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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 石百達 | zh_TW |
dc.contributor.advisor | Pai-Ta Shih | en |
dc.contributor.author | 陳韋翰 | zh_TW |
dc.contributor.author | Wei-Han Chen | en |
dc.date.accessioned | 2023-07-31T16:06:54Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-07-31 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-03 | - |
dc.identifier.citation | [1] Beneish, M. D., Lee, C. M., & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57-82.
[2] Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82. [3] Chen, L., Pelger, M., & Zhu, J. (2019). Deep learning in asset pricing. Available at SSRN 3350138. [4] Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. [5] Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636. [6] Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. [7] Halbritter, G., & Dorfleitner, G. (2015). The wages of social responsibility—where are they? A critical review of ESG investing. Review of Financial Economics, 26, 25-35. [8] Haugen, R. A., & Baker, N. L. (1996). Commonality in the determinants of expected stock returns. Journal of Financial Economics, 41(3), 401-439. [9] Hou, K., Xue, C., & Zhang, L. (2020). Replicating anomalies. The Review of Financial Studies, 33(5), 2019-2133. [10] Kim, J. B., & Zhang, L. (2014). Financial reporting opacity and expected crash risk: Evidence from implied volatility smirks. Contemporary Accounting Research, 31(3), 851-875. [11] Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197. [12] Lewellen, J. (2015). The cross section of expected stock returns. Critical Finance Review, 4(1), 1-44. [13] Moskowitz, T. J., & Grinblatt, M. (1999). Do industries explain momentum?. The Journal of Finance, 54(4), 1249-1290. [14] Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 109-131. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87884 | - |
dc.description.abstract | 本研究建立三個分別包含3、6及13個公司特徵因子的模型,模擬一個投資人在真實世界中如何運用當下能取得的公開資訊進行Fama-Macbeth迴歸分析,並以迴歸計算之係數預測股價的期望報酬率。研究結果顯示,本研究之模型所計算的期望報酬率能在橫斷面上顯著的解釋股價的真實報酬率。同時,期望報酬率投資策略所建構的做多及多空避險投組在常用來衡量績效的平均月報酬率及Sharpe ratio等面向上皆有非常良好的表現。在臺股市場實際應用上,即使加入交易稅及手續費的衝擊,投資組合仍有遠勝大盤的績效表現,且能透過交疊數個月的投資組合進一步縮減策略的換股率及交易成本。最後,本研究的結果在不同子樣本及迴歸期間下普遍不受太大影響,且在Fama-French三因子迴歸的檢定下有非常優異的超額報酬,顯示本研究結果的穩健性。 | zh_TW |
dc.description.abstract | This study establishes three models, each containing 3, 6, and 13 firm characteristics or factors respectively, to simulate how an investor can utilize publicly available information in the real world for Fama-Macbeth regression analysis. The regression coefficients are then used to predict the expected returns of stocks. The results of the study show that the expected returns calculated by the models can significantly explain the real returns of stocks in the cross-section. Furthermore, the expected-return-sorted long-short portfolios exhibit excellent performance in various aspects, such as average monthly return and Sharpe ratio. In the actual application in the Taiwan stock market, even with the impact of taxes and transaction costs, the portfolios still outperform the market index, and the turnover rate and transaction costs of the strategy can be further reduced by overlapping. Finally, the results of this study are robust across different subsamples and regression rolling years, and exhibit excellent excess returns based on the Fama-French three-factor regression, indicating the robustness of the study findings. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-07-31T16:06:54Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-07-31T16:06:54Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 表目錄 vi 圖目錄 vii 第一章 緒論 1 第二章 文獻回顧 3 2.1 因子模型相關研究 3 2.2 使用Fama-Macbeth迴歸分析的橫斷面研究 4 第三章 樣本資料、模型設定及研究方法 6 3.1 樣本資料 6 3.2 模型及變數設定 6 3.2.1 因子介紹 6 3.2.2 模型設定 8 3.3 研究方法 8 第四章 實證結果 10 4.1 Fama-Macbeth迴歸分析 10 4.1.1 因子對股價報酬率的解釋力 10 4.1.2 股價期望報酬率的表現 11 4.2 期望報酬率投資策略 12 4.3 不同長短的Fama-Macbeth迴歸期間窗口 13 4.4 臺灣股票市場實際應用探討 14 4.5 穩健性測試 16 第五章 結論 17 參考文獻 18 表目錄 表1 敘述統計表 20 表2 因子的Fama-Macbeth迴歸分析 21 表3 期望報酬率對真實報酬率的解釋力 22 表4 投資策略績效比較 23 表5 期望報酬率投資策略績效 24 表6 不同迴歸期間窗口的期望報酬率解釋力 25 表7 不同迴歸期間窗口的投資組合績效 27 表8 投資組合換股率 28 表9 加入交易成本的期望報酬率投資策略績效 28 表10 時間多角化後的期望報酬率投資策略績效 29 表11 穩健性測試 31 圖目錄 圖1 投資策略績效比較 23 | - |
dc.language.iso | zh_TW | - |
dc.title | 臺灣股票市場的橫斷面期望報酬率 | zh_TW |
dc.title | Cross-Sectional Expected Return of Taiwan Stock Market | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 盧佳琪;洪偉峰 | zh_TW |
dc.contributor.oralexamcommittee | Chia-Chi Lu;Wei-Feng Hung | en |
dc.subject.keyword | 因子模型,Fama-Macbeth迴歸分析,期望報酬率,橫斷面實證研究,臺灣股票市場, | zh_TW |
dc.subject.keyword | factor model,Fama-Macbeth regression,expected return,cross-sectional empirical research,Taiwan stock market, | en |
dc.relation.page | 31 | - |
dc.identifier.doi | 10.6342/NTU202301260 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-07-04 | - |
dc.contributor.author-college | 管理學院 | - |
dc.contributor.author-dept | 財務金融學系 | - |
顯示於系所單位: | 財務金融學系 |
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