應用倒傳遞類神經與序列探勘技術建構企業財務危機預警模型─以台灣電子產業為例

Applying Back Propagation Neural Network and Sequential Pattern Mining to Construct Corporation Crisis Prediction Model–A Case of Taiwan’s Electronic Industry

洪立劼(Li-Jie Hon)

指導教授 ： 羅淑娟

國立臺北科技大學/管理學院/工業工程與管理研究所/碩士(2008年)

摘要

早期危機預警模型的建立，常常透過財務報表上的資訊作為學習樣本，以統計方法或人工智慧的技術建立預測模型，再利用預測模型評估未來公司發生危機的可能性，但這些預測模型必須有過去特定期間的財務資訊，才能對未來產生預測，無法透過過去的趨勢，在缺乏財務資訊的情況下進行預測。本研究根據過去的研究，考量產業因素，採取不同的樣本配對方法，並透過不同時期的樣本資料建立倒傳遞類神經分類模型，並將這些分類模型的分類訊號進一步以序列探勘進行探勘研究，試圖取得健全公司與危機公司的預測樣式並利用樣式進行預測。結果發現，倒傳遞類神經方法結合序列探勘技術可有效地對公司未來情況進行預測。

關鍵字

倒傳遞類神經網路 ； 序列探勘 ； 預測樣式

並列摘要

A bankruptcy prediction model is often built upon the information which comes from financial statements. Many researchers adopt statistical methods or artificial intelligence to build the classification model and use the model to predict the future status. Since these models require financial information to judge or predict the operational situation, it is impossible to predict without any financial data. Our research tries to combine Back-propagation Neural Network(BPNN) and sequential pattern mining to overcome this drawback. We use two ways to match our distress and non-distress data by considering industrial factors and use samples from different period to build the classification models. We see classification result from the models as signals, which means distress or non-distress at specific term and furthermore, we mine those signals in order to get some patterns which help us do prediction. We experiment on financial data of Taiwan’s electronic industry from TEJ database and the result shows the combination of BPNN and sequential pattern mining can predict the operational status efficiently.

並列關鍵字

BPNN ； Sequential Pattern Mining ； Prediction Pattern

參考文獻

[41] 陳仕昇，許秉瑜，陳彥良，”以可重複序列挖掘網路瀏覽規則之研究”，資管評論，第九卷，1999，第53-72頁。

[1] B. P. Foster, T. J. Ward, and J. Woodroof, "An analysis of the usefulness of Debt defaults and Going Concern Opinions in Bankruptcy Risk Assessment," Journal of Accounting Auditing & Finance, Summer, 1998, pp. 351-371.

[2] E. B. Deakin, "A Discriminate Analysis of Predictor of Business Failure," Journal of Accounting Research, vol. 10, 1972, pp. 167-179.

[3] E. I. Altman, "Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy," Journal of Finance, 1968, pp.589-609.

[4] E. I. Altman, G.. Marco, and F. Varetto, "Corporate Distress dDiagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks," Journal of Banking and Finance, vol. 18, 1994, pp. 505-529.