Includes bibliographical references (leaves 255-266). / The research objective of this study was to construct an empirical model for the prediction of corporate failure in South Africa through the application of machine learning techniques using information generally available to investors. The study began with a thorough review of the corporate failure literature, breaking the process of prediction model construction into the following steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE Securities Exchange between 1 January 1996 and 30 June 2003. A paired sample of non-failed companies was selected. Pairing was performed on the basis of year of failure, industry and asset size (total assets per the company financial statements excluding intangible assets). A minimum of two years and a maximum of three years of financial data were collated for each company. Such data was mainly sourced from BFA McGregor RAID Station, although the BFA McGregor Handbook and JSE Handbook were also consulted for certain data items. A total of 75 financial and non-financial ratios were calculated for each year of data collected for every company in the final sample. Two databases of ratios were created - one for all companies with at least two years of data and another for those companies with three years of data. Missing and undefined data items were rectified before all the ratios were normalised. The set of normalised values was then imported into MatLab Version 6 and input into a Population-Based Incremental Learning (PBIL) algorithm. PBIL was then used to identify those subsets of features that best separated the failed and non-failed data clusters for a one, two and three year forward forecast period. Thornton's Separability Index (SI) was used to evaluate the degree of separation achieved by each feature subset.

國內企業失敗的研究大都定位在財務危機的領域，且幾乎完全以股票上市 公司為研究對象。因受限於國內證券市場規模太小，研究樣本受到相當大 的限制，歷年來發生財務危機的案例甚少，所以財務預警系統只說明模式 本身區別成效，至於樣本外的測試，則付之闕如，無法了解此一模式對不 同時期的樣本是否仍有相同的區別能力，模式穩定性也就無從得知了。本 研究採用洪榮華（民82）的研究，擴大解釋危機的定義，以企業發生虧損 為一觀察事件，找尋足夠的樣本來建構財務危機預測模式，並從事樣本外 的預測，以驗證區別模式的穩定性。前人的研究結果顯示：大多數的預測 模式在樣本內的測試均有相當不錯的區別能力，樣本外的預測能力卻大幅 下降。Platt & Platt（1990）對於模式不穩定的原因歸納出兩個結論： 財務報表資料會隨時間的經過產生不穩定的現象、產業間財務資料的差異 ，經由產業相對財務比率的調整可以消除產業效果使得模式的區別能力趨 於穩定。Marquette（1980） 指出，良好的預測模型必須具有動態模型、 經得起時間考驗、以及不受時間約束等特性。也就是說，一個優良的財務 預警模型不會受到產業與時間因素的影響，可以適用在不同產業、時期， 即模式具有高度的穩定性。因此，基於上述理由本研究之目的有三：1.比 較以產業相對財務比率與公司財務比率所建立的預警模式對企業財務危機 的預測，何者有較佳的區別能力。2.驗證由原始樣本所建立區別模式對同 一時期的保留樣本，是否有顯著的區別能力，用以了解模式是否有同期的 適用性。3.驗證由原始樣本所建立區別模式對後期樣本，是否有顯著的預 測能力，用以了解模式是否有跨期的適用性。結論有四：1.公司財務比率 模型並非一穩定模型。模式本身具有區別能力，但卻不具有同期的適用性 與跨期的適用性。2.相對而言，產業相對財務比率模型為一穩定模型。模 式本身不僅具有區別能力，也同時具有跨期的適用性。3.就模式的區別能 力與預測能力而言，除危機前一年外，產業相對財務比率模型的區別效果 ，並未顯著優於公司財務比率模型。4.從本研究中發現，無論公司或產業 相對比率模型，對財務危機事件的預測，短期的預測能力高，長期則顯著 下降。

Corporate Financial Planning--A System Dynamic Approach.