政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152791

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Title:	結合數值資訊與文字資訊的股價預測模型：以有考慮ESG因子的股票為例 Stock price prediction model combining numerical information and text information: taking stocks that consider ESG factors as an example
Authors:	蔡青穎
Contributors:	黃泓智蔡青穎
Keywords:	ESG投資嵌入模型離散小波轉換集成學習 ESG investment Embedding model Discrete wavelet transform Ensemble learning
Date:	2024
Issue Date:	2024-08-05 14:03:16 (UTC+8)
Abstract:	本研究探討結合數值資訊與文字資訊來預測有考慮ESG因子股票的隔日報酬率表現，提出了一種多元的預測模型。ESG投資日益受到重視，不僅因其社會責任和環境影響，還因其在長期財務表現中的潛力。然而，台灣現有的ESG數據庫存在資料缺失和標準不一致等問題，促使我們採用不同的文字資料來源進行預測。研究方法包括資料的預處理，包含利用M3-Embedding的嵌入模型作為文字資訊的向量化處理，並跳脫以往情緒分析的框架，直接將所有向量作為特徵值，以及數值資料的離散小波轉換，機器學習部分則利用多種機器學習模型（包括極限學習機、隨機森林、多層感知器、支援向量機以及卷積神經網路）和集成學習方法進行訓練和比較。實證結果顯示，僅含有數值資訊的模型仍有較低的誤差值，然而，整體來看，綜合了數值和文字資訊的模型在預測股價報酬率和風險控制方面均表現出較好的績效，尤其是在夏普比率、最大回落以及報酬率等績效指標上優於僅使用單一類型資訊的模型，且更能有效地利用市場的即時資訊進行預測。綜上，本研究證明了在股票預測中結合文字和數值資訊的可行性和優勢，為ESG投資的相關研究提供了新的方向和參考。 This study explores the combination of numerical information and text information to predict the next-day return performance of stocks that consider ESG factors, and proposes a multivariate prediction model. ESG investing is increasingly valued not only for its social responsibility and environmental impact, but also for its potential in long-term financial performance. However, existing ESG databases in Taiwan have problems such as missing data and inconsistent standards, which prompts us to use different textual data sources for prediction. Research methods include data preprocessing, including using the M3-Embedding embedding model as vectorization processing of text information, breaking away from the previous sentiment analysis framework, directly using all vectors as features, and discrete wavelet transformation of numerical data. Also, this study uses a variety of machine learning models (including extreme learning machines, random forests, multi-layer perceptrons, support vector machines and convolutional neural networks) and ensemble learning method for training and comparison. Empirical results show that models containing only numerical information still have lower error values. However, the model that combine numerical and text information show better performance in predicting stock price changes and risk control, especially in terms of Sharpe ratio, performance indicators such as maximum drawdown and return rate are better than models that only use a single type of information, and can more effectively utilize real-time market information for prediction. In summary, this study proves the feasibility and advantages of combining text and numerical information in stock prediction, and provides new directions and references for ESG investment-related research.
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Description:	碩士國立政治大學風險管理與保險學系 111358028
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111358028
Data Type:	thesis
Appears in Collections:	[Department of Risk Management and Insurance] Theses

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