(100.26.179.251) 您好!臺灣時間:2021/04/21 21:18
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:莊曜丞
研究生(外文):Yao-Cheng Chuang
論文名稱:應用遺傳演算法與夏普指標建構資金配置策略-以台灣股市為例
論文名稱(外文):Application of Genetic Algorithms and Sharpe Ratio Build Capital Allocation Strategy Based on Taiwan Stock
指導教授:侯佳利侯佳利引用關係
指導教授(外文):Jia-Li Hou
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊管理碩士學位學程
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
論文頁數:56
中文關鍵詞:投資組合理論遺傳演算法夏普指標
外文關鍵詞:Portfolio managementGenetic AlgorithmSharpe ratio
相關次數:
  • 被引用被引用:0
  • 點閱點閱:116
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
股票一直是投資人最常使用的投資工具,股價的漲跌取決於買家和賣價對公司未來的期許。儘管股票投資報酬率高,但同時也伴隨著高風險性。本研究以台灣50指數當日成分股作為投資標的,搭配移動視窗法,在訓練期間使用C_2^50方式產生1225個標的組合,並透過夏普指標進行實驗一最大化夏普和實驗二最大化夏普改善率,各實驗皆挑選前20名標的組合於測試期進行驗證,期望標的組合能同時具備最小化風險和報酬最大化。接著以夏普指標在訓練期間所篩選出前20名標的組合,透過遺傳演算法搜尋資金配置最佳解,而由於遺傳演算法或多或少受限於傳統編碼方式,需耗費大量計算資源在解答空間的問題上,因此本研究採用侯佳利學者所提出的「組合編碼遺傳演算法」的方式找出實驗一最大化夏普和實驗二最大化夏普改善率篩選出的標的組合在資金配置上的最佳解,進行實驗三最大化夏普資金配置和實驗四最大化夏普改善率,求出最佳報酬。
研究結果顯示以實驗一所篩選的標的組合,具有更好的避險效果和相當的獲利,四年本利和為1.3高於大盤本利和1.19。以實驗二篩選標的組合,雖然也有相當避險效果,但獲利方面則是低於大盤,四年本利和為1.14低於大盤1.19。而以實驗一和實驗二在訓練期間所篩選出的標的組合,透過遺傳演算法尋找最佳資金配置解方面,實驗三也有相當的獲利,四年本利和為1.26高於大盤1.19。而實驗四方面,四年本利和為1.03低於大盤1.19。

Stocks have been would be the most frequently used investment tools, although stock have high return on investment, but also with this high-risk. The observation of this study in TW50 index as an investing target, during the training using C_2^50 to generate 1225 portfolio, We applied the Sharpe Ratio to analysis experiment 1: maximum Sharpe ratio and experiment 2: maximum Sharpe improvement rate ,with selected top20 in the 1225 portfolio. And applied Genetic Algorithm to find a global optimality that capital allocation by estimating average return on the top20 portfolio, analysis experiment 3 and experiment 4. The GA is more or less limited to the traditional way of coding, this study use a combining GA by Doctor Hou. And test using the method of sliding windows in long terms.
Experimental results show that both the experiment 1 and experiment 3 of compound amount are 1.3 and 1.26 greater than market of compound amount 1.191, both the experiment 2 and experiment 4 of compound amount are 1.14 and 1.03 less than market of compound amount 1.191.

致謝 I
摘要 III
abstract V
目錄 VII
圖目錄 IX
表目錄 XI
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
一、 以夏普指標於台灣50成分股中建構投資組合策略 3
二、 應用遺傳演算法優化資金配置參數 3
三、 驗證標的組合績效 3
第三節 研究貢獻 3
一、 以夏普指標做選股和資金配置策略 3
二、 運用遺傳演算法於標的組合找尋最佳化資金配置解 4
第四節 論文章節說明 4
第二章 文獻探討 5
第一節 投資組合理論 5
一、 弱式效率市場 5
二、 半強式效率市場 5
三、 強式效率市場 5
四、 投資組合相關文獻 6
第二節 夏普指標 8
一、 夏普指標說明 8
二、 夏普指標相關文獻 10
第三節 遺傳演算法 12
一、 遺傳演算法簡介 12
二、 遺傳演算法運算程序 12
三、 組合編碼遺傳演算法 16
第四節 遺傳演算法相關文獻 19
第三章 研究方法 25
第一節 研究架構與說明 25
一、 選擇股票 26
二、 夏普指標 28
三、 遺傳演算法 29
四、 評估績效與累積報酬 30
五、 移動視窗法 30
第二節 研究工具 31
一、 軟體環境 31
二、 硬體環境 31
第四章 實驗說明與結果 33
第一節 應用夏普指標篩選標的組合 33
一、 實驗一 最大化夏普說明 33
二、 實驗一 最大化夏普結果 34
三、 實驗二 最大化夏普改善率說明 39
四、 實驗二 最大化夏普改善率結果 39
第二節 遺傳演算法最佳化資金配置 45
一、 遺傳演算法說明 45
二、 實驗三 應用GA優化最大化夏普資金配置說明 48
三、 實驗三 應用GA優化最大化夏普資金配置結果 48
四、 實驗四 應用GA優化最大化夏普改善率資金配置說明 49
五、 實驗四 應用GA優化最大化夏普改善率資金配置結果 50
第五章 結論與未來展望 52
第一節 結論與研究貢獻 52
第二節 研究限制與未來研究方向 52
參考文獻 54

[1] Sharpe, "Mutual fund performance," The Journal of business, vol. 39(1), pp. 119-138, 1966.
[2] B. R. Auer and F. Schuhmacher, "Robust evidence on the similarity of Sharpe ratio and drawdown-based hedge fund performance rankings," Journal of international financial markets, institutions and money, vol. 24, pp. 153-165, 2013.
[3] B. G. Malkiel and E. F. Fama, "Efficient capital markets: A review of theory and empirical work," The journal of Finance, vol. 25, pp. 383-417, 1970.
[4] H. Markowitz, "Portfolio selection," The journal of finance, vol. 7, pp. 77-91, 1952.
[5] H. G. Grubel, "Internationally diversified portfolios: welfare gains and capital flows," The American Economic Review, vol. 58, pp. 1299-1314, 1968.
[6] R. A. Ippolito, "Efficiency with costly information: A study of mutual fund performance, 1965-1984," The Quarterly Journal of Economics, pp. 1-23, 1989.
[7] P. Jorion, "Asset allocation with hedged and unhedged foreign stocks and bonds," The Journal of Portfolio Management, vol. 15, pp. 49-54, 1989.
[8] C. O. Alexander and C. T. Leigh, "On the covariance matrices used in value at risk models," The Journal of Derivatives, vol. 4, pp. 50-62, 1997.
[9] M. Statman, "How much diversification is enough?," Available at SSRN 365241, 2002.
[10] H. A. Shawky and D. M. Smith, "Optimal number of stock holdings in mutual fund portfolios based on market performance," Financial Review, vol. 40, pp. 481-495, 2005.
[11] W. F. Sharpe, "The sharpe ratio," The journal of portfolio management, vol. 21(1), pp. 49-58, 1994.
[12] R. S. Carlson, "Aggregate performance of mutual funds, 1948–1967," Journal of Financial and Quantitative Analysis, vol. 5, pp. 1-32, 1970.
[13] J. Meyer and R. H. Rasche, "Sufficient conditions for expected utility to imply mean-standard deviation rankings: empirical evidence concerning the location and scale condition," The Economic Journal, vol. 102, pp. 91-106, 1992.
[14] K. Dowd, "A value at risk approach to risk-return analysis," The Journal of Portfolio Management, vol. 25, pp. 60-67, 1999.
[15] B. Branch and L. Qiu, "Exploring the pros and cons of target date funds," Financial Services Review, vol. 20, p. 95, 2011.
[16] M. Schuster and B. R. Auer, "A note on empirical Sharpe ratio dynamics," Economics letters, vol. 116, pp. 124-128, 2012.
[17] W. K. Wong, J. A. Wright, S. C. P. Yam, and S. Yung, "A mixed Sharpe ratio," Risk and Decision Analysis, vol. 3, pp. 37-65, 2012.
[18] Ł. Delong and A. Pelsser, "Instantaneous mean-variance hedging and instantaneous Sharpe ratio pricing in a regime-switching financial model, with applications to equity-linked claims," arXiv preprint arXiv:1303.4082, 2013.
[19] T. Jagric, B. Podobnik, S. Strasek, and V. Jagric, "Risk-adjusted performance of mutual funds: some tests," South-eastern Europe journal of Economics, vol. 5, 2015.
[20] T. T. Nguyen, L. Gordon-Brown, A. Khosravi, D. Creighton, and S. Nahavandi, "Fuzzy portfolio allocation models through a new risk measure and fuzzy Sharpe ratio," IEEE Transactions on Fuzzy Systems, vol. 23, pp. 656-676, 2015.
[21] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press, 1975.
[22] R. K. Pitman, S. P. Orr, D. F. Forgue, J. B. de Jong, and J. M. Claiborn, "Psychophysiologic assessment of posttraumatic stress disorder imagery in Vietnam combat veterans," Archives of General Psychiatry, vol. 44, pp. 970-975, 1987.
[23] D. P. Goldberg, R. Gater, N. Sartorius, T. Ustun, M. Piccinelli, O. Gureje, et al., "The validity of two versions of the GHQ in the WHO study of mental illness in general health care," Psychological medicine, vol. 27, pp. 191-197, 1997.
[24] J.-L. Hou, "Constructing Combination Encoding GA and Applications on Capital Allocation Problem," 2001.
[25] Y. Orito and G. Yamazaki, "Index fund portfolio selection by using GA," in Computational Intelligence and Multimedia Applications, 2001. ICCIMA 2001. Proceedings. Fourth International Conference on, 2001, pp. 118-122.
[26] C. Zhou, L. Yu, T. Huang, S. Wang, and K. K. Lai, "Selecting valuable stock using genetic algorithm," in Asia-Pacific Conference on Simulated Evolution and Learning, 2006, pp. 688-694.
[27] D.-Y. Chiu and P.-J. Chen, "Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm," Expert Systems with Applications, vol. 36, pp. 1240-1248, 2009.
[28] C.-F. Tsai and Y.-C. Hsiao, "Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches," Decision Support Systems, vol. 50, pp. 258-269, 2010.
[29] S. Asadi, E. Hadavandi, F. Mehmanpazir, and M. M. Nakhostin, "Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction," Knowledge-Based Systems, vol. 35, pp. 245-258, 2012.
[30] C.-F. Huang, T.-N. Hsieh, B. R. Chang, and C.-H. Chang, "A Comparative Study of Regression and Evolution-Based Stock Selection Models for Investor Sentiment," in Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on, 2012, pp. 73-78.
[31] Y. Perwej and A. Perwej, "Prediction of the Bombay Stock Exchange (BSE) market returns using artificial neural network and genetic algorithm," 2012.
[32] C.-F. Huang, "A hybrid stock selection model using genetic algorithms and support vector regression," Applied Soft Computing, vol. 12, pp. 807-818, 2012.
[33] J.-J. Wang, J.-Z. Wang, Z.-G. Zhang, and S.-P. Guo, "Stock index forecasting based on a hybrid model," Omega, vol. 40, pp. 758-766, 2012.
[34] C. Evans, K. Pappas, and F. Xhafa, "Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation," Mathematical and Computer Modelling, vol. 58, pp. 1249-1266, 2013.
[35] S. Ding, Y. Zhang, J. Chen, and W. Jia, "Research on using genetic algorithms to optimize Elman neural networks," Neural Computing and Applications, vol. 23, pp. 293-297, 2013.
[36] L.-Y. Wei, "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, vol. 33, pp. 893-899, 2013.
[37] E. Asadi, M. G. da Silva, C. H. Antunes, L. Dias, and L. Glicksman, "Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application," Energy and Buildings, vol. 81, pp. 444-456, 2014.
[38] B.-Y. Liao, H.-W. Chen, S.-Y. Kuo, and Y.-H. Chou, "Portfolio Optimization Based on Novel Risk Assessment Strategy with Genetic Algorithm," in Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, 2015, pp. 2861-2866.
[39] A. M. Rather, A. Agarwal, and V. Sastry, "Recurrent neural network and a hybrid model for prediction of stock returns," Expert Systems with Applications, vol. 42, pp. 3234-3241, 2015.
[40] Y. Hu, K. Liu, X. Zhang, L. Su, E. Ngai, and M. Liu, "Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review," Applied Soft Computing, vol. 36, pp. 534-551, 2015.
[41] K. Prema, N. M. Agarwal, M. Krishna, and V. Agarwal, "Stock Market Prediction using Neuro-Genetic Model," Indian Journal of Science and Technology, vol. 8, 2016.
[42] M. Qiu, Y. Song, and F. Akagi, "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market," Chaos, Solitons & Fractals, vol. 85, pp. 1-7, 2016.
[43] J. Zhang and D. Maringer, "Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading," Computational Economics, vol. 47, pp. 551-567, 2016.
[44] M. Qiu and Y. Song, "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PloS one, vol. 11, p. e0155133, 2016.


電子全文 電子全文(網際網路公開日期:20210814)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔