本文利用統計科學方法Logistic迴歸，建構非科技產業之財務預警模型(Financial Early-Warning Models)，期望在公司尚未發生財務危機前，預測公司可能發生財務危機之機率，達事前預警與風險管理之效。實證分析上，利用財務與非財務資訊變數作為解釋變數，以因素分析法萃取主成分因素，進一步以Logistic廻歸法篩選影響公司績效(Corporate Performance)之顯著變數，作為非科技產業財務預警之用，檢視加入公司治理變數對財務預測能力的影響。實證結果發現，財務資訊影響公司績效主要包括財務結構、償債能力和經營能力指標；加入公司治理變數後預測準確率提高，此項結果值得作為投資人投資決策之參考。降低負債比率、現金再投資比率、財務槓桿指數，提高利息保障倍數及避免更換會計師(CPAs)，保障債權人利益，將增強非科技業之經營、獲利與償債能力、外部監控(Outside Monitoring)與公司治理能力(Corporate Governance Power)；提高應收帳款週轉率與現金流量允當比率可提升非科技產業之經營能力與公司價值。因此，本文之實證價值與管理意涵為投資決策、公司治理、財務風險管理與財務預測。

This paper primarily used statistical methods to establish financial early-warning models that made it be possible to predict in advance the probability of some not science and technology companies experiencing financial distress. This empirical analysis is the first study that attempts to use the financial and non-financial ratios, and this study used Logistic regressions model to make a comparison with the financial ratios and the initiation of corporate governance variables. Findings, upon examining the predictor variables for corporate financial distress at one, two, and three years prior to distress, we found that the achievement result of the conventional company had financial structure, solvency, and operating performance are the principal ratio variables. To increase the degrees of predictive accuracy, we employed the corporate governance and investment decisions variables, including the non-financial ratios, the debt outside appropriately and cash flow reinvestment ratio, switched of CPAs, outside monitoring and corporate governance power, receivables turnover and cash flow adequacy ratio. Therefore, this paper may be useful for researchers and practitioners who are focusing on investment decisions, corporate governance, financial risk management and financial forecasting implementation.