台灣金融預警制度之研究: 以上市(櫃)本國金融機構之市場與會計資訊應用為例

摘要：
由於政府對金融機構設立管制之鬆綁，台灣近年來金融機構家數已有明顯過多 (over-banking) 的現象。各金融機構為維持其營運，無不殺價競爭，導致市場利差逐年下降；加以近年來在總體經濟因素：金融風暴及景氣下挫，資產品質迭遭侵蝕；加上公司治理因素：金融機構負責人淘空、舞弊等情事之發生，對其經營無疑是雪上加霜。
金融機構的資金來源，主要是吸收社會大眾存款，其經營良窳自為政府當局及存款戶關切，如何未雨稠繆，金融監督管理委員會亟需建立一有效、客觀且超然的銀行經營績效評鑑及預警系統，適時監控金融機構經營與管理，在問題徵兆初現時即介入，以收防微杜漸之效。本研究以上市(櫃)本國商業銀行與金融控股公司為例，應用可以反應金融機構資產市場價值的市場資訊，並結合會計資訊的變數，建立銀行經營績效評鑑及預警模型，俾能及早發現並導正其營運缺失，以健全金融機構之發展。
本研究使用一般會計資訊所建立模型進行比較，討論Probit模型對銀行與金控公司經營績效辨識及預測能力的差異性。本研究以上市(櫃)24家商業銀行及金融控股公司為研究樣本，根據金融機構一般經營原則及國內外相關研究，整理出 15項財務變數作為研究變數，包括資本適足性、資產品質、守法性、經營策略與穩定性、獲利能力及流動性等六項指標。利用各金融機構相關比率之大小、排序、變動趨勢等，所產生經營績效之成果，同時運用加權效率性之觀念，選用Probit模型來建立本國金融機構之預警模型，提供政府金管單位、銀行管理人員及社會大眾參考。
本研究之結論有以下幾點：
一、母公司或原始公司為新銀行的金融機構，被歸類為經營績效較差的群組；而舊銀行或以保險業為母公司的金融機構，其經營績效較佳。
二、本研究所建立的Probit模型，符合各金融機構之財務變數計有X9 (淨值報酬率)及X15 (放款/存款比率)，另外尚有X5 (利率敏感性缺口/淨值比率)。
三、以原始樣本所建立的Probit模型，以預測前一期(前半年)之金融機構資料所建立方法的加權效率性較高，其正確區別率達86.47%，型I誤差為17.45%，型II誤差為11.87%。
四、建立金融機構其他的預警模型因素，由於國內外金融環境遽變，尚有近年來重大影響的總體經濟、公司治理(含人為因素)等變數可以納入考量。

The Study of Early Warning System in Taiwan’s Public Listing Financial Institutions-Examples for the Applications of Market and Accounting Information

Abstract：
Due to the deregulation of establishing financial institutions by the government, the numbers of financial institutions is “over-banking” in the recent years. Each financial institution attempts to maintain its operation to kill the price for the competition to yield the spreads to decline year by year. Moreover, the macroeconomic factors-financial storms and economic recession to spoil out the asset quality, and the corporate governance factors-the financial institution superintendent’s empty and fraud to make its operation much worse without doubts.
The capital source of financial institution is from person’s deposits in the society. Its management performance is concerned by the government agency and the deposit persons. How does it play the percentages? The Financial Supervisory Commission (F.S.C), Executive Yuan will need to establish an effective, objective, and detached banking management performance evaluations and early warning system to supervise and control the financial institution’s operation and management in the right time. As the problem’s indication appears initially, the F.S.C will interfere to HUtakeUH HUeveryUH HUprecautionUH HUatUH HUbeginningUH. The study using Taiwan’s public offering commercial banks and financial holding companies as the example apply market information to reflect the financial institutions’ assets market value, and integrate the variables of accounting information to build up a banking management performance evaluation and early warning system in order to find and guild its shortcomings for the operation in time to better the financial institutions’ development.
The study using general accounting information to build the model for the comparisons, it discusses by Probit model for the differences of banks and F.H.Cs’ management performance signaling and predicting capacities. The study using Taiwan’s 24 public offering commercial banks and financial holding companies as the research example, according to the financial institution’s general management principles to collate 15 items of financial variables as the research variables, which includes of 6 items of index, such as capital adequacy, asset quality, regulations compliance, strategies and stability, earnings, and liquidity. It utilizes of each financial institution’s related ratios’ percentages, orderings, and the change trend to generate the results of management performance, and applies the concept of weighted efficiency in Probit models to construct Taiwan’s financial institution’s early warning model to provide the F.S.C, bank’s managers, and people for the reference.
The conclusions of the study as shown in the below:
1. The financial institution’s parents (or original) company is new banks to be classified as worse performance groups. However, old banks or insurance company as parents (or original) company’s financial institutions, whose financial performance are better.
2. The study constructed Probit model fits the financial institution’s variables are X9 (return on equity) and X15 (loans/deposits ratio), and X5 (interest rate sensitivity gap/net worth ratio).
3. Using the original samples constructed Probit model, the weighted efficiency in the pre-1st period’s (pre-an half year) financial institution’s data is higher with the percentage of correctly classified is 86.47%, type I error is 17.45%, and type II error is 11.87%。
4. The other factors for the financial institution’s to construct early warning model, it may take into account the tremendous influential macroeconomic, corporate governance (consisting man-made factors) variables in the recent years due to the dramatic change of international economic environment.

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