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研究生:葉書豪
研究生(外文):Yeh, Shu-Hao
論文名稱:利用深度學習演算法預測企業下市模式
論文名稱(外文):Corporate Delisting Prediction via Deep Learning Algorithms
指導教授:王釧茹
指導教授(外文):Wang, Chuang-Ju
口試日期:2016-07-04
學位類別:碩士
校院名稱:臺北市立大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:31
中文關鍵詞:卷積神經網路深度信念網路深度學習下市預測
外文關鍵詞:Convolutional Neural NetworkDeep Belief NetworkDeep learningDelisting prediction
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本論文使用深度學習演算法,處理下市公司預測之問題。我們搜集一定時間區間內的下市及未下市公司的股票收益當作輸入訊號,並透過兩種屬於深度架構下的深度信念網路以及卷積神經網路來訓練模型,用以預測下一年這些公司將會下市與否。由實驗結果可以得知,本論文提出之方法的預測準確度優於傳統的機器學習演算法。
This thesis provides a new perspective on the corporate delisting prediction problem using deep learning algorithms. By taking the advantages of deep learning, the
representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both delisting and listing companies as input signals and adopt two of the deep learning architectures, Deep Belief Networks (DBN) and Convolutional Neural Networks (CNN), to train the prediction models. The experimental results show that the proposed approach outperforms traditional machine learning algorithms.
致謝 Ⅰ

中文摘要 Ⅱ

Abstract Ⅲ

1 Introduction 1

2 Related Work 3
2.1 Classical Statistical Models.................... 3
2.1.1 Altman’s Z-Score.......................... 3
2.1.2 Ohlson’s O-Score.......................... 4
2.2 Market-Based Models............................. 5
2.3 Machine Learning Models......................... 5
2.3.1 Logistic Regression....................... 6
2.3.2 Neural Networks (NN)...................... 6
2.3.3 Support Vector Machine (SVM).............. 7

3 Methodology 10
3.1 Stock Return Calculation........................10
3.2 Problem Formulation.............................10
3.3 Deep Belief Network (DBN).......................11
3.3.1 Restricted Boltzmann Machines (RBM).......11
3.3.2 Deep Belief Network (DBN).................12
3.4 Convolutional Neural Network (CNN)..............13

4 Experimental Results 15
4.1 Experimental Settings...........................15
4.1.1 Dataset...................................15
4.1.2 Data Preprocessing........................16
4.1.3 Experimental Settings.....................17
4.2 Experimental Results............................20

5 Conclusions and Future Work 28

Bibliography 29
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