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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

以基本面分析建構最適資產配置流程 / Using Fundamental Analysis To Construct The Optimal Asset Allocation Process

蕭鈞銓 Unknown Date (has links)
於現今經濟情勢混沌不明,令人想起價值投資的投資策略方法,期望 在任何環境下,只要篩選出的股票是具有獲益潛力,則可趁勢進場,獲取 超額收益。本論文嘗試以基本面分析為主體建構三步驟的資產配置流程。 第一步驟使用比例交集法進行資產選擇,而多因子方法通常比單因子所篩 選的報酬率更優異,且加上月營收成長率作為篩選條件其報酬率更是亮眼。 再者,第二步驟透過風控管指標選股發現,當採用 GSR 做為資產選擇的條件時,可達到最佳的表現。最後於最適權重的配置之中,資產模型及目標函數會因為不同的資產組合而有不同的效果,其中,當 FCFY(0.1) & ROA(0.2)加上月營收成長率20%做為篩選條件並使用GSR進行二次篩選後,使用 ARMA(1,1)-GARCH(1,1)且目標函數為最大化夏普指標時可達最大報酬。
2

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.

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