銀行為何倒閉？從競爭優勢及銀行預警系統建置企業競爭優勢－在全球金融危機前後下的金融產業為例

Abstract

全球金融危機，銀行業是導火線也是危機下慘烈的受傷產業，許多百年老店追起了息號燈，尤其雷曼兄弟的倒閉更是震驚全球，但也有許多銀行趁勝追擊，持續成長，推斷全球金融危機前後銀行業的資源構型在這中間扮演了重要角色，而此論文希望能從這出發並揭露其中的緣由。 此篇研究主要根據兩大理論，永續競爭優勢與CAMEL評等，筆者採用貝式機率和信用模型來推斷與預測失敗/非失敗和競爭優勢/弱勢群體的機率，此外，試圖能找到一個框架來解釋結果的根本原因。在統計分析中，本研究採用因素分析和區別分析。其中一個方法是為了找到銀行業的資源構型，另外一個的目的是在銀行倒閉前兩年根據資源構型預測倒閉機率和分類銀行的策略群集，最後根據策略管理理論解釋競爭策略軸。 大多數先前研究著重在財務指標和模型的有效性，適用於預測銀行破產機率的預警系統。然而，在全球金融危機中，仍然有銀行許多在相關法令監控下倒閉，顯而易見地，當銀行面臨著更加複雜和動態競爭的環境中下競爭，僅監控風險是不夠的。過往也有一些研究整合財務指標以策略的角度和策略理論來解釋產業的永續競爭優勢。而此篇研究的重點不僅是預測指標或模型，更重要是加入策略的觀點角度探究背後的因素。 整體而言，研究探究銀行資源構型的群體，並成功預測銀行失敗倒閉的機率，和美國銀行是否具競爭優勢在全球金融危機前後的資源構型，從貝式分析、預警系統和競爭優勢軸觀點切入。從長遠的角度來看，加強管理者應當對風險採取策略性及謹慎面對的態度，而不是僅遵守法令規定下的風險因應措施，才能驅動銀行業在動態競爭環境中永續成長。

With the global financial crisis, banking industry has suffered the most damage and had great impact to the world economic. Some banks went bankruptcy in the global financial crisis; while some banks still grow or remain stable, we could infer that banking industry configuration really matters and the research mainly to disclosure underlying reasons. The research is based on two theory sustainable competitive advantage and CAMEL –related indicators (early warning system) in banking industry. The author adopt Bayesian and failure credit model to infer and predict failure/non-failure and competitive advantage/disadvantage groups, furthermore, trying to find a function or framework to interpret the underlying reasons of results. Most prior banking prediction researches are focus on identifying effective indicators and models that can be applied to develop an early warning system for individual bank failure. However, during global financial crisis, there is still a large amount of bankrupted banks, we can easily see that detecting risk is not enough to facing the even more complicated and dynamic competitive surroundings. In strategy point of view, there are some research integrate the financial indicators and strategy theory to interpret sustainable competitive advantage in the industry. In conclusion, the research mainly find banking industry configurations and successfully to predict the fail and competitive advantage dimension after global financial crisis of banks in U.S. As a matter of fact, also try to advance early warning system and Powell axis based on Bayesian analysis to take global financial crisis as a reference point to see underlying reasons whether banks to grow or fail. From a long term perspective, to strengthen the managers should take a risk seriously– instead of just complying with rules is might be underlying reasons what drives a business to sustainable growth in dynamic business banking industry.