類神經網路於投資策略的應用

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姓名

林奇鋒(Chi-Feng Lin) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

類神經網路於投資策略的應用

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摘要(中)

股票的波動是一種時間序列的資料。時間序列的預測是一個重要的研究議題，人工智慧計算模型目前正被廣泛使用於該議題，例如:類神經模糊系統等。本文提出複數型模糊類神經系統 (Complex neuro-fuzzy system)並應用於多目標時間序列預測，此模型具有多組複數型態輸出，其中，每一組複數型態的輸出，其實部和虛部可分別針對兩個不同實數型態目標進行預測。有關特徵挑選，本研究採用多目標特徵挑選，篩選出針對所有目標有利的特徵，並以此作為模型輸入，以降低模型整體運算負擔及提高資料運用效率。在模型方面，由輸入層、複數模糊集合神經層 (Complex fuzzy sets layer)、前提式神經層 (Premise neural layer)、T-S神經層 (Takagi-Sugeno neural layer)及輸出層，建構出多層式類神經網路。在參數學習方面，訓練模型時我們採用分治原則(Divide-and-conquer principle)。複數模糊集合神經層的參數使用不同的演算法優化，像是粒子群演算法 (Particle swarm optimization, PSO)、人工蜂群演算法 (Artificial bee colony optimization, ABCO); T-S神經層的參數使用遞迴式最小平方演算法 (Recursive least-squares estimation, RLSE)進行優化; 其他的神經層沒有參數需要優化。在實驗方面，我們設計三個實驗檢驗模型的效能，將PSO-RLSE及ABCO-RLSE實驗結果結合投資策略，計算模型利潤互相比較也與不同的文獻方法比較。本研究提出新的投資策略，與過去做利潤的比較，經由效能及利潤比較結果，本文提出多目標預測的研究方法表現出優秀效能以及投資效果。

摘要(英)

Stock fluctuations are time series data. The prediction of time series is an important research topic. Artificial intelligence models are currently being widely used in this topic, such as neuro-fuzzy systems. This paper proposes a complex neuro-fuzzy system and applies it to multi-target time series prediction. This model has multiple complex-valued outputs, every output can have real and imaginary parts for two different real-valued targets, respectively. With regard to feature selection, this study uses multi-target feature selection to filter out features that are beneficial to all targets and use this as the model inputs to reduce the overall computational burden and improve data utilization efficiency. In terms of model, multi-layer neural network is constructed from input layer, Complex fuzzy set layer (CFS layer), Premise neural layer, Takagi-Sugeno neural layer (T-S neural layer), and output layer. For parameter learning, we use the divide-and-conquer principle when training the model. The parameters of the complex fuzzy set neural layer are optimized using different algorithm, such as particle swarm optimization (PSO), artificial bee colony optimization (ABCO); the parameters of the T-S neural layer are optimized using recursive least-squares estimation (RLSE), other neural layers have no parameters to optimize. In terms of experiments, we use three experiments to test the performance of the model. We combine investment strategy with PSO-RLSE and ABCO-RLSE experimental results, respectively, and calculate model profit to compare with each other and the different literature methods. This study proposed a new investment strategy. Through the results of the performance comparison and the profits comparison, this paper presents a multi-target prediction method showing excellent performance and investment effect.

關鍵字(中)

★ 類神經網路
★ 粒子群演算法
★ 複數模糊類神經系統
★ 人工蜂群演算法
★ 時間序列

關鍵字(英)

論文目次

類神經網路於投資策略的應用 i
Neural Network Applied in Investment Strategy ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究方法概述 4
1.4 論文架構 4
第二章文獻探討 6
2.1 特徵選取 6
2.2 複數模糊集合 7
2.3 類神經網路 8
2.4 複數類神經模糊系統 8
第三章系統設計與架構 10
3.1 複數模糊集 10
3.2 結構學習 11
3.3 複數模糊類神經模型 14
3.4 參數學習演算法 17
3.4.1 粒子群演算法 17
3.4.2 人工蜂群演算法 18
3.4.3 遞迴最小平方演算法 19
3.5 投資策略 21
第四章實驗 24
4.1 實驗一：台灣股票加權指數單目標預測 25
4.2 實驗二：台灣股票加權指數與恆生指數雙目標預測 32
4.3 實驗三：台灣股票加權指數、道瓊工業指數、納斯達克和標準普爾500四目標預測 40
第五章討論 50
第六章結論與未來研究方向 53
6.1 結論 53
6.2 未來研究方向 53
參考文獻 55

參考文獻

[1] E. Abbasi and A. Abouec, "Stock price forecast by using neuro-fuzzy inference system," Proceedings of World Academy of Science, Engineering and Technology, vol. 36, pp. 320-323, 2008.
[2] P. L. Avrim L. Blum, "Selection of relevant features and examples in machine learning," Artificial Intelligence, vol. 97, no. 1-2, 1997.
[3] J. Buckley, "Fuzzy complex numbers," Fuzzy Sets and Systems, vol. 33, no. 3, pp. 333-345, 1989.
[4] J. Buckley and Y. Qu, "Fuzzy complex analysis I: differentiation," Fuzzy Sets and Systems, vol. 41, no. 3, pp. 269-284, 1991.
[5] J. J. Buckley, "Fuzzy complex analysis II: integration," Fuzzy Sets and Systems, vol. 49, no. 2, pp. 171-179, 1992.
[6] C.-H. Cheng and J.-H. Yang, "Fuzzy time-series model based on rough set rule induction for forecasting stock price," Neurocomputing, vol. 302, no. 9, pp. 33-45, 2018.
[7] S. L. Chiu, "Fuzzy model identification based on cluster estimation," Journal of Intelligent & fuzzy systems, vol. 2, no. 3, pp. 267-278, 1994.
[8] R. Clausius, "Uber eine veranderte Form des zweiten Hauptsatzes der mechanischen Warmetheorie," Annalen der Physik, vol. 169, no. 12, pp. 481-506, 1854.
[9] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.
[10] L. Davis, "Handbook of genetic algorithms," Artificial Intelligence, vol. 100, no. 1-2, pp. 325-330, 1991.
[11] E. F. Fama, "Random walks in stock market prices," Financial analysts journal, vol. 51, no. 1, pp. 75-80, 1995.
[12] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, pp. 1157-1182, 2003.
[13] D. O. Hebb, The organization of behavior: A neuropsychological theory. New York: Wiley, 1949.
[14] N. Hoque, D. Bhattacharyya, and J. K. Kalita, "MIFS-ND: a mutual information-based feature selection method," Expert Systems with Applications, vol. 41, no. 14, pp. 6371-6385, 2014.
[15] H.-H. Hsu, C.-W. Hsieh, and M.-D. Lu, "Hybrid feature selection by combining filters and wrappers," Expert Systems with Applications, vol. 38, no. 7, pp. 8144-8150, 2011.
[16] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence," Automatic Control, vol. 42, no. 10, Oct 1997.
[17] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, vol. 200, 2005.
[18] M. G. Kendall and A. B. Hill, "The analysis of economic time-series-part i: Prices," Journal of the Royal Statistical Society. Series A (General), vol. 116, no. 1, pp. 11-34, 1953.
[19] J. Kennedy and R. Eberhart, "A new optimizer using particle swarm theory," Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1941-1948, 1995.
[20] K.-j. Kim and I. Han, "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index," Expert systems with Applications, vol. 19, no. 2, pp. 125-132, 2000.
[21] T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, "Stock market prediction system with modular neural networks," Neural Networks, pp. 1-6, 1990.
[22] R. S. Koijen, H. Lustig, and S. Van Nieuwerburgh, "The cross-section and time series of stock and bond returns," Journal of Monetary Economics, vol. 88, pp. 50-69, 2017.
[23] C. Li and T.-W. Chiang, "Complex neuro-fuzzy self-learning approach to function approximation," presented at the Asian Conference on Intelligent Information and Database Systems, 2010.
[24] C. Li and T.-W. Chiang, "Complex fuzzy computing to time series prediction—A multi-swarm PSO learning approach," presented at the Asian Conference on Intelligent Information and Database Systems, 2011.
[25] C. Li, C. W. Lin, and H. Huang, "Neural Fuzzy Forecasting of the China Yuan to US Dollar Exchange Rate—A Swarm Intelligence Approach," presented at the International Conference in Swarm Intelligence, 2011.
[26] C. Li and J.-W. Hu, "A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting," Engineering Applications of Artificial Intelligence, vol. 25, no. 2, pp. 295-308, 2012.
[27] C. Li and T.-W. Chiang, "Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 567-584, 2013.
[28] C. Li, "國立中央大學資訊管理所李俊賢教授, 研究生訓練課程內容," 2016-2018.
[29] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115-133, 1943.
[30] Z. Pan and L. Liu, "Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects," Physica A: Statistical Mechanics and its Applications, vol. 492, pp. 168-180, 2018.
[31] P. B. Patel and T. Marwala, "Neural networks, fuzzy inference systems and adaptive-neuro fuzzy inference systems for financial decision making," in International Conference on Neural Information Processing, 2006, pp. 430-439: Springer.
[32] D. Ramot, R. Milo, M. Friedman, and A. Kandel, "Complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 171-186, 2002.
[33] N. Rochester, J. Holland, L. Haibt, and W. Duda, "Tests on a cell assembly theory of the action of the brain, using a large digital computer," IRE Transactions on information Theory, vol. 2, no. 3, pp. 80-93, 1956.
[34] N. Russel, Artificial Intelligenz-A Modern Approach, 2nd. 2002.
[35] C. E. Shannon, "A mathematical theory of communication," ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, no. 1, pp. 3-55, 2001.
[36] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," Readings in Fuzzy Sets for Intelligent Systems, vol. SMC-15, no. 1, pp. 387-403, 1985.
[37] J. Vieira, F. M. Dias, and A. Mota, "Neuro-fuzzy systems: a survey," in 5th WSEAS NNA international conference on neural networks and applications, Udine, Italia, 2004.
[38] L.-Y. Wei, "A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting," Applied Soft Computing, vol. 42, pp. 368-376, 2016.
[39] P. Werbos, "Beyond regression: new fools for prediction and analysis in the behavioral sciences," PhD thesis, Harvard University, 1974.
[40] J. Yao, C. L. Tan, and H.-L. Poh, "Neural networks for technical analysis: a study on KLCI," International journal of theoretical and applied finance, vol. 2, no. 02, pp. 221-241, 1999.
[41] S. Yao, M. Pasquier, and C. Quek, "A foreign exchange portfolio management mechanism based on fuzzy neural networks," in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 2576-2583.
[42] L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy," Journal of machine learning research, vol. 5, pp. 1205-1224, Oct 2004.
[43] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338-353, 1965.
[44] X. Zhi-Bin and L. Rong-Jun, "Credit risk evaluation with fuzzy neural networks on listed corporations of China," presented at the VLSI Design and Video Technology, 2005.

指導教授

李俊賢

審核日期

2018-7-20

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