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研究生:柳育彣
研究生(外文):Liu, Yu-Wen
論文名稱:財報文字分析之句子風險程度偵測研究
論文名稱(外文):Risk-related Sentence Detection in Financial Reports
指導教授:蔡銘峰蔡銘峰引用關係王釧茹
指導教授(外文):Tsai, Ming-FengWang, Chuan-Ju
口試委員:蘇家玉
口試委員(外文):Su, Chia-Yu
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:35
中文關鍵詞:文字探勘財務風險情緒分析機器學習
外文關鍵詞:Text miningFinancial riskSentiment analysisMachine learning
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本論文的目標是利用文本情緒分析技巧,針對美國上市公司的財務報表進行以句子為單位的風險評估。過去的財報文本分析研究裡,大多關注於詞彙層面的風險偵測。然而財務文本中大多數的財務詞彙與前後文具有高度的語意相關性,僅靠閱讀單一詞彙可能無法完全理解其隱含的財務訊息。本文將研究層次由詞彙拉升至句子,根據基於嵌入概念的~fastText~與~Siamese CBOW~兩種句子向量表示法學習模型,利用基於嵌入概念模型中,使用目標詞與前後詞彙關聯性表示目標詞語意的特性,萃取出財報句子裡更深層的財務意涵,並學習出更適合用於財務文本分析的句向量表示法。實驗驗證部分,我們利用~10-K~財報資料與本文提出的財務標記資料集進行財務風險分類器學習,並以傳統詞袋模型(Bag-of-Word)作為基準,利用精確度(Accuracy)與準確度(Precision)等評估標準進行比較。結果證實基於嵌入概念模型的表示法在財務風險評估上比傳統詞袋模型有著更準確的預測表現。由於近年大數據時代的來臨,網路中的資訊量大幅成長,依賴少量人力在短期間內分析海量的財務資訊變得更加困難。因此如何協助專業人員進行有效率的財務判斷與決策,已成為一項重要的議題。為此,本文同時提出一個以句子為分析單位的財報風險語句偵測系統~RiskFinder~,依照~fastText~與~Siamese CBOW~兩種模型,經由~10-K~財務報表與人工標記資料集學習出適當的風險語句分類器後,對~1996~至~2013~年的美國上市公司財務報表進行財報句子的自動風險預測,讓財務專業人士能透過系統的協助,有效率地由大量財務文本中獲得有意義的財務資訊。此外,系統會依照公司的財報發布日期動態呈現股票交易資訊與後設資料,以利使用者依股價的時間走勢比較財務文字型與數值型資料的關係。
The main purpose of this paper is to evaluate the risk of financial report of listed companies in sentence-level. Most of past sentiment analysis studies focused on word-level risk detection. However, most financial keywords are highly context-sensitive, which may likely yield biased results. Therefore, to advance the understanding of financial textual information, this thesis broadens the analysis from word-level to sentence level. We use two sentence-level models, fastText and Siamese-CBOW, to learn sentence embedding and attempt to facilitate the financial risk detection. In our experiment, we use the 10-K corpus and a financial sentiment dataset which were labeled by financial professionals to train our financial risk classifier. Moreover, we adopt the Bag-of-Word model as a baseline and use accuracy, precision, recall and F1-score to evaluate the performance of financial risk prediction. The experimental results show that the embedding models could lead better performance than the Bag-of-word model. In addition, this paper proposes a web-based financial risk detection system which is constructed based on fastText and Siamese CBOW model called RiskFinder. There are total 40,708 financial reports inside the system and each risk-related sentence is highlighted based on different sentence embedding models. Besides, our system also provides metadata and a visualization of financial time-series data for the corresponding company according to release day of financial report. This system considerably facilitates case studies in the field of finance and can be of great help in capturing valuable insight within large amounts of textual information.
致謝 1
中文摘要 2
Abstract 3
第一章 緒論 1
1.1 研究背景 1
1.2 傳統財務風險預測方法及其限制 1
1.3 研究目標 2
第二章 相關文獻探討 4
2.1 財務風險預測 4
2.2 文字情緒分析與詞向量表示法 5
第三章 研究方法 8
3.1 Word2Vec 8
3.2 fastText 10
3.2.1 基於字根的詞象量學習法 10
3.2.2 句向量的線性分類器 11
3.3 Siamese CBOW 12
3.4 財報風險語句標記資料集 14
第四章 實驗結果與討論 16
4.1 實驗設定 16
4.1.1 資料集搜集及資料前處理 16
4.1.2 量化評估標準 19
4.2 實驗結果分析與討論 20
4.3 小結 24
第五章 財報風險語句偵測系統 25
5.1 設計目的 25
5.2 操作介面 26
5.3 案例分析 29
5.4 小結 31
第六章 結論 32
參考文獻 34
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