuan, Yuan, 2006. "The state of competition of the Chinese banking industry," Journal of Asian Economics, Elsevier, vol. 17(3), pages 519-534, June.
    22. World Bank, 2012. "Thai Flood 2011," World Bank Publications - Reports 26862, The World Bank Group.
    23. Warusawitharana, Missaka, 2013. "The expected real return to equity," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1929-1946.
    24. E. Nur Ozkan-Gunay & Mehmed Ozkan, 2007. "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, Emerald Group Publishing, vol. 8(5), pages 465-480, November.
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    More about this item

    Keywords

    Financial stability; Floods; Industrial estates; Manufacturing sector; Ratio analysis; Small- and medium-sized enterprises; SMEs; Thailand;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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