情感分析對股價預測之影響研究

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郭兆瑞(Chao-Jui Kuo) 查詢紙本館藏

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資訊管理學系

論文名稱

情感分析對股價預測之影響研究
(Stock Prediction based on Sentiment Analysis)

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至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

目前股價預測研究多是基於技術面的數據資料，然而隨著網路社群的發展，消息面的發酵會影響投資人的決策和短期股價走向。因此，如何萃取消息面的文本資訊，進而能同時考量技術面和消息面的影響，將是未來提升股價預測準確性的重要研究方向。
本研究主要是利用近年來改良的動態與靜態詞嵌入方法，預測社群媒體文本的情感分數後，結合技術面資料後，透過深度學習分類器進行股價預測。而在深度學習分類器中，在情感分析方法的部分是根據近年來在文本序列上十分熱門的Transformer架構進行不同組態的改良搭配，以找出最適用於情感分析的Transformer組態。而在股價預測方面，則是提出一個結合混合式模型中的CNN，與Transformer架構中的Multi-head Self-Attention機制，搭配LSTM進行改良的分類器(Multi-head Self-Attention with Convolutional neural network LSTM, MAC-LSTM)，加強重要訊息的關注，以更準確掌握股票漲跌的趨勢。
本研究蒐集兩個社群媒體文本(Twitter、Stocktwit)的資料並以亞馬遜公司(AMZN)與網飛公司(NFLX)為預測標的，搭配不同的情感分析方法(辭典法、API、動靜態詞嵌入情感分析方法)，並以三種深度分類器，ATLSTM、CNN-LSTM和MAC-LSTM進行股價預測，探討其何種組合最適用於股價預測。最終，本實驗發現以內容面改良的動態詞嵌入情感分析方法finBERT，搭配深度學習分類器MAC-LSTM能最精準的預測股票的未來漲跌趨勢，其準確率最高達71.70%，為最佳組合。

摘要(英)

At present, most research on stock price prediction is based on technical data. However, With the development of social media, investors can easily receive numerous information so that they will make different decisions, and thus cause the short-term inflation of the stock price. Therefore, considering both text and technical data will be an important direction for improving the accuracy of stock price prediction.
This research is mainly discussed the efficiency about using several improved dynamic and static word embedding methods to conduct the sentiment scores of social media texts, and then combine them with structured data to predict stock trend by a deep learning classifier. In the deep learning classifier, we figure out the best Transformer configuration which called Enhanced Transformer for sentiment analysis. In the area of stock price prediction, we propose an improved classifier (Multi-head Self-Attention with Convolutional Neural Network LSTM, MAC) by combining CNN in hybrid model and Multi-head Self-Attention mechanism in Transformer architecture. MAC-LSTM) to enhance the attention of important information to grasp the trend of stock rise and fall more accurately.
This research collects data from two kinds of social media text data (Twitter and Stocktwit) and uses Amazon (AMZN) and Netflix (NFLX) as the predict targets, with different sentiment analysis methods (dictionary method, API, and dynamic word embedding sentiment analysis method), and three deep classifiers, ATLSTM, CNN-LSTM, and MAC-LSTM, for investigating which combination is most suitable for stock prediction. At the end of the research, we found that the improved dynamic word embedding sentiment analysis method finBERT with the stock prediction classifier MAC-LSTM can predict the future stock trend with the best combination of 71.70% accuracy.

關鍵字(中)

★ 詞嵌入
★ 情感分析
★ 股價預測
★ 深度學習
★ Transformer

關鍵字(英)

★ word embedding
★ sentiment analysis
★ stock price prediction
★ deep learnring
★ Transformer

論文目次

目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 3
二、文獻探討 4
2-1 文字探勘的基本概念 4
2-2 過往股票預測文獻探討 4
2-3 動、靜態詞嵌入方法的發展 5
2-3-1 fastText 8
2-3-2 BERT&finBERT 9
2-3-3 XLNet 11
2-4 深度學習分類器模型 14
2-4-1 LSTM 17
2-4-2 注意力機制 (Attention Mechanism) 18
2-5 Transformer的架構、特性及標準化(normalization) 19
三、研究方法 23
3-1 研究資料集 24
3-2 資料前處理 25
3-2-1 文本資料前處理 25
3-2-2 數值資料前處理 26
3-3 評估指標 27
3-4 探討Transformer分類器於靜態詞嵌入情感分析之最佳組態 27
3-5 探討近年改良的動靜態詞嵌入方法於情感分析之影響 28
3-6 探討不同情感分析方法搭配不同分類器於股價預測之適用性 29
3-6-1 漲跌標註 31
3-6-2 Day Forward-Chaining 31
3-6-3 詞嵌入情感分析方法 32
3-6-4 辭典法 33
3-6-5 情感分析套件SenticNet 34
3-6-6 分類器架構 34
3-7 探討結構化和非結構化資料合併後，於股價預測的結果 36
四、實驗結果與分析 38
4-1 針對情感分析設計並評估具有最佳組態的Enhanced Transformer 38
4-2 不同情感分析方法於財經相關文本之適用性探討 40
4-2-1 評選最佳的情感字典 41
4-2-2 探討不同詞嵌入法於財經相關文本之適用性 42
4-3 探討不同情感分析方法對於股價預測之影響 43
4-3-1 以資料集的角度探討情感分析方法於股價預測模型的效能 44
4-3-2 以分類器的角度探討情感分析方法於股價預測模型的效能 45
4-3-3 小結 46
4-4 不同深度學習分類器對於股價預測的優劣 47
4-5 不同資料集來源和不同特性之個股對於股價預測之影響 48
4-5-1 不同社群媒體平台於股價預測的適用性 48
4-5-2 不同特性之個股於股價預測的適用性 49
4-6 探討消息面與數據面資料合併，對於股價預測模型之影響 50
4-6-1 探討使用數據面、消息面和兩者合併在股價預測上的效能 51
4-6-2 和現有研究進行效能評比 52
4-7 以最佳組合實際進行股市模擬的成效 54
4-7-1 建立最佳的自動化交易策略 55
4-7-2 不同標的於股市模擬之效能分析 59
4-7-3 與現有指標的效能評比 60
五、結論 62
5-1 結論與貢獻 62
5-2 研究限制 65
5-3 未來研究與建議 65
參考文獻 66

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指導教授

蘇坤良(Kuen-Liang Su)

審核日期

2022-8-4

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