近年來受到中國鋼鐵業生產技術漸趨成熟及削價增加出口之影響，台灣鋼鐵業之技術或價格競爭優勢逐漸降低。此外，由於過度生產致鋼價下跌，使鋼鐵市場低迷。而全球經濟成長趨緩，亦不利產品售價提升。因此，台灣鋼鐵業之獲利下降及經營風險增加，使銀行對鋼鐵業授信風險提高。

本研究依據鋼鐵業行業特性選取財務結構、償債能力、經營能力、獲利能力及現金流量等構面之財務比率，建構鋼鐵業財務危機預警模式，以辨別財務危機高的公司。此外，本研究考慮景氣循環對鋼鐵業之影響，分別以2010年(景氣佳)及2014年(景氣谷底)之財務危機公司為分析對象，進行實證研究。實證結果發現，在財務危機公司發生財務危機前三年，其流動比率、速動比率、利息保障倍數、稅前純益率及總資產週轉率等多項財務指標顯著低於正常公司。無論景氣繁榮或低迷，財務危機模式預測正確率在財務危機前一年最高，雖在財務危機前兩年及前三年預測正確率較低，但整體預測準確率均在75%。本研究亦以作者任職銀行所承做之個案驗證財務危機模式，其結果顯示模式具區別力。

In recent years, Taiwan’s iron and steel industry has gradually lost its competitive advantage in production technology and prices because of the significant growth of technology and price-cutting strategy in China’s steel industry. In addition, the steel market has been declining over the past few years as a result of the large oversupply of steel that pressured prices downwards. The global recession that hit the commodities sectors hard also contributed to the shrinking growth in steel industry. Thus, the profits of the steel industry in Taiwan decrease and operating risk increases. The credit risk increases when banks make loan to companies in steel industry.

The objective of this study is to construct a financial distress prediction model which includes financial ratios of financial structure, solvency, operating efficiency, profitability and cash flow based on the characteristics of steel industry. Furthermore, the study examines how the economic cycle affects financial distress prediction of steel industry by using year 2010 as the expansion year and 2014 as the recession year, respectively. The empirical results indicate that current ratio, quick ratio, interest coverage ratio, net income before tax, and total asset turnover rate for companies in financial crises are significantly lower than those of normal companies three years before financial distress. No matter how the economic cycle is, the prediction of the financial crisis model is the most accurate in one year before financial crisis occurs. The overall prediction rate for two years and three years before the financial crisis occurs is about 75%. This study uses one case company to verify the financial distress prediction model. The results indicate that the financial distress prediction model is useful for financial institutions to screen out the financial distress companies in steel industry.

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