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| Title: | 融合新聞情緒分析與結構化狀態空間模型的多重輸入股價預測
Multi-Input Stock Price Forecasting Integrating News Sentiment Analysis and Structured State Space Models |
| Authors: | 陳竑宇
Chen, Hong-Yu |
| Contributors: | 張宏慶
Jang, Hung-Chin
陳竑宇
Chen, Hong-Yu |
| Keywords: | 股價預測
Mamba
新聞情緒分析
結構化狀態空間模型
多模態輸入
Stock Price Forecasting
Mamba
News Sentiment Analysis
Structured State Space Model
Multi-modal Input |
| Date: | 2025 |
| Issue Date: | 2025-09-01 16:20:03 (UTC+8) |
| Abstract: | 隨著金融科技與人工智慧的快速發展，運用機器學習模型進行股價預測成為熱門研究領域。然而，傳統深度學習架構如 LSTM 或 Transformer 在處理長時間序列與整合非結構化新聞資料時，仍存在一定侷限。為提升預測準確性與模型表現，本研究引入選擇性遞迴機制與高推理效率的結構化狀態空間模型 Mamba，並設計融合中文新聞情緒分析與多重股價特徵的預測架構：Mamba-Stock 模型。
本研究以臺灣證券交易所上市之 1,020 檔股票為實驗對象，結合每日股價結構化特徵與新聞情緒特徵，對未來一天的股價進行預測。新聞資料來自《經濟日報》，經翻譯與情緒分析後轉化為每日聚合的情緒分數，並與股價資料對齊輸入模型。同時，本研究設計多組實驗，包含是否加入新聞、特徵選擇策略 (SelectKBest、Random Forest、無特徵挑選) 與不同超參數組合，進行共計 11,976 次訓練與預測實驗。
實驗結果顯示，Mamba-Stock 模型具備高度穩定性與預測準確率，在加入新聞特徵的情境下，平均 R² 提升 0.158，並顯著降低 MAE 與 MAPE。中位 R² 高達 0.9999，驗證了新聞情緒對股價預測的有效貢獻。整體而言，本研究提出具備高度可擴展性與實務應用潛力的預測系統，並證明 Mamba 架構能有效處理結構化與非結構化的資料融合，為金融時間序列預測領域提供嶄新解方。
With the rapid advancement of financial technology and artificial intelligence, stock price forecasting using machine learning models has become a prominent area of research. Traditional deep learning models, such as LSTM and Transformer, face challenges in handling long-term dependencies and integrating unstructured data like news texts. To enhance predictive accuracy and efficiency, this study introduces the Structured State Space Model (SSM) architecture—Mamba—featuring selective recurrence and high inference efficiency. We propose a novel multi-input forecasting framework named Mamba-Stock, integrating both structured market features and unstructured news sentiment.
Using a dataset of 1,020 listed companies from the Taiwan Stock Exchange, the model incorporates daily structured stock features and aggregated sentiment features extracted from translated and analyzed financial news articles from Economic Daily News. A total of 11,976 forecasting experiments were conducted under different configurations, including the presence of news, feature selection methods (SelectKBest, Random Forest, or without feature selection) , and hyperparameter combinations.
The results demonstrate that the Mamba-Stock model delivers stable and accurate predictions. In experiments that incorporated news features, the average R² improved by 0.158, with notable reductions in MAE and MAPE. The median R² reached 0.9999, confirming the significant contribution of news sentiment to predictive performance. This study presents a scalable and practical forecasting system, highlighting Mamba’s capability to effectively integrate structured and unstructured data, offering new insights into time series modeling for financial applications. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
112971015 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112971015 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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