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1	文字背後的意含-資訊的量化測量公司基本面與股價（以中鋼為例） / Behind the words - quantifying information to measure firms' fundamentals and stock return (taking the China steel corporation as example) 傅奇珅, Fu, Chi Shen Unknown Date (has links) 本研究蒐集經濟日報、聯合報、與聯合晚報的新聞文章，以中研院的中文斷詞性統進行結構性的處理，參考並延伸Tetlock、Saar-Tsechansky和Macskassy(2008)的研究方法，檢驗使用一個簡單的語言量化方式是否能夠用來解釋與預測個別公司的會計營收與股票報酬。有以下發現： 1. 正面詞彙(褒義詞)在新聞報導中的比例能夠預測高的公司營收。 2. 公司的股價對負面詞彙(貶義詞)有過度反應的現象，對正面詞彙(褒義詞)則有效率地充分反應。綜合以上發現，本論文得到，新聞媒體的文字內容能夠捕捉到一些關於公司基本面難以量化的部份，而投資者迅速地將這些資訊併入股價。 / This research collects all of the news stories about China Steel Corporation from Economic Daily News, United Daily News, and United Evening News. These articles I collect are segmented by a Chinese Word Segmentation System of Academia Sinica and used by the methodology of Tetlock, Saar-Tsechansky, and Macskassy(2008). I examine whether a simple quantitative measure fo language can be used to predict individual firms’ accounting sales and stock returns. My two main findings are: 1. the fraction of positive words (commendatory term) in firm-specific news stories forecasts high firm sales; 2. firm’s stock prices briefly overreaction to the information embedded in negative words (Derogatory term); on the other hand, firm’s stock prices efficiently incorporate the information embedded in positive words (commendatory term). All of the above, we conclude this linguistic media content captures otherwise hard-toquantify aspects of firms’ fundamentals, which investors quickly incorporate into stock prices. 內容分析法文字資訊資訊內涵文件資料探勘關鍵資訊擷取資訊效果褒義詞貶義詞正面詞彙負面詞彙基本面分析股票報酬分析 Content Analysis Textual Information Informative Content Text Mining Information Effect Critical Information Extraction Commendatory Term Derogatory Term Positive words Negative words Fundamental Analysis Stock Return Analysis

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文字背後的意含-資訊的量化測量公司基本面與股價（以中鋼為例） / Behind the words - quantifying information to measure firms' fundamentals and stock return (taking the China steel corporation as example)