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研究生:陳冠瑋
研究生(外文):CHEN, GUAN-WEI
論文名稱:整合台灣金融BERT情緒分析與CNN-BiLSTM-SA模型進行股票預測
論文名稱(外文):Integrating Taiwan Financial BERT Sentiment Analysis with CNN-BiLSTM-SA Model for Stock Prediction
指導教授:許乙清
指導教授(外文):HSU, I-CHING
口試委員:吳祥維周志賢曾源揆
口試委員(外文):WU, HSIANG-WEICHOU, JUE-SAMTZENG, YUAN-KWEI
口試日期:2024-06-19
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:66
中文關鍵詞:股票預測自注意力機制BERTCNN-BiLSTM-AM
外文關鍵詞:BERTCNN-BiLSTM-AMStock PredictionSelf-Attention Mechanism
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股票市場中,投資人的情緒容易引起股市快速波動,因此在股票預測中,投資人情緒是一個重要的參考因素。本論文為研究台灣社群情緒與台灣股市漲跌關係,基於BERT使用台灣股市新聞進行領域自適應預訓練,建立專精於台灣金融領域的Chinese FinBERT模型,並使用台灣社群討論平台「PTT」股票版的每日討論內容做為台股社群情緒參考資料對Chinese FinBERT模型進行微調,建立Daily FinSentiment模型分析社群情緒並計算情緒分數做為預測股市漲跌的參考特徵。本論文提出CNN-BiLSTM-SA模型使用股票價格及情緒分數做為訓練資料進行股票漲跌預測,此模型使用CNN-BiLSTM-AM模型進行改良,將Attention Mechanism(AM)架構替換為Self Attention(SA)使其對於訓練資料理解更加全面,模型中的CNN層用於提取訓練資料的重要特徵,BiLSTM層根據提取特徵值進行預測,並使用SA層確定不同時間特徵對股票收盤價的影響後更新模型權重。在本論文實驗結果中,使用未加入情緒特徵訓練資料時,CNN-BiLSTM-SA模型預測準確率相較CNN-BiLSTM-AM模型提升22%,準確率達到87.5%,訓練資料加入情緒特徵後,準確率進一步提升至90.62%,模型在經過改良且加入投資人情緒特徵參考後的預測效果顯著提高。本論文將CNN-BiLSTM-SA模型與LINE Bot串接,使預測結果能夠方便檢視並參考。
In the stock market, investor sentiment can easily cause rapid fluctuations, making it an important reference factor in stock prediction.。This paper investigates the relationship between community sentiment in Taiwan and the fluctuations of the Taiwan stock market. We conduct domain-adaptive pre-training using BERT on Taiwan stock market news to establish a Chinese FinBERT model specialized in the Taiwanese financial sector. We then fine-tune the Chinese FinBERT model using daily discussion content from the stock section of the Taiwanese community discussion platform "PTT" as a reference for Taiwan stock market community sentiment, thereby creating a Daily FinSentiment model. This model analyzes community sentiment and calculates sentiment scores as reference features for predicting stock market fluctuations.。This paper proposes the CNN-BiLSTM-SA model, which uses stock prices and sentiment scores as training data to predict stock price movements. This model is an improvement over the CNN-BiLSTM-AM model, with the Attention Mechanism (AM) architecture replaced by Self Attention (SA) to achieve a more comprehensive understanding of the training data. The CNN layer in the model is used to extract important features from the training data, the BiLSTM layer makes predictions based on the extracted feature values, and the SA layer updates model weights by determining the impact of different temporal features on stock closing prices. In the experimental results of this paper, the CNN-BiLSTM-SA model achieves a prediction accuracy of 87.5%, representing a 22% improvement compared to the CNN-BiLSTM-AM model when trained without sentiment features. When sentiment features are included in the training data, the accuracy further improves to 90.62%. This significant enhancement in prediction performance demonstrates the effectiveness of incorporating investor sentiment features into the model. Furthermore, we integrate the CNN-BiLSTM-SA model with a LINE Bot, allowing for easy review and reference of prediction results.
摘要.............i
Abstract........ii
誌謝............iii
目錄............iv
表目錄..........vii
圖目錄..........viii
第一章 緒論......1
1.1 研究背景.....1
1.2 研究動機.....2
1.3 研究目的.....3
第二章 研究技術與探討......4
2.1 情緒分析..............4
2.2 BERT (Bidirectional Encoder Representations from Transformer).............5
2.3 領域自適應訓練........6
2.4 Fine-Tuning..........7
2.5 機器學習(Machine Learning)....8
監督式學習(Supervised Learning)...8
非監督式學習(Unsupervised Learning)......8
半監督式學習(Semi-Supervised Learning)...8
2.6 多元分類 (Multiclass Classification).9
2.7 長短期記憶 (Long Short-Term Memory, LSTM)....10
2.8 卷積雙向長短期記憶注意力機制 (Convolutional Neural Network Bi Long Short-Term Memory Attention Mechanism , CNN-Bi-LSTM-AM).........................11
2.9 注意力機制(Attention Mechanism)......12
2.10 自注意力機制(Self Attention).......13
2.11 混淆矩陣(Confusion Matrix)..........14
2.11.1 準確率(Accuracy)..................14
2.11.2 精確率(Precision).................14
2.11.3 召回率(Recall)....................15
2.11.4 調和平均數(F1-Score)..............15
2.12 近年相關研究..............16
第三章 研究方法與步驟..............17
3.1 Chinese FinBERT資料蒐集與訓練..............17
3.1.1 資料來源..............17
3.1.2 資料前處理..............18
3.1.3 Mask Language Model(MLM)預訓練..............18
3.1.4 Chinese FinBERT資料集..............19
3.1.5 Chinese FinBERT模型架構..............20
3.2 情緒分析模型Daily FinSentiment..............21
3.2.1 社群資料蒐集與說明..............21
3.2.2 資料標記..............22
3.2.3 半監督式標記 Daily FinSentiment資料集..............22
3.2.4 情緒分數計算方式..............23
3.2.5 模型訓練..............23
3.3 股票預測模型CNN-BiLSTM-SA大盤及產業類股預測..............25
3.3.1 CNN-BiLSTM-SA預測架構圖 25
Input Layer..............26
CNN Layer..............26
BiLSTM Layer..............27
Self Attention..............27
Output Layer..............29
3.4 股票產業類股分類預測..............29
3.4.1 資料集建置..............29
3.4.2 預測模型特徵值..............31
3.4.3 股票分類預測架構圖..............31
3.5 系統介面架構..............32
3.5.1 系統介面之系統架構圖..............33
第四章 研究結果..............34
4.1 Chinese FinBERT預訓練模型結果..............34
4.2 Daily FinSentiment每日情緒預測模型結果..............36
4.3 台股大盤預測模型結果..............37
4.4 台灣產業類股預測結果..............38
4.5 系統UI應用介面..............39
明日股價漲跌以及類股漲跌預測..............39
金融新聞及金融商品報價..............40
第五章 結論..............41
第六章 未來展望..............42
參考文獻..............43
Extended Abstract..............47
Abstract..............47

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