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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李賢源 | |
dc.contributor.author | Yen-Yi Tsai | en |
dc.contributor.author | 蔡嚴毅 | zh_TW |
dc.date.accessioned | 2021-06-08T02:39:03Z | - |
dc.date.copyright | 2018-07-06 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-07-06 | |
dc.identifier.citation | 1. Ang, A. and Bekaert, G. (2006). Stock Return Predictability: Is it There?. Review of Financial Studies, 20(3), pp.651-707.
2. Bekaert, G. and Hodrick, R. (1992). Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets. The Journal of Finance, 47(2), p.467. 3. Bollerslev, T., Xu, L. and Zhou, H. (2012). Stock Return and Cash Flow Predictability: The Role of Volatility Risk. SSRN Electronic Journal. 4. Campbell, J. and Cochrane, J. (1999). By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior. Journal of Political Economy, 107(2), pp.205-251. 5. Campbell, J. and HAMAO, Y. (1992). Predictable Stock Returns in the United States and Japan: A Study of Long-Term Capital Market Integration. The Journal of Finance, 47(1), pp.43-69. 6. Campbell, J. and Thompson, S. (2007). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?. Review of Financial Studies, 21(4), pp.1509-1531. 7. Chang, S., Chen, S., Chou, R. and Lin, Y. (2012). Local sports sentiment and returns of locally headquartered stocks: A firm-level analysis. Journal of Empirical Finance, 19(3), pp.309-318. 8. Dangl, T. and Halling, M. (2012). Predictive regressions with time-varying coefficients. Journal of Financial Economics, 106(1), pp.157-181. 9. Fama, E. and French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), pp.3-56. 10. Fama, E. and French, K. (2000). Forecasting Profitability and Earnings. The Journal of Business, 73(2), pp.161-175. 11. Ferson, W. and Harvey, C. (1994). Sources of risk and expected returns in global equity markets. Journal of Banking & Finance, 18(4), pp.775-803. 12. Ferson, W. and Harvey, C. (1999). Economic, Financial and Fundamental Global Risk In and Out of the EMU. SSRN Electronic Journal. 13. Giot, P. and Petitjean, M. (2006). International Stock Return Predictability: Statistical Evidence and Economic Significance. SSRN Electronic Journal. 14. Graham, B. and Dodd, D. (2009). Security analysis. New York: McGraw-Hill. 15. Guidolin, M., McMillan, D. and Wohar, M. (2013). Time varying stock return predictability: Evidence from US sectors. Finance Research Letters, 10(1), pp.34-40. 16. Henkel, S., Martin, J. and Nardari, F. (2011). Time-varying short-horizon predictability☆. Journal of Financial Economics, 99(3), pp.560-580. 17. Hjalmarsson, E. (2008). Predicting Global Stock Returns. SSRN Electronic Journal. 18. Lawrenz, J. and Zorn, J. (2018). Decomposing the Predictive Power of Local and Global Financial Valuation Ratios. The Quarterly Review of Economics and Finance. 19. Lettau, M. and Ludvigson, S. (1999). Resurrecting the (C)CAPM: A Cross-Sectional Test When Risk Premia are Time-Varying. SSRN Electronic Journal. 20. Lettau, M. and Van Nieuwerburgh, S. (2007). Reconciling the Return Predictability Evidence. Review of Financial Studies, 21(4), pp.1607-1652. 21. Neely, C., Rapach, D., Tu, J. and Zhou, G. (2011). Forecasting the Equity Risk Premium: The Role of Technical Indicators. SSRN Electronic Journal. 22. Paye, B. and Timmermann, A. (2006). Instability of return prediction models. Journal of Empirical Finance, 13(3), pp.274-315. 23. Pettenuzzo, D., Timmermann, A. and Valkanov, R. (2014). Forecasting stock returns under economic constraints. Journal of Financial Economics, 114(3), pp.517-553. 24. Poterba, J. and Summers, L. (1988). Mean reversion in stock prices. Journal of Financial Economics, 22(1), pp.27-59. 25. Rapach, D., Ringgenberg, M. and Zhou, G. (2016). Short interest and aggregate stock returns. Journal of Financial Economics, 121(1), pp.46-65. 26. Rapach, D., Strauss, J. and Zhou, G. (2012). International Stock Return Predictability: What is the Role of the United States?. SSRN Electronic Journal. 27. Schrimpf, A. (2010). International Stock Return Predictability under Model Uncertainty. SSRN Electronic Journal. 28. Solnik, B. (1993). The performance of international asset allocation strategies using conditioning information. Journal of Empirical Finance, 1(1), pp.33-55. 29. Wang, Y., Liu, L., Ma, F. and Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45, pp.141-156. 30. Welch, I. and Goyal, A. (2007). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. Review of Financial Studies, 21(4), pp.1455-1508. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20033 | - |
dc.description.abstract | 早在Graham and Dodd, 1934. Book “Security Analysis”一書中即顯示價值投資之重要性。股價旨在反映公司之真實價值,若有所偏離長期應調整至真實價值的平均水位。本篇文章使用個股P/E ratio過去之歷史平均與亞太區同產業之平均做為限制,並根據訊號放射的原理建立預測模型,並提升模型預測的精準度,內文中稱其為提升之精準程度為預測能力增益。
以個股過去平均以及區域內平均作為限制,能夠簡單且有效的提升模型預測的有效性。此外加入亞太區域內產業的平均值,來避免個股間不同產業之差異。判別個股目前是同時低於過去歷史平均與亞太區域內產業平均,抑或是同時高於上述兩者平均值。偏離兩者平均值是同向的,那麼未來個股報酬的預測方向較為明確,實證的預測能力增益明顯;但若個股P/E ratio介於兩者平均值之間,那未來個股報酬不易預測,實證的預測能力增益低落。 實證發現,根據產業特性及預測期間長短的不同,預測能力之增益在各個產業之間有所改變。在景氣循環週期較長的產業中,如:水泥業、橡膠、電機機械、紡織業、造紙業、汽車業、鋼鐵業。實證結果發現需要較長的時間來使其報酬調整至平均值,因此短期的預測增益效果低落。然而在以季、半年為中長期的預測當中此產業的預測增益效果非常顯著,預測能力較使用benchmark之預測模型增加了近20%。而在景氣循環週期較短,波動較大的產業中,如:金融業、電子業、塑化業、玻璃業。實證發現預測增益在中短期(15天左右)達到高峰。以金融業為例,在預測累積15日報酬時採取本篇研究方法,預測模型增益可達近30%以上。 關鍵字:預測報酬、台股、景氣循環、本益比、基本面因子 | zh_TW |
dc.description.abstract | Many Researchers had discussed how to improve the ability of prediction on return predictability of individual stock. Among those research, Fundamental indexes are frequently used, as a proxy of company’s quality. Fundamental indexes aim to reflect how well a company operate, how steady a company’s financial situation are, and how far a company’s prospect can go. If these indexes do reflect a company’s value, then this should pass on to the company’s stock price someday. This thesis is based on those research, and further improve the predictability of predict model. Domestic and foreign investor take fundamental indexes into account while allocate asset, hence fundamental indexes are regarded as an important reference. For example, J.P. Morgan’s guide to the market are provided to investor annually. And this guide includes fundamental indexes from country to country. This thesis imports Asia’s regional fundamental indexes as control variable, and found that this could actually improve model predictability.
We distinguish individual stock by 17 industries, then we can compare the stock to regional average. Further, each stock will compare to it’s own historical average. If the stock’s fundamental index ratio were below historical and regional average at the same time, it shows that the stock might be undervalued; while beyond those, the stock might be overvalued, and in the long run, the stock’s price should adjust to the average. And we further improve the predictability of model by considering restriction model and prosperity of economic. keywords:return predictability, stock, fundamental index, business cycle, pe ratio | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:39:03Z (GMT). No. of bitstreams: 1 ntu-107-R05723050-1.pdf: 4156909 bytes, checksum: e49de7c27de6b41cff500c1d8d92e0cc (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員審定書 i
摘要 ii Abstract iii 目錄 iv 一、 緒論 1 二、文獻探討 2 第一節:運用Cross-Sectional基本面指標是否能增進預測能力 2 第二節:運用Historical指標能否增進預測能力 3 第三節:運用基本面指標會有mean-revering的想法建立預測模型 3 第四節:進一步改善模型的預測能力 4 三、研究方法 6 第一節 資料來源與研究期間 6 一、 資料來源 6 二、 研究時間 6 第二節 研究方法與流程 6 一、 股票分群 6 二、 建立回測預測模型 7 三、 Switching model 9 四、 衡量樣本外預測能力 13 五、流程圖 14 四、實證結果 15 第一節 日報酬預測 15 一、Consistent類別預測 16 二、Opposing類別預測 27 三、Reverse類別預測 31 小結 35 第二節 月報酬預測 36 一、Consistent類別預測 37 二、Reverse類別預測 41 小結 47 五、結論與建議 48 第一節 結論 48 第二節 後續研究建議 49 參考文獻 51 附表一 Consistent類別日資料預測 54 附表二 Reverse類別日資料預測 55 附表三 Opposing類別日資料預測 56 附表四 Consistent類別月資料預測 57 附表五 Opposing類別月資料預測 58 附表六 Reverse類別月資料預測 59 | |
dc.language.iso | zh-TW | |
dc.title | 產業類股增進預測能力 | zh_TW |
dc.title | Enhance Return Predictability by Industry | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡偉澎,李宗培 | |
dc.subject.keyword | 預測報酬,台股,景氣循環,本益比,基本面因子, | zh_TW |
dc.subject.keyword | return predictability,stock,fundamental index,business cycle,pe ratio, | en |
dc.relation.page | 59 | |
dc.identifier.doi | 10.6342/NTU201801326 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2018-07-06 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
顯示於系所單位: | 財務金融學系 |
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