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| Title: | 新聞情緒之於罕見評級轉移的早期預警：檢驗 FinBERT 在情緒偏向與類別失衡情境下的領先預測能力
Early-Warning Power of News Sentiment for Infrequent Rating Transitions: Examining FinBERT’s Leading Predictive Ability under Sentiment Skew and Class Imbalance |
| Authors: | 林威宇
Lin, Wei-Yu |
| Contributors: | 江彌修
Chiang, Mi-Hsiu
林威宇
Lin, Wei-Yu |
| Keywords: | 罕見評級變動
類別失衡深度學習
情緒萃取
SMOTE合成資料 |
| Date: | 2025 |
| Issue Date: | 2025-07-01 15:19:01 (UTC+8) |
| Abstract: | 本研究針對傳統財務變數難以有效利用非結構化新聞資訊之侷限，提出結合遷移式學習與金融語境特化模型的企業信用預警創新方法。利用金融領域預訓練自然語言模型 FinBERT，本研究從 RavenPack 資料庫所蒐集之美國 S&P500 成分股新聞資料中萃取新聞文本之情緒特徵，進而建構企業信用不確定風險指標 (Corporate Credit Uncertainty risk index, CCU)，用以量化新聞情緒對企業未來信用狀況可能產生的不確定性衝擊。實證結果顯示，FinBERT 不僅在情緒辨識 (正面、中立、負面) 的多項驗證指標表現上顯著優於一般語境模型 BERT，尤其在情緒偏向顯著的資料中展現更佳的穩定辨識能力，凸顯其遷移學習的明顯優勢。
為克服企業信用評級變動資料中評級變動三類標籤間的嚴重類別失衡問題，本研究採用 SMOTE (Synthetic Minority Over-sampling Technique) 技術進行少數類別樣本強化，以提升模型對罕見變動事件的辨識能力。結合傳統財務變數與 CCU 指標的 XGBoost 機器學習分類預警模型證實，CCU 指標在特徵重要性排序中顯著領先其他變數，是預測企業信用評級變動的最關鍵特徵，能有效補足傳統模型對非結構化軟性資訊的反應不足，顯著提升企業信用預警在情緒偏向與類別失衡情境下的即時性與準確度。
This study proposes a novel early warning framework for corporate credit rating changes by incorporating unstructured textual information from financial news, which is often overlooked by traditional financial variables. Utilizing the domain-specific pre-trained language model FinBERT, we extract sentiment features from news articles and construct a Corporate Credit Uncertainty (CCU) index to quantify the impact of media sentiment on credit risk. Based on news data of S&P 500 constituent companies from the RavenPack database, we compare the performance of BERT and FinBERT in identifying three sentiment categories (positive, neutral, negative). Empirical results indicate that FinBERT significantly outperforms the general-purpose BERT model across various evaluation metrics and maintains robust performance in sentiment-biased corpora, demonstrating the effectiveness of transfer learning in financial contexts.
To address the severe class imbalance among rating changes, we adopt the Synthetic Minority Over-sampling Technique (SMOTE) to enhance the model’s ability to recognize rare rating transitions. We then construct a classification model using XGBoost, incorporating both CCU and traditional financial indicators. Results show that the CCU index ranks highest in feature importance and outperforms all conventional variables, serving as a key predictive signal for credit rating movements. The proposed approach effectively captures soft information absent in traditional models and significantly improves the timeliness and accuracy of credit risk forecasting, particularly under sentiment bias and class imbalance scenarios. |
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| Description: | 碩士
國立政治大學
金融學系
112352035 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112352035 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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