財務危機預測信用評分法與選擇權評價法比較—以台灣建築業實證分析

Abstract

授信業務是銀行獲利的主要來源，不過授信業務有其信用風險，一旦授信戶違約，銀行不但不能獲利，連帶也會影響金融體系的穩定，因此如何管理授信信用風險係銀行業相當重要的課題，早期銀行業多以人工評估方式來管理授信信用風險，後來開始發展企業信用評等來取代。鑑於過去關於違約機率參考文獻大部份以台灣全部上市上櫃公司為實證樣本，但每一種產業的風險屬性不同，故本研究以單一產業—台灣的建築業進行實證析。 本研究針對信用評分法與選擇權評價法在預測台灣建築產業之財務危機上何者較有效的實證分析，實證結果發現，無論由群內分析法、迴歸分析或是檢定力曲線來看，代表信用評分法的Altman’s Z-Score 在預測能力上與代表選擇權評價法的Merton（1974）模型所計算出的違約距離比較預測台灣建設公司的違約機率有效性上，Merton（1974）模型比Altman’s Z-Score模型好一點，但實證資料顯示這兩種指標對於預測台灣建築產業違約率還不夠優秀。本研究期能提供營建業有效信用衡量指標，以作為資金供給者之參考，進而降低營建業財務危機之發生，鞏固營建業與金融體系財務之穩定性。

The reason that banks play an important role in Taiwan financial industry is because lending, from which banks made most portion of revenue,is the most important part of banking; however, there is one risk, named credit risk, highly related to bank loans. Once bank’s clients turned to default, the credit risk influences not only banks’ profit but also the stability of whole Taiwan financial environment. As a consequence, how to manage the credit risk of lending business is one of the main topics of bank industry in Taiwan.In early age, banks took advantage of human judgments to manage credit risks of business, and later on they started to develop corporation credit evaluation methods, such as the internal rating model, in order to fully or partially replace human judgments. Due to few researches on . credit measures for managing credit risks, The purpose of this paper is to develop a dynamic prediction model for financial distress in construction firms using Data Mining.

This research compares Altman’s Z-Score with the distance to default calculated from Merton （1974）on their abilities in predicting corporate financial distress, we investigate which measure contains more information about financial distress. The empirical results of the intra-cohort analysis, logit regressions, and power curves all reach the same conclusion that Merton （1974）significantly outperforms distance to default. The empirical results of Altman’s Z-Score and Merton （1974）their prediction effectiveness is not good enough.This research expects to provide construction firm managers and creditors an effective index for evaluating the credit risk a construction firm. Results show that the proposed model has higher accuracy and stability for distress prediction and can provide a more effective quantitative framework for evaluating the financial standing of a construction firm.