學術產出-Theses
Article View/Open
Publication Export
-
題名 分析師樣本公司之因子模型 : 台灣市場實證分析
Factor model of analyst forecasting companies : an empirical analysis of Taiwan market作者 阮彥勳
Juan, Yen Hsun貢獻者 林士貴
阮彥勳
Juan, Yen Hsun關鍵詞 統計套利
因子模型
分析師歧異度
投資組合策略
Statistical arbitrage
Factor model
Analyst dispersion
Portfolio日期 2017 上傳時間 11-Jul-2017 11:29:55 (UTC+8) 摘要 研究使用2000~2016年台灣證券交易所1887家公司，包含所有上下市櫃分析師預測公司，分析師預測資料除研究常用之盈餘預測外，亦將營收、毛利與毛利率等預測項目納入研究，此外加入額外因子，如:規模因子、淨值市價比因子、系統性風險因子、非流動性因子等進行多因子研究，使用Fama and French(1992)之Fama Macbeth迴歸模型，進行時間序列與橫斷面迴歸測驗，檢驗各因子之有效性，最終依據各績效評估因子決定出最適之投資組合，並附上各因子組合之權益曲線與績效。 實證結果發現，在台灣分析師樣本公司中，分析師歧異度、短期動能與長期動能三因子的影響較為顯著，分析師預期歧異度較高的公司未來預期報酬相對低於分析師預期歧異度較低的公司，而短期動能與長期動能較強的公司相較於短期動能與長期動能較弱的公司，擁有較高之未來預期報酬，以此三因子構建之投資組合，在2000~2016年間夏普值達0.78；而Fama and French使用的三因子在此樣本空間解釋力並不顯著，非流動性因子亦不顯著。
This paper used the 1887 companies in Taiwan from 2000 to 2016, including all the analysts forecasting listed and delisted companies in either exchange market or over-the-counter market. The data of analyst’s prediction not only used the earnings forecast, but also revenue, gross profit and gross profit forecast in this research. In addition, other factors such as size factor, B/M factor, systemic risk factor, non-liquidity factor were used in this study. This paper used the Fama Macbeth regression model, which contains both time series and cross section Regression test, test the effectiveness of each factor, and ultimately based on the performance factor to determine the optimal portfolio, and finally obtain the equity curve and performance of the combination with various factors. The empirical results show that the analyst`s earning dispersion, short-term momentum and long-term momentum three factors are more significant in the analyst forecasting companies in Taiwan. Companies with higher degree of earning prediction dispersion have relatively lower return in the future, and companies with higher short-term momentum and long-term momentum have a higher expected return. Build a portfolio with the three factor in 2000~2016 could obtain 0.88 Sharpe ratio! Neither Fama and French three factors nor non-liquidity factor in this sample space is significant.參考文獻 [1] Abarbanell, J. S. (1991). Do analysts` earnings forecasts incorporate information in prior stock price changes?. Journal of Accounting and Economics, 14(2), 147-165.[2] Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.[3] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929-985.[4] Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18.[5] Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.[6] Choi, J. H., Kalay, A., & Sadka, G. (2016). Earnings news, expected earnings, and aggregate stock returns. Journal of Financial Markets, 29, 110-143.[7] Fama, Eugene F.; French, Kenneth R. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 1993, 33 (1): 3–56.[8] Gleason, C. A., & Lee, C. M. (2003). Analyst forecast revisions and market price discovery. The Accounting Review, 78(1), 193-225..[9] Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881-898.[10] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.[11] Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of finance, 32(4), 1151-1168.[12] Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive evidence of market inefficiency. The Journal of Portfolio Management, 11(3), 9-16.[13] Veenman, D., & Verwijmeren, P. (2015). Earnings Expectations and the Dispersion Anomaly. Working Paper.[14] Jorgensen, B., Li, J., & Sadka, G. (2012). Earnings dispersion and aggregate stock returns. Journal of Accounting and Economics, 53(1), 1-20.[15] 李佳玲. " 分析師預測歧異度與修正量對股權價值評估之攸關性, 靜宜大學財務金融學系碩士論文." (2014).[16] 陳榮昌. "台灣股票報酬之結構分析, 國立中山大學財務管理學系碩士論文." (2002). 描述 碩士
國立政治大學
金融學系
104352010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104352010 資料類型 thesis dc.contributor.advisor 林士貴 zh_TW dc.contributor.author (Authors) 阮彥勳 zh_TW dc.contributor.author (Authors) Juan, Yen Hsun en_US dc.creator (作者) 阮彥勳 zh_TW dc.creator (作者) Juan, Yen Hsun en_US dc.date (日期) 2017 en_US dc.date.accessioned 11-Jul-2017 11:29:55 (UTC+8) - dc.date.available 11-Jul-2017 11:29:55 (UTC+8) - dc.date.issued (上傳時間) 11-Jul-2017 11:29:55 (UTC+8) - dc.identifier (Other Identifiers) G0104352010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110799 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 104352010 zh_TW dc.description.abstract (摘要) 研究使用2000~2016年台灣證券交易所1887家公司，包含所有上下市櫃分析師預測公司，分析師預測資料除研究常用之盈餘預測外，亦將營收、毛利與毛利率等預測項目納入研究，此外加入額外因子，如:規模因子、淨值市價比因子、系統性風險因子、非流動性因子等進行多因子研究，使用Fama and French(1992)之Fama Macbeth迴歸模型，進行時間序列與橫斷面迴歸測驗，檢驗各因子之有效性，最終依據各績效評估因子決定出最適之投資組合，並附上各因子組合之權益曲線與績效。 實證結果發現，在台灣分析師樣本公司中，分析師歧異度、短期動能與長期動能三因子的影響較為顯著，分析師預期歧異度較高的公司未來預期報酬相對低於分析師預期歧異度較低的公司，而短期動能與長期動能較強的公司相較於短期動能與長期動能較弱的公司，擁有較高之未來預期報酬，以此三因子構建之投資組合，在2000~2016年間夏普值達0.78；而Fama and French使用的三因子在此樣本空間解釋力並不顯著，非流動性因子亦不顯著。 zh_TW dc.description.abstract (摘要) This paper used the 1887 companies in Taiwan from 2000 to 2016, including all the analysts forecasting listed and delisted companies in either exchange market or over-the-counter market. The data of analyst’s prediction not only used the earnings forecast, but also revenue, gross profit and gross profit forecast in this research. In addition, other factors such as size factor, B/M factor, systemic risk factor, non-liquidity factor were used in this study. This paper used the Fama Macbeth regression model, which contains both time series and cross section Regression test, test the effectiveness of each factor, and ultimately based on the performance factor to determine the optimal portfolio, and finally obtain the equity curve and performance of the combination with various factors. The empirical results show that the analyst`s earning dispersion, short-term momentum and long-term momentum three factors are more significant in the analyst forecasting companies in Taiwan. Companies with higher degree of earning prediction dispersion have relatively lower return in the future, and companies with higher short-term momentum and long-term momentum have a higher expected return. Build a portfolio with the three factor in 2000~2016 could obtain 0.88 Sharpe ratio! Neither Fama and French three factors nor non-liquidity factor in this sample space is significant. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究動機 11.2 研究目的 2第二章 文獻回顧 42.1 分析師預測因子 42.1.1 分析師預測歧異度因子 42.1.2 分析師預測修正因子 52.2 Fama French三因子模型 52.3 動能因子與反轉效應 52.4流動性因子 6第三章 研究方法 73.1 Fama Macbeth Regession: 73.2 新增因子簡介與計算方法: 93.3投資組合構建方法: 103.4投資組合績效評估因子: 11第四章 實證分析 124.1 樣本公司資料描述: 124.2 樣本公司各因子之敘述統計: 154.3 因子分群結果: 174.4 Fama Macbeth Regression多因子結果 284.5 投資組合結果與績效: 314.6 將資料區間去除2011~2013年 344.7 穩健性測試: 364.7.1 更改分析師預測點數濾網: 364.7.2 放空公司組合改為放空台灣加權指數期貨: 37第五章 結論 38參考文獻 40 zh_TW dc.format.extent 1033222 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104352010 en_US dc.subject (關鍵詞) 統計套利 zh_TW dc.subject (關鍵詞) 因子模型 zh_TW dc.subject (關鍵詞) 分析師歧異度 zh_TW dc.subject (關鍵詞) 投資組合策略 zh_TW dc.subject (關鍵詞) Statistical arbitrage en_US dc.subject (關鍵詞) Factor model en_US dc.subject (關鍵詞) Analyst dispersion en_US dc.subject (關鍵詞) Portfolio en_US dc.title (題名) 分析師樣本公司之因子模型 : 台灣市場實證分析 zh_TW dc.title (題名) Factor model of analyst forecasting companies : an empirical analysis of Taiwan market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Abarbanell, J. S. (1991). Do analysts` earnings forecasts incorporate information in prior stock price changes?. Journal of Accounting and Economics, 14(2), 147-165.[2] Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.[3] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929-985.[4] Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18.[5] Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.[6] Choi, J. H., Kalay, A., & Sadka, G. (2016). Earnings news, expected earnings, and aggregate stock returns. Journal of Financial Markets, 29, 110-143.[7] Fama, Eugene F.; French, Kenneth R. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 1993, 33 (1): 3–56.[8] Gleason, C. A., & Lee, C. M. (2003). Analyst forecast revisions and market price discovery. The Accounting Review, 78(1), 193-225..[9] Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881-898.[10] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.[11] Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of finance, 32(4), 1151-1168.[12] Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive evidence of market inefficiency. The Journal of Portfolio Management, 11(3), 9-16.[13] Veenman, D., & Verwijmeren, P. (2015). Earnings Expectations and the Dispersion Anomaly. Working Paper.[14] Jorgensen, B., Li, J., & Sadka, G. (2012). Earnings dispersion and aggregate stock returns. Journal of Accounting and Economics, 53(1), 1-20.[15] 李佳玲. " 分析師預測歧異度與修正量對股權價值評估之攸關性, 靜宜大學財務金融學系碩士論文." (2014).[16] 陳榮昌. "台灣股票報酬之結構分析, 國立中山大學財務管理學系碩士論文." (2002). zh_TW
