基於台灣上市公司長期和短期行為的財務困境預測;Financial Distress Prediction Based on Long-Term and Short-Term Behaviour of Taiwan List Companies

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Title:	基於台灣上市公司長期和短期行為的財務困境預測;Financial Distress Prediction Based on Long-Term and Short-Term Behaviour of Taiwan List Companies
Authors:	阿努蒂;FADILAH, AYU NUR
Contributors:	資訊工程學系
Keywords:	奧特曼變量;財務困境預測;整合學習;Altman Variables;Financial Distress Prediction;Ensemble Learning
Date:	2020-07-17
Issue Date:	2020-09-02 17:43:09 (UTC+8)
Publisher:	國立中央大學
Abstract:	財務比率在先前的研究中已被廣泛使用，以建立其財務困境預測模型。奧特曼（Altman）所提出的Z分數模型已成為預測中最常使用的方法，特別是在學術研究中。在理想情況下，Altman的Z-Score模型旨在在兩年內衡量公司的財務狀況，並且被證實在各種情況和市場中預測破產是準確的。然而，以前的研究都沒有嘗試以不同的方式來識別和分析五個奧特曼變量，並根據它們的行為對其進行威脅。因此，本研究受到研究問題的推動：使用疊加泛化將五個Altman變量分為長期和短期行為，是否可以幫助預測明年台灣上市公司的財務困境？為了研究該問題，我們提出了將五個奧特曼變量並行處理為兩個不同特徵集的堆疊整合學習方法，並進行了綜合分析。這些研究發現不僅有助於混合所有財務比率資訊，還可以根據長期和短期條件仔細考慮，從而協助公共投資考慮貸款決策。;Financial Ratio had been used widely on the previous research to build their model of financial distress prediction. Altman’s Z-Score was become the most often used for predicting especially in academic studies. Ideally, Altman’s Z-Score purposes to measure a company’s financial health within two years and it proven accurate to forecast bankruptcy in a wide variety of contexts and markets. However, none of the previous research tried to identify and analyse the five Altman variables differently and threat them based on their behaviour. Therefore, this study is motivated by research question: Could the splitting of five Altman Variables into Long-Term and Short-term behaviour using stacking generalization help to predict the financial distress of Taiwan list companies in the next year? To examine this question, we proposed the stacking ensemble learning which threat five Altman Variables into two different feature set parallel and conducted a comprehensive analysis. These findings will help the public investment to consider a lending decision, not only by mixing all information of financial ratio, but carefully consider based on its long-term condition and short-term condition.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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