根據定性與定量資料預測企業信用評級

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姓名

賴沂彤(Yi-Tung Lai) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

根據定性與定量資料預測企業信用評級
(Predicting Corporate Credit Ratings Based on Qualitative and Quantitative Data)

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摘要(中)

企業信用評級是資本市場裡投資者對一家公司的重要判斷依據，公開評級結果能夠使企業的違約風險與信用狀況更加公開透明，以降低投資者與經營者之間的資訊不對稱，讓資本市場的交易更公平。

由於傳統的企業信用評級評定過程非常耗費時間與金錢成本，為了即時且快速地因應市場變化作出更新，採用機器學習方法來預測企業信用評級成了近年常被討論的研究課題。

本研究將文本資料(公司管理階層觀點)透過自然語言模型(NLP)處理後匯出文本向量表示，並加入檢索增強生成(RAG)的輸出結果，最後再結合傳統財務數據並匯入極限梯度提升法(XGBoost)進行企業信用評級的預測，來獲得一個兼顧準確率與可解釋力的多元分類模型。

摘要(英)

Corporate credit rating serves as a critical reference for investors in the capital market to evaluate a company. Publicly available rating results enhance transparency regarding a company’s default risk and credit status, reducing information asymmetry between investors
and operators, and promoting fairer transactions in the capital market.

Traditional corporate credit rating processes are time-consuming and costly. To respond to market changes promptly and efficiently, applying machine learning methods to predict corporate credit ratings has become a widely discussed research topic in recent years.

This study leverages textual data (MD&A), processed through natural language models (NLP), to generate text vector representations. It incorporates the output of Retrieval-Augmented Generation (RAG) and integrates it with traditional financial data. These inputs are then fed into the Extreme Gradient Boosting (XGBoost) algorithm to predict corporate credit ratings, aiming to develop a multi-class classification model that balances accuracy and interpretability.

關鍵字(中)

★ 企業信用評級預測
★ MD&A文本分析
★ 深度學習
★ 極限梯度提升法

關鍵字(英)

★ Corporate Credit Rating Prediction
★ MD&A
★ Deep Learning
★ XGBoost

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章、緒論 1
1-1 研究背景與動機 1
1-2 問題定義 2
1-3 研究目的 3
1-4 研究方法 4
1-5 研究架構 4
第二章、文獻探討 5
2-1 預測企業信用評級的相關研究 5
2-2 文本分析 8
2-3 蒸餾BERT (Distil Bidirectional Encoder Representations from Transformers, DistilBERT) 9
2-3-1 詞嵌入 (Word Embedding) 9
2-3-2 轉換器 (Transformer) 10
2-4 檢索增強生成 (Retrieval-Augmented Generation, RAG) 12
2-4-1 檢索器（Retriever）與生成器(Generator） 12
2-4-2 大型語言模型（LLM） 12
2-5 極限梯度提升法 (Extreme Gradient Boosting, XGBoost) 13
第三章、方法論 14
3-1 原始資料介紹與資料前處理 15
3-1-1 數值資料 15
3-1-2 文本資料 15
3-1-3 企業信用評級的分類 16
3-2 模型設計 17
3-2-1 蒸餾BERT (Distil Bidirectional Encoder Representations from Transformers, DistilBERT) 17
3-2-2 檢索增強生成 (Retrieval-Augmented Generation, RAG) 18
3-2-3 極限梯度提升法 (Extreme Gradient Boosting, XGBoost) 19
3-3 模型評估 21
第四章、實驗結果 22
4-1 實驗設計 22
4-1-1 資料集分割 22
4-1-2 輸入變量組合與訓練參數設計 22
4-1-3 最佳化模型配置 23
4-2 實驗分析與評估 27
4-2-1 探討不同配置組合之預測績效 27
4-2-2 探討不同配置組合在不同模型下之預測績效 30
4-2-3 極限梯度提升法之可解釋力 34
第五章、結論與未來研究方向 35
參考文獻 37
附錄一財務指標 40
附錄二 Query 41

參考文獻

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指導教授

葉英傑

審核日期

2025-1-8

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