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題名 以機器學習改善實證相似度技術指標交易策略之研究
Adapting machine learning to similarity-based technical trading sstrategies
作者 陳致鈞
貢獻者 江彌修
陳致鈞
關鍵詞 技術分析
技術指標
相似度技術指標交易策略
機器學習
貪婪演算法
模擬淬鍊法
日期 2016
上傳時間 1-Jul-2016 15:00:49 (UTC+8)
摘要 技術面分析是使用過去市場資料包含股票價格與交易量來預測未來市場動態。技術分析將股價與交易量經由數學轉換成易懂且能繪製成圖表的技術分析指標，幫助技術分析投資人預測未來股價。本文的決策過程有別於傳統的技術面分析，使用相似度模型以貼近現實技術分析投資人的決策過程。此策略使用多個技術指標作為相似度技術指標交易策略的依據，用以捕捉市場動態與預測未來股價報酬，且即便不同的技術指標提供不同的買賣訊號，技術分析投資人依然可以藉由相似度技術指標交易策略進行投資決策。相似度技術指標交易策略所預測的未來報酬是根據過往價格圖形出現相似情境的報酬加權平均作為未來預測報酬。當預測報酬為正則買；預測報酬為負則賣。本文使用S&P500指數期貨來檢測相似度技術指標交易策略的獲利能力，發現在不同的技術指標下，相似度技術指標交易策略報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。為使本文相似度技術指標交易策略更能模擬現實投資人的真實情況，導入機器學習改善相似度技術指標交易策略，分別使用貪婪演算法與模擬淬鍊法(Simulated Annealing)來模擬現實投資人會根據交易策略表現的好壞變更決策過程的策略。其報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。本研究發現投資人會參考不同的混合技術指標策略，且會依照不同混合策略的過往績效，篩選出參考策略，進而決定投資策略，這也呼應混合技術指標的相似度技術指標交易策略比單一技術指標的相似度技術指標交易策略擁有較好的預測能力。因此使用混合技術指標的相似度技術指標交易策略作為機器學習篩選的策略可有效的改善原本的相似度技術指標交易策略。
參考文獻 Amos Tversky and Daniel Kahneman, (1973). Availability: A heuristic for judging frequency and probability., Cognitive Psychology 5 (2), 207-232.
Andrei Shynkevich, (2012). Performance of technical analysis in growth and small cap segments of the US equity market., Journal of Banking and Finance 36 (1), 193-208.
Andrew W. Lo, Harry Mamaysky and Jiang Wang, (2000). Foundations of technical analysis:Computational algorithms, statistical inference, and empirical implementation., Journal of Finance 55 (4), 1705-1765.
Ben R. Marshalla, Rochester H. Cahanb and Jared M. Cahana, (2008). Does intraday technical analysis in the U.S. equity market have value?, Journal of Empirical Finance 15 (2), 199-210.
Bo Qian and Khaled Rasheed, (2006). Stock market prediction with multiple classifiers, Applied Intelligence 26 (1), 25-33.
Burton G. Malkiel, (2003). The efficient market hypothesis and its critics., Journal of Economic Perspectives, 59-82.
Charles D. Kirkpatrick II and Julie R. Dahlquist, (2006). Technical Analysis: The Complete Resource for Financial Market Technicians., Financial Times Press, 3.
Cheol-Ho Park and Scott H. Irwin, (2010). A reality check on technical trading rule profits in the U.S. futures markets, Journal of Futures Markets 30 (7), 633-659.
David Hume, (1748). Enquiry into the human understanding, Oxford, Clarendon Press.
Fama, E. Fama and Marshall E. Blume, (1966). Filter rules and stock-market trading.,Journal of Business 39 (1), 226-241.
Francis Nicholson, (1968). Price-earnings ratios in relation to investmente results., Financial Analysts Journal, 105-109.
Gene Savin, Paul Weller and Janis Zvingelis, (2007). The predictive power of “Head-and-Shoulders” price patterns in the U.S. stock market., Journal of Econometrics 5 (2), 243-265.
Halbert White, (2000). A reality check for data snooping., Econometrica 68 (5), 1097-1126.
Itzhak Gilboa and David Schmeidler, (1995). Case-based decision theory., Quarterly Journal of Economics 110 (3), 605-63.
Itzhak Gilboa, Offer Lieberman and David Schmeidler, (2006). Empirical similarity., Review of Economics and Statistics 88 (3), 433-444.
Itzhak Gilboa, Offer Lieberman and David Schmeidler, (2011). A similarity-based approach to prediction., Journal of Econometrics 162 (1), 124-131.
Jeffrey S. Abarbanell and Brian J. Bushee, (1997). Fundamental analysis, future earnings, and stock prices., Journal of Accounting Research 35, no.1, 1-24.
Kosrow Dehnad, (2011). Behavioral finance and technical analysis., Journal of Financial Transformation, vol.32, 107-111.
Lukas Menkhoff, (2010). The use of technical analysis by fund managers: International evidence., Journal of Banking and Finance 34 (11), 2573-2586.
Mark P. Taylor and Helen Allen, (1992). The use of technical analysis in the foreign exchange market., Journal of International Money and Finance 11 (3), 304-314.
Michael C. Jensen and George A. Benington, (1970). Random walks and technical theories: some additional evidence., Journal of Finance 25 (2), 469-482.
Michael Kearns and Yuriy Nevmyvaka, (2013). Machine Learning for Market Microstructure and High Frequency Trading., High Frequency Trading - New Realities for Traders, Markets and Regulators (ed. O’Hara, M., de Prado, M.L. and Easley, D.). London: Risk Books, 91-124.
Peter Reinhard Hansen, (2005). A test for superior predictive ability., Journal of Business and Economic Statistics 23 (4), 365-380.
Pierre Bajgrowicz and Olivier Scaillet, (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs., Journal of Financial Economics 106 (3), 473-491.
Po-Hsuan Hsu and Chung-Ming Kuan, (2005). Reexamining the profitability of technical analysis with data snooping checks., Journal of Financial Econometrics 3 (4), 606-628.
Ramazan Gencay, (1998). Optimization of technical trading strategies and the profitability in security markets., Economics Letters 59 (2), 249-254.
Randall S. Sextona, Robert E. Dorseyb and John D. Johnsonc, (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing., European Journal of Operational Research 144 (3), 589-601.
Wai Mun Fong and Lawrence H. M. Yong, (2005). Chasing trends: Recursive moving average trading rules and internet stocks., Journal of Empirical Finance 12 (1), 43-76.
William Brock, Josef Lakonishok and B lake LeBaron, (1992). Simple technical trading rules and the stochastic properties of stock returns., Journal of Finance 47 (5), 1731-1764.
William Goffe, Gary Ferrier and John Rogers, (1994). Global Optimization of Statistical Functions with Simulated Annealing., Journal of Econometrics 60 (1-2), 65-99.
Wing Keung Wong, Meher Manzur and Boon-Kiat Chew, (2003). How rewarding is technical analysis? Evidence from Singapore stock market., Applied Financial Economics 13 (7), 543-551.
Yingzi Zhu and Guofu Zhou, (2009). Technical analysis: An asset allocation perspective on the use of moving averages., ournal of Financial Economics 92 (3), 519-544.
Yu-Hon Lui and David Mole, (1998). The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence., Journal of International Money and Finance 17 (3), 535-545.
描述 碩士
國立政治大學
金融學系
103352011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103352011
資料類型 thesis
dc.contributor.advisor 江彌修zh_TW
dc.contributor.author (Authors) 陳致鈞zh_TW
dc.creator (作者) 陳致鈞zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 1-Jul-2016 15:00:49 (UTC+8)-
dc.date.available 1-Jul-2016 15:00:49 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2016 15:00:49 (UTC+8)-
dc.identifier (Other Identifiers) G0103352011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98569-
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) 103352011zh_TW
dc.description.abstract (摘要) 技術面分析是使用過去市場資料包含股票價格與交易量來預測未來市場動態。技術分析將股價與交易量經由數學轉換成易懂且能繪製成圖表的技術分析指標，幫助技術分析投資人預測未來股價。本文的決策過程有別於傳統的技術面分析，使用相似度模型以貼近現實技術分析投資人的決策過程。此策略使用多個技術指標作為相似度技術指標交易策略的依據，用以捕捉市場動態與預測未來股價報酬，且即便不同的技術指標提供不同的買賣訊號，技術分析投資人依然可以藉由相似度技術指標交易策略進行投資決策。相似度技術指標交易策略所預測的未來報酬是根據過往價格圖形出現相似情境的報酬加權平均作為未來預測報酬。當預測報酬為正則買；預測報酬為負則賣。本文使用S&P500指數期貨來檢測相似度技術指標交易策略的獲利能力，發現在不同的技術指標下，相似度技術指標交易策略報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。為使本文相似度技術指標交易策略更能模擬現實投資人的真實情況，導入機器學習改善相似度技術指標交易策略，分別使用貪婪演算法與模擬淬鍊法(Simulated Annealing)來模擬現實投資人會根據交易策略表現的好壞變更決策過程的策略。其報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。本研究發現投資人會參考不同的混合技術指標策略，且會依照不同混合策略的過往績效，篩選出參考策略，進而決定投資策略，這也呼應混合技術指標的相似度技術指標交易策略比單一技術指標的相似度技術指標交易策略擁有較好的預測能力。因此使用混合技術指標的相似度技術指標交易策略作為機器學習篩選的策略可有效的改善原本的相似度技術指標交易策略。zh_TW
dc.description.tableofcontents 第一章 緒論 1
第二章 文獻回顧 3
第三章 樣本選取與研究方法 6
第一節 樣本選取 6
一、 樣本來源 6
二、 技術指標分類 7
第二節 研究方法 9
一、 相似度技術指標交易策略建構 9
二、 以機器學習(Machine Learning)改善策略之建構 12
三、 資料探測與檢驗(Data-snooping) 15
第四章 實證結果與分析 19
第一節 相似度技術指標交易策略(STRBs) 19
第二節 以機器學習改善之相似度技術指標交易策略 31
第五章 結論 79
參考文獻 81		zh_TW
dc.format.extent 2032939 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103352011en_US
dc.subject (關鍵詞) 技術分析zh_TW
dc.subject (關鍵詞) 技術指標zh_TW
dc.subject (關鍵詞) 相似度技術指標交易策略zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 貪婪演算法zh_TW
dc.subject (關鍵詞) 模擬淬鍊法zh_TW
dc.title (題名) 以機器學習改善實證相似度技術指標交易策略之研究zh_TW
dc.title (題名) Adapting machine learning to similarity-based technical trading sstrategiesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Amos Tversky and Daniel Kahneman, (1973). Availability: A heuristic for judging frequency and probability., Cognitive Psychology 5 (2), 207-232.
Andrei Shynkevich, (2012). Performance of technical analysis in growth and small cap segments of the US equity market., Journal of Banking and Finance 36 (1), 193-208.
Andrew W. Lo, Harry Mamaysky and Jiang Wang, (2000). Foundations of technical analysis:Computational algorithms, statistical inference, and empirical implementation., Journal of Finance 55 (4), 1705-1765.
Ben R. Marshalla, Rochester H. Cahanb and Jared M. Cahana, (2008). Does intraday technical analysis in the U.S. equity market have value?, Journal of Empirical Finance 15 (2), 199-210.
Bo Qian and Khaled Rasheed, (2006). Stock market prediction with multiple classifiers, Applied Intelligence 26 (1), 25-33.
Burton G. Malkiel, (2003). The efficient market hypothesis and its critics., Journal of Economic Perspectives, 59-82.
Charles D. Kirkpatrick II and Julie R. Dahlquist, (2006). Technical Analysis: The Complete Resource for Financial Market Technicians., Financial Times Press, 3.
Cheol-Ho Park and Scott H. Irwin, (2010). A reality check on technical trading rule profits in the U.S. futures markets, Journal of Futures Markets 30 (7), 633-659.
David Hume, (1748). Enquiry into the human understanding, Oxford, Clarendon Press.
Fama, E. Fama and Marshall E. Blume, (1966). Filter rules and stock-market trading.,Journal of Business 39 (1), 226-241.
Francis Nicholson, (1968). Price-earnings ratios in relation to investmente results., Financial Analysts Journal, 105-109.
Gene Savin, Paul Weller and Janis Zvingelis, (2007). The predictive power of “Head-and-Shoulders” price patterns in the U.S. stock market., Journal of Econometrics 5 (2), 243-265.
Halbert White, (2000). A reality check for data snooping., Econometrica 68 (5), 1097-1126.
Itzhak Gilboa and David Schmeidler, (1995). Case-based decision theory., Quarterly Journal of Economics 110 (3), 605-63.
Itzhak Gilboa, Offer Lieberman and David Schmeidler, (2006). Empirical similarity., Review of Economics and Statistics 88 (3), 433-444.
Itzhak Gilboa, Offer Lieberman and David Schmeidler, (2011). A similarity-based approach to prediction., Journal of Econometrics 162 (1), 124-131.
Jeffrey S. Abarbanell and Brian J. Bushee, (1997). Fundamental analysis, future earnings, and stock prices., Journal of Accounting Research 35, no.1, 1-24.
Kosrow Dehnad, (2011). Behavioral finance and technical analysis., Journal of Financial Transformation, vol.32, 107-111.
Lukas Menkhoff, (2010). The use of technical analysis by fund managers: International evidence., Journal of Banking and Finance 34 (11), 2573-2586.
Mark P. Taylor and Helen Allen, (1992). The use of technical analysis in the foreign exchange market., Journal of International Money and Finance 11 (3), 304-314.
Michael C. Jensen and George A. Benington, (1970). Random walks and technical theories: some additional evidence., Journal of Finance 25 (2), 469-482.
Michael Kearns and Yuriy Nevmyvaka, (2013). Machine Learning for Market Microstructure and High Frequency Trading., High Frequency Trading - New Realities for Traders, Markets and Regulators (ed. O’Hara, M., de Prado, M.L. and Easley, D.). London: Risk Books, 91-124.
Peter Reinhard Hansen, (2005). A test for superior predictive ability., Journal of Business and Economic Statistics 23 (4), 365-380.
Pierre Bajgrowicz and Olivier Scaillet, (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs., Journal of Financial Economics 106 (3), 473-491.
Po-Hsuan Hsu and Chung-Ming Kuan, (2005). Reexamining the profitability of technical analysis with data snooping checks., Journal of Financial Econometrics 3 (4), 606-628.
Ramazan Gencay, (1998). Optimization of technical trading strategies and the profitability in security markets., Economics Letters 59 (2), 249-254.
Randall S. Sextona, Robert E. Dorseyb and John D. Johnsonc, (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing., European Journal of Operational Research 144 (3), 589-601.
Wai Mun Fong and Lawrence H. M. Yong, (2005). Chasing trends: Recursive moving average trading rules and internet stocks., Journal of Empirical Finance 12 (1), 43-76.
William Brock, Josef Lakonishok and B lake LeBaron, (1992). Simple technical trading rules and the stochastic properties of stock returns., Journal of Finance 47 (5), 1731-1764.
William Goffe, Gary Ferrier and John Rogers, (1994). Global Optimization of Statistical Functions with Simulated Annealing., Journal of Econometrics 60 (1-2), 65-99.
Wing Keung Wong, Meher Manzur and Boon-Kiat Chew, (2003). How rewarding is technical analysis? Evidence from Singapore stock market., Applied Financial Economics 13 (7), 543-551.
Yingzi Zhu and Guofu Zhou, (2009). Technical analysis: An asset allocation perspective on the use of moving averages., ournal of Financial Economics 92 (3), 519-544.
Yu-Hon Lui and David Mole, (1998). The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence., Journal of International Money and Finance 17 (3), 535-545.		zh_TW