Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/135899
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dc.contributor.advisor郭維裕zh_TW
dc.contributor.advisorKuo, Wei-Yuen_US
dc.contributor.author許至豪zh_TW
dc.contributor.authorHsu,Chih-Haoen_US
dc.creator許至豪zh_TW
dc.creatorHsu, Chih-Haoen_US
dc.date2021en_US
dc.date.accessioned2021-07-01T08:07:39Z-
dc.date.available2021-07-01T08:07:39Z-
dc.date.issued2021-07-01T08:07:39Z-
dc.identifierG0108351018en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/135899-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description國際經營與貿易學系zh_TW
dc.description108351018zh_TW
dc.description.abstract本研究以台灣股票市值前百大與半導體樣本為研究對象,研究期間分為年資料與季資料,先透過九個財務比率因子檢測因子與股價之相關性,發現市值前百大樣本中,存貨週轉率與應收帳款週轉率因子對股價沒有相關性,半導體樣本中,研究發展費用率與存貨週轉率對股價沒有相關性,將不具相關性的因子踢除後,再利用剩餘七個財務比率因子建構一個 H-SCORE 分數系統,區分高分群與低分群,針對年資料持有一季、半年與一年並每年換股,季資料持有一個月、二個月與三個月並每季換股,藉此分析不同樣本期間之投資策略與大盤間的關係。\n從實證結果發現:\n1. 在市值前百大樣本年資料與季資料中,高分群公司報酬率都高於低分群公司,證\n實 H-SCORE 基本面財報比率系統有效。但以買高分群賣低分群策略來看,只有年資料有效戰勝大盤,故基本面分析更適合持有期間越長的投資策略。\n2. 在半導體樣本年資料與季資料中,高分群公司報酬率均低於低分群公司,證實 H-SCORE 基本面財報比率系統無效。若採取買高分群賣低分群策略來看,年資料與季資料都無法戰勝大盤,代表半導體公司股價不只受基本面財報比率影響,反而更容易受到消息面、籌碼面與技術面等影響,基本面對於半導體股價反應較慢。\n3. 由實證結果可知,Potroski提出的投資策略指標F-score在台股並非完全適用,只適用於市值前百大公司,對於半導體公司則無效。zh_TW
dc.description.abstractFrom the empirical results:\n1. In the top 100 sample year data and quarterly data of market capitalization, the return rates of high-segment companies are higher than that of low-segment companies, confirming the effectiveness of the H-SCORE fundamental financial reporting ratio system. However, from the perspective of the strategy of buying high scores and selling low scores, only annual data can effectively beat the market, so fundamental analysis is more suitable for investment strategies with longer holding periods.\n2. In the semiconductor sample year data and quarterly data, the return rates of high- segment companies are lower than those of low-segment companies, confirming that the H-SCORE fundamental earnings ratio system is invalid. If the strategy of buying high scores and selling low scores is adopted, neither annual data nor quarterly data can beat the market, which means that the stock price of semiconductor companies is not only affected by the fundamental earnings ratio, but is more likely to be affected by news, bargaining chips, and technical aspects. The reaction to semiconductor stock prices has been slower.\n3. From the empirical results, it can be seen that the investment strategy indicator F-score proposed by Potroski is not completely applicable to Taiwan stocks. It is only applicable to the top 100 companies in market capitalization, and is not valid for semiconductor companies.en_US
dc.description.tableofcontents第一章 緒論 1\n第一節 研究動機 1\n第二節 研究目的 3\n第三節 研究架構 4\n第二章 文獻回顧 5\n第一節 基本面投資策略 5\n第二節 多因子模型 8\n第三節 Score 財務比率因子計分法 10\n第三章 研究方法 12\n第一節 樣本對象 12\n第二節 樣本頻率期間與報酬率之計算 14\n第三節 定義變數 16\n第四節 研究設計與因子檢定 19\n第四章 實證結果 24\n第一節 敘述統計量 24\n第二節 投資策略分析 26\n第三節 投資報酬率圖示 37\n第五章 結論與建議 43\n第一節 研究結論 43\n第二節 研究貢獻 45\n第三節 研究限制與建議 46\n參考文獻 47zh_TW
dc.format.extent1279149 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0108351018en_US
dc.subject基本面分析zh_TW
dc.subjectH-SCOREzh_TW
dc.subject因子檢定zh_TW
dc.subject超額報酬zh_TW
dc.subjectFundamental analysisen_US
dc.subjectH-SCOREen_US
dc.subjectFactor verificationen_US
dc.subjectExcess returnen_US
dc.titleH-SCORE 基本面投資策略在台股市場之應用 -以台灣市值前百大公司與半導體公司為例zh_TW
dc.titleApplication of H-SCORE Fundamental Investment Strategy in Taiwan Stock Market-Take Taiwan’s top 100 companies and semiconductor companiesen_US
dc.typethesisen_US
dc.relation.reference中文部分:\n1. 李雅璇,2013,以巴菲特選股模式建構基本面投資組合:在台股之實證研究,國立中央大學財務金融學研究所碩士學位論文\n2. 林冠宏,2017,因子投資策略之回顧及重現—以台灣股市為例,國立中正大學財務金融研究所碩士學位論文\n3. 周世元,2007,基本分析及會計穩健性與投資報酬之關係,輔仁大學會計學研究所碩士學位論文\n4. 何孟璇,2017,基本面價值投資策略績效: 以益本比為實證,逢甲大學財務金融學系研究所碩士學位論文\n5. 蘇惠玲,2005,台灣股票市場價值股投資策略之探討, 朝陽科技大學會計所碩士學位論文\n6. 沈孟軒,2003,淨值市價比投資策略:財務報表資訊之應用,國立中正大學會計與資訊科技研究所碩士學位論文\n7. 邱庭于,2017,財務比率與股票報酬:以電子產業為例,國立中興大學會計學研究所碩士學位論文\n8. 楊繡瑋,2005,台灣上市公司基本分析與股價之研究-電子業與傳統產業之比較,中國文化大學國際貿易學系碩士班碩士學位論文\n9. 陳彬洲,2015,基本面、籌碼面與總體面對股票報酬影響,亞洲大學財務金融學系碩士在職專班碩士學位論文\n10. 簡嘉怡,2009,台灣電子類股報酬之多因子模型探討,國立屏東科技大學財務金融研究所碩士學位論文\n11. 謝孟廷,2013,基本面指標投資策略在特定產業中的績效評估分析─以台灣電子產業為例,國立臺灣大學國際企業學研究所碩士學位論文\n\n英文部分:\n1. Ali, A., L. Hwang and M. Trombley. 2003. Arbitrage Risk and the Book-to- Market Anomaly. Journal of Financial Economics 69, 355-373\n2. Banz, R. 1981 The Relationship between Return and Market Value of Common Stocks. Journal of Financial Economics 6, 103-126\n3. Beneish, M.D., C.M. Leeand R.L. Tarpley. 2001. Contextual Financial Statement Analysis through the Prediction of Extreme Returns. Review of Accounting Studies 6, 165-189\n4. Campbell R. Harvey, Yan Liu, Heqing Zhu. 2016. and the Cross-Section of Expected Returns, pp6-40\n5. Eugene F. Fama and Kenneth R. French.2014. Dissecting Anomalies with a Five- Factor Model, pp.24-46\n6. Fama, E. F. and French, K. R. 1992. The Cross-Section of Ecpected Stock Returns. Journal of Finance 47, 427-465\n7. Gwilliam schwert.2003. Anomalies and market efficient, pp.941-962\n8. Kewei Hou, Chen Xue, Lu Zhang . Digesting Anomalies: An Investment Approach.pp.2-51\n9. Kewei Hou, Chen Xue, Lu Zhang.2019. Replicating Anomalies, pp.2-18\n10. Mohanram, S.,P. 2005. Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis. Reviw of Accountng Studies 10, 133-170\n11. Monahan, S. Conservatism, Growth and the Role of Accounting Numbers in the\nFundamental Analysis Process, Review of Accounting Studies 10, 227-260\n12. Ou, J., and S. Penman. 1989. Financial Statement Analysis and the Prediction of Stock Returns. Journal of Accounting and Economics 11, 295-329\n13. Piotroski, J. 2000. Value Investing:The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research 38, 1- 41zh_TW
dc.identifier.doi10.6814/NCCU202100563en_US
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