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題名 以技術指標建構市場指標投資台灣股票市場
The Optimal Asset Allocation in Taiwan Stock Market: Using Technical Analysis as Market Indicator
作者 賴欣沅
Lai, Hsin Yuan
貢獻者 黃泓智
賴欣沅
Lai, Hsin Yuan
關鍵詞 技術指標
綜合信號指標
資產配置
Regular Vine Copula
Technical Indicator
Combined Signal Approach
Asset Allocation
Regular Vine Copula
日期 2015
上傳時間 13-Jul-2015 11:09:49 (UTC+8)
摘要 許多新興風險隨著金融市場的變化而產生，以致於發生許多大型金融災害造成許多金融產業蒙受鉅額損失。而於金融市場尋求利潤已是金融產業重要的一環，有鑑於此，本論文提出ㄧ套完整的資產配置流程，利用技術指標建構綜合信號指標作為市場指標再選擇投資資產並估計、模擬、最適化投資權重並投資，以達到規避大型金融事件風險並獲取超額利潤。本論文亦嘗試不同股票評分指標、股票資產模型、結構模型、投資組合大小等組合，以找出最適合台灣股票支股票評分指標、資產模型以及投資組合大小。
本論文發現綜合信號指標作為市場指標可有效判讀金融事件的發生與結束時間，經由此指標判斷可獲得相當的超額利潤。本論文亦發現當投資組合為5支股票、資產模型為GJR GARCH(1,1)模型、相關結構型態為多元高斯Copula時可獲得超額利潤。
參考文獻  Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182-198.
 Bertrand, P., & Prigent, J.-l. (2011). Omega performance measure and portfolio insurance. Journal of Banking & Finance, 35(7), 1811-1823.
 Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2), 1004-1017.
 Esfahanipour, A., & Mousavi, S. (2011). A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Systems with Applications, 38(7), 8438-8445.
 Friesen, G. C., Weller, P. A., & Dunham, L. M. (2009). Price trends and patterns in technical analysis: A theoretical and empirical examination. Journal of Banking & Finance, 33(6), 1089-1100.
 Hitaj, A., Corazza, M., & Pizzi, C. (2014). Portfolio allocation using Omega function: An empirical analysis Mathematical and Statistical Methods for Actuarial Sciences and Finance.
 Ingersoll, J., Spiegel, M., Goetzmann, W., & Welch, I. (2007). Portfolio Performance Manipulation and Manipulation-proof Performance Measures. The Review of Financial Studies, 20(5), 1503-1546.
 Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567-581.
 Leigh, W., Purvis, R., & Ragusa, J. M. (2002). Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems, 32(4), 361-377.
 Lento, C. (2008). A Combined Signal Approach to Technical Analysis on the S&P 500. Rochester: Social Science Research Network.
 Lento, C. (2009). Combined signal approach: evidence from the Asian–Pacific equity markets. Applied Economics Letters, 16(7), 749-753.
 Levich, R. M., & Thomas Iii, L. R. (1993). The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach. Journal of International Money and Finance, 12(5), 451-474.
 Mohanram, P. (2005). Separating Winners from Losers among LowBook-to-Market Stocks using Financial Statement Analysis. Review of Accounting Studies, 10(2-3), 133-170.
 Neely, C. J., & Weller, P. A. (1999). Technical trading rules in the European Monetary System. Journal of International Money and Finance, 18(3), 429-458.
 Neely, C. J., & Weller, P. A. (2003). Intraday technical trading in the foreign exchange market. Journal of International Money and Finance, 22(2), 223-237.
 Potvin, J.-Y., Soriano, P., & Vallée, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 31(7), 1033-1047.
 Rossello, D. (2015). Ranking of investment funds: Acceptability versus robustness. European Journal of Operational Research, 245(3), 828-836. Wang, J.-L., & Chan, S.-H. (2007). Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(2), 304-315.
 Zakamouline, V., & Koekebakker, S. (2009). Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance. Journal of Banking & Finance, 33(7), 1242-1254.
 Lento, Camillo, Tests of Technical Trading Rules in the Asian-Pacific Equity Markets: A Bootstrap Approach. Academy of Financial and Accounting Studies Journal, Vol. 11, No. 2, 2007.
描述 碩士
國立政治大學
風險管理與保險研究所
102358021
103
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102358021
資料類型 thesis
dc.contributor.advisor 黃泓智zh_TW
dc.contributor.author (Authors) 賴欣沅zh_TW
dc.contributor.author (Authors) Lai, Hsin Yuanen_US
dc.creator (作者) 賴欣沅zh_TW
dc.creator (作者) Lai, Hsin Yuanen_US
dc.date (日期) 2015en_US
dc.date.accessioned 13-Jul-2015 11:09:49 (UTC+8)-
dc.date.available 13-Jul-2015 11:09:49 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2015 11:09:49 (UTC+8)-
dc.identifier (Other Identifiers) G0102358021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76437-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 風險管理與保險研究所zh_TW
dc.description (描述) 102358021zh_TW
dc.description (描述) 103zh_TW
dc.description.abstract (摘要) 許多新興風險隨著金融市場的變化而產生，以致於發生許多大型金融災害造成許多金融產業蒙受鉅額損失。而於金融市場尋求利潤已是金融產業重要的一環，有鑑於此，本論文提出ㄧ套完整的資產配置流程，利用技術指標建構綜合信號指標作為市場指標再選擇投資資產並估計、模擬、最適化投資權重並投資，以達到規避大型金融事件風險並獲取超額利潤。本論文亦嘗試不同股票評分指標、股票資產模型、結構模型、投資組合大小等組合，以找出最適合台灣股票支股票評分指標、資產模型以及投資組合大小。
本論文發現綜合信號指標作為市場指標可有效判讀金融事件的發生與結束時間，經由此指標判斷可獲得相當的超額利潤。本論文亦發現當投資組合為5支股票、資產模型為GJR GARCH(1,1)模型、相關結構型態為多元高斯Copula時可獲得超額利潤。		zh_TW
dc.description.tableofcontents 第一章 、緒論 1
第二章 、文獻回顧 3
第一節 、技術指標文獻 3
第二節 、財務報表及股票評分文獻 4
第三節 、資產模型文獻 5
第三章 、研究方法 8
第一節 、前言 8
第二節 、技術指標簡介及使用方法 8
第三節 、資產選擇 13
第四節 、資產模型 17
第五節 、蒙地卡羅模擬與最適化目標函數 19
第四章 、實驗結果 21
第一節 、前言 21
第二節 、不同門檻值基金淨值 21
第三節 、不同股票評量指標基金淨值 29
第四節 、不同資產模型基金淨值 30
第五節 、不同投資組合大小基金淨值 31
第六節 、不同相關結構基金淨值 32
第五章 、結論及未來建議 34
參考資料 35
附錄一、圖形型態判斷方法 38
附錄二、綜合信號最適化權重 40		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102358021en_US
dc.subject (關鍵詞) 技術指標zh_TW
dc.subject (關鍵詞) 綜合信號指標zh_TW
dc.subject (關鍵詞) 資產配置zh_TW
dc.subject (關鍵詞) Regular Vine Copulazh_TW
dc.subject (關鍵詞) Technical Indicatoren_US
dc.subject (關鍵詞) Combined Signal Approachen_US
dc.subject (關鍵詞) Asset Allocationen_US
dc.subject (關鍵詞) Regular Vine Copulaen_US
dc.title (題名) 以技術指標建構市場指標投資台灣股票市場zh_TW
dc.title (題名) The Optimal Asset Allocation in Taiwan Stock Market: Using Technical Analysis as Market Indicatoren_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻)  Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182-198.
 Bertrand, P., & Prigent, J.-l. (2011). Omega performance measure and portfolio insurance. Journal of Banking & Finance, 35(7), 1811-1823.
 Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2), 1004-1017.
 Esfahanipour, A., & Mousavi, S. (2011). A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Systems with Applications, 38(7), 8438-8445.
 Friesen, G. C., Weller, P. A., & Dunham, L. M. (2009). Price trends and patterns in technical analysis: A theoretical and empirical examination. Journal of Banking & Finance, 33(6), 1089-1100.
 Hitaj, A., Corazza, M., & Pizzi, C. (2014). Portfolio allocation using Omega function: An empirical analysis Mathematical and Statistical Methods for Actuarial Sciences and Finance.
 Ingersoll, J., Spiegel, M., Goetzmann, W., & Welch, I. (2007). Portfolio Performance Manipulation and Manipulation-proof Performance Measures. The Review of Financial Studies, 20(5), 1503-1546.
 Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567-581.
 Leigh, W., Purvis, R., & Ragusa, J. M. (2002). Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems, 32(4), 361-377.
 Lento, C. (2008). A Combined Signal Approach to Technical Analysis on the S&P 500. Rochester: Social Science Research Network.
 Lento, C. (2009). Combined signal approach: evidence from the Asian–Pacific equity markets. Applied Economics Letters, 16(7), 749-753.
 Levich, R. M., & Thomas Iii, L. R. (1993). The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach. Journal of International Money and Finance, 12(5), 451-474.
 Mohanram, P. (2005). Separating Winners from Losers among LowBook-to-Market Stocks using Financial Statement Analysis. Review of Accounting Studies, 10(2-3), 133-170.
 Neely, C. J., & Weller, P. A. (1999). Technical trading rules in the European Monetary System. Journal of International Money and Finance, 18(3), 429-458.
 Neely, C. J., & Weller, P. A. (2003). Intraday technical trading in the foreign exchange market. Journal of International Money and Finance, 22(2), 223-237.
 Potvin, J.-Y., Soriano, P., & Vallée, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 31(7), 1033-1047.
 Rossello, D. (2015). Ranking of investment funds: Acceptability versus robustness. European Journal of Operational Research, 245(3), 828-836. Wang, J.-L., & Chan, S.-H. (2007). Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(2), 304-315.
 Zakamouline, V., & Koekebakker, S. (2009). Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance. Journal of Banking & Finance, 33(7), 1242-1254.
 Lento, Camillo, Tests of Technical Trading Rules in the Asian-Pacific Equity Markets: A Bootstrap Approach. Academy of Financial and Accounting Studies Journal, Vol. 11, No. 2, 2007.		zh_TW