博碩士論文 107552016 詳細資訊




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姓名 謝莉醇(Li-Chun Hsieh)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱
(TCN-based Futures Prediction Using Financial Indices Bargain Chips, and Forum Messages)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2023-1-28以後開放)
摘要(中) 傳統的股票和/或期貨價格預測研究多以過去的股票和/或期貨價格和技術指標為特徵,如KD,RSI,MACD等。很少有研究將討論區留言或法人籌碼作為股票和/或期貨價格預測。在本研究中,PTT和CMoney論壇的討論區留言將使用再訓練的BERT轉換成每日情緒向量,然後以每日情緒向量和三個法人籌碼為特徵,訓練GRU和TCN模型。實驗結果表明,TCN在MAE、MAPE、RMSE和準確率方面都基於GRU的RNN模型。而轉換而成的每日情緒向量、法人籌碼和討論區留言在期貨價格預測中都被證實是有用的。基於歷史期貨價格的市場模擬表明,使用 TCN模型的簡單投資策略,利用技術指標、法人籌碼和討論區留言,可以在一年之間賺取的投資收益為成本的7倍以上。
摘要(英) Traditional research on the stock and/or futures price prediction mostly uses the past stock/future prices and technique indicators, such as KD, RSI, and MACD, as features. Very few studies consider the forum messages or bargaining chips as stock and/or futures price prediction features. In this thesis, discussion messages from both PTT and CMoney forums are converted into daily sentimental vectors using the retrained BERT. The daily sentimental vector as well as three bargaining chips are then used as features to train the GRU and TCN models. The experiment results show that the TCN performs better than the GRU-based RNN model in terms of MAE, MAPE, RMSE, and accuracy. In addition, both of the bargaining chips and forum messages are verified to be useful in the futures price prediction. The market simulations based on the historical futures price show that a simple investment strategy using the TCN model using techniques, bargaining chips, and forum messages can earn more than 7 times of the investment in the period of one year.
關鍵字(中) ★ 期貨預測
★ 時間卷積網路
★ 情緒分析
關鍵字(英) ★ Futures Prediction
★ Temporal Convolutional Network
★ Sentiment Analysis
論文目次 1 Introduction 1
2 RelatedWork 3
2.1 Deep Learning with Numerical Features . . . . . . . . . . . . . . . . . . . 3
2.2 Deep Learning with Graph Features . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Preliminary 7
3.1 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1.1 Multi-Layer Perceptrons . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1.2 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . 8
3.1.3 Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . 9
3.1.4 Temporal Convolutional Networks . . . . . . . . . . . . . . . . . . . 10
3.2 Bidirectional Encoder Representations from Transformers . . . . . . . . . . 11
3.3 Techniques to Reduce Overfitting . . . . . . . . . . . . . . . . . . . . . . . 12
3.3.1 Early Stopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.2 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.3 L1 and L2 Regularizations . . . . . . . . . . . . . . . . . . . . . . . 15
3.4 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4.1 Confusion Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.5 Financial Indicators and Bargaining Chips . . . . . . . . . . . . . . . . . . 19
4 Design 22
4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.3.1 Historical Data of TAIEX Futures . . . . . . . . . . . . . . . . . . . 24
4.3.2 TAIEX Futures Forum . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.1 Retrained BERT Model . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.2 Evaluation Metrics of retrained BERT . . . . . . . . . . . . . . . . 26
4.4.3 Random Oversampling . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4.4 Daily Sentimental Vectors . . . . . . . . . . . . . . . . . . . . . . . 28
4.5 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Performance 31
5.1 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.1.1 Mean Absolute Error . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.1.2 Root Mean Square Error . . . . . . . . . . . . . . . . . . . . . . . . 32
5.1.3 Mean Absolute Percentage Error . . . . . . . . . . . . . . . . . . . 32
5.1.4 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.1.5 Market Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Model Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2.1 Model Tuning for GRU Models . . . . . . . . . . . . . . . . . . . . 37
5.2.2 Model Tuning for TCN Models . . . . . . . . . . . . . . . . . . . . 40
5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6 Conclusions and Future Works 45
Reference 46
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指導教授 孫敏德(Min-Te Sun) 審核日期 2021-1-28
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