應用AI技術於財務危機預測

CCUR > College of Business > Department of Accounting & Graduate Institute of Accounting > Thesis > Item 987654321/51241

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/51241

Title:	應用AI技術於財務危機預測 Application of AI Technologies in Financial Distress Prediction
Authors:	翁汪偉
Contributors:	會計學系
Keywords:	財務危機機器學習深度學習卡方自動交叉檢驗支持向量機卷積神經網路 financial distress machine learning deep learning chaid support vector machine convolutional neural network
Date:	2022
Issue Date:	2023-03-01 14:47:27 (UTC+8)
Abstract:	財務危機發生使員工、債權人、股東以及其他利害關係人（stakeholders）造成嚴重的損害，也會造成社會經濟的動盪。因此，建立一個有效的財務危機預測模型是相當重要的。本研究樣本取自台灣經濟新報資料庫（taiwan economic journal），使用2010年至2020發生財務危機之台灣上市櫃公司作為樣本對象並以1:2進行配對，採用14個財務變數與4個非財務變數。首先分別使用決策樹CHAID與支持向量機(support vector machine)進行重要變數之篩選，再將變數資料輸入至卷積神經網路(convolutional neural network)進行訓練，分別來建立財務危機預測模型。實證結果顯示，使用支持向量機搭配卷積神經網路所建立之模型(SVM-CNN)，並跟未經篩選後之卷積神經網路模型與決策樹CHAID搭配卷積神經網路模型進行比較，以支持向量機篩選後之卷積神經網路模型為本研究最佳財務危機預測模型，其平均準確率達89.74%。 The financial distress causes serious damage to employees, creditors, shareholders and other stakeholders, as well as social and economic turmoil. Therefore, it is very important to establish an effective financial distress prediction model. The sample of this research is taken from the database of Taiwan Economic Journal (TEJ), using Taiwan listed OTC companies with financial distress from 2010 to 2020 as the sample object and matching 1:2, using 14 financial variables and 4 non-financial variables. First, the CHAID and the support vector machine are used to screen important variables, and then the variable data is input into the convolutional neural network for training, respectively, to establish a financial distress prediction model. The empirical results show that the model (SVM-CNN) established by using the support vector machine with the convolutional neural network is compared with the unfiltered convolutional neural network model and the CHAID with the convolutional neural network model , the convolutional neural network model screened by support vector machine is the best financial crisis prediction model in this study, and its average accuracy rate is 89.74%.
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] Thesis

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	154	View/Open

View Licence

Loading...