Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/32206
|
| Title: | 投資訊號之演化性辨識:機制設計的研究與應用 |
| Authors: | 郭子文 |
| Contributors: | 陳樹衡
郭子文 |
| Keywords: | 計算智慧
遺傳規劃
過度學習
交易策略
股票市場 |
| Date: | 2004 |
| Issue Date: | 2009-09-14 13:24:37 (UTC+8) |
| Abstract: | 隨著計算智慧(Computational Intelligence)工具的發展日益成熟, 將這些工具應用在經濟或財務問題上的文章逐漸增多, 國際上對這塊跨科際的研究領域愈來愈重視, 本論文討論和使用的工具是演化計算中的遺傳規劃(Genetic Programming)。
很多應用遺傳規劃的文章,經常被人質疑的一個問題是參數的任意設定,
參數任意或故意的設定是否會影響搜尋的速度或結果,是很多使用者所關心的; 除了參數設定的問題之外,也會有人質疑應用強大的搜尋工具是否會發生過度學習, 關於這些質疑,本論文仔細討論應用此工具時應該注意的參數設定和相關問題, 同時也討論關於過度學習和學習不足的問題,並提出一些心得,可以作為遺傳規劃使用者的參考。
除了討論這些問題之外,本論文使用這些經驗和心得,實際應用在交易策略尋找的問題上, 其中一個應用範圍是關於跨國資金流動的問題,另一個則應用在股票市場。 由以前相關的文獻可以知道,使用遺傳規劃演化出來的交易策略通常是複雜而且不易閱讀, 如果希望遺傳規劃能夠真正對投資者有所幫助,除了交易策略是否能夠獲利之外, 如何改善策略不易閱讀是很重要的問題。 本論文也針對這個問題提出方法並且實際應用在股票市場,
結果發現尋找出來的交易策略不但有超額報酬而且策略簡單易懂。 |
| Reference: | Franklin Allen and Risto Karjalainen. Using genetic algorithms to find technical trading rules. Technical Report 20-93, Rodney L. White Center for Financial Research, The Wharton School, 1993.
Franklin Allen and Risto Karjalainen. Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2):245--271, 1999.
R. J. Jr. Bauer. Genetic Algorithms and Investment Strategies. Wiley, 1994.
R. J. Jr. Bauer and G. E. Liepins. Genetic algorithms and computerized trading strategies. In Paul R. Watkins and Daniel Edmund O`Leary, editors, Expert Systems in Finance. North Holland, 1992.
Edward A. Bender. Mathematical Methods in Artificial Intelligence. IEEE, Los Alamitos, Calif, 1996.
H. Bessembinder and K. Chan. The profitability of technical trading rules in the asian stock markets. Pacific Basin Finance Journal, 3:257--284, 1995.
Siddhartha Bhattacharyya, Olivier V. Pictet, and Gilles Zumbach. Knowledge-intensive genetic discovery in foreign exchange markets. IEEE Transactions on Evolutionary Computation, 6(2):169--181, 2002.
William Brock, Josef Lakonishok, and Blake LeBaron. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5):1731--1764, 1992.
Shu-Heng Chen and Tzu-Wen Kuo. Genetic programming: A tutorial with the software simple gp. In Shu-Heng Chen, editor, Genetic Algorithms and Genetic Programming in Computational Finance, pages 55--77. Kluwer Academic Publishers, 2002.
Shu-Heng Chen and Chia-Hsuan Yeh. Toward a computable approach to the efficient market hypothesis: An application of genetic programming. Journal of Economic Dynamics and Control, 21(6):1043--1063, 1997.
N. Chidambaran, C.-W. J. Lee, and J. Trigueros. Option pricing via genetic programming. In Shu-Heng Chen, editor, Evolutionary Computation in Economics and Finance, pages 383--398. Physica-Verlag, 2002.
Alonzo Church. The Calculi of Lambda Conversion. Princeton University Press, 1941.
Michael Cooper. Filter rules based on price and volume in individual security overreaction. The Review of Financial Studies, 12(4):901--935, 1999.
Adam Fadlalla and Chien-Hua Lin. An analysis of the applications of neural networks in finance. INTERFACES, 31(4):112--122, 2001.
S. Geman, E. Bienenstock, and R. Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4(1):1--58, 1992.
M. A. Kaboudan. A measure of time series`s predictability using genetic programming applied to stock returns. Journal of Forecasting, 18:345--357, 1999.
M. A. Kaboudan. Genetically evolved models and normality of their fitted residuals. Journal of Economic Dynamics and Control, 25(11):1719--1749, 2001.
M. A. Kaboudan. Gp forecasts of stock prices for profitable trading. In Shu-Heng Chen, editor, Evolutionary Computation in Economics and Finance, pages 359--381. Physica-Verlag, 2002.
V. Kecman. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, 2001.
John R. Koza. A genetic approach to econometric modelling. In P. Bourgine and B. Walliser, editors, Economics and Cognitive Science, pages 57--75. Pergamon Press, 1992.
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts, May 1994.
M. Lettau. Explaining the facts with adaptive agents: the case of mutual fund flows. Journal of Economic Dynamics and Control, 21(7):1117--1147, 1997.
A. Loraschi and A. Tettamanzi. An evolutionary algorithm for portfolio selection within a downside framework. In C. Dunis, editor, Forecasting Financial Markets: Exchange Rates, Interest Rates and Asset Management, pages 275--285. John Wiley & Sons, 1996.
K. Mehrotra, C. Mohan, and S. Ranka. Elements of Artificial Neural Networks. MIT Press, Cambridge, Mass, 1997.
Christopher J. Neely and Paul A. Weller. Technical trading rules in the european monetary system. Journal of International Money and Finance, 18(3):429--458, 1999.
Christopher J. Neely and Paul A. Weller. Using gp to predict exchange rate volatility. In Shu-Heng Chen, editor, Genetic Algorithms and Genetic Programming in Computational Finance, pages 263--279. Kluwer Academic Publishers, 2002.
Christopher J. Neely, Paul A. Weller, and Rob Dittmar. Is technical analysis in the foreign exchange market profitable? A genetic programming approach. The Journal of Financial and Quantitative Analysis, 32(4):405--426, December 1997.
Thomas H. Noe and Jun Wang. The self-evolving logic of financial claim prices. In Shu-Heng Chen, editor, Genetic Algorithms and Genetic Programming in Computational Finance, pages 249--262. Kluwer Academic Publishers, 2002.
S. Novkovic. A genetic algorithm simulation of a transition economy: An application to insider-privatization in croatia. Computational Economics, 11(3):221--243, 1998.
Michael O`Neill, Anthony Brabazon, and Conor Ryan. Forecasting market indices using evolutionary automatic programming. In Shu-Heng Chen, editor, Genetic Algoritms and Genetic Programming in Computational Finance, chapter 8, pages 175--195. Kluwer Academic Publishers, 2002.
R. Pereira. Forecasting ability but no profitability: An empirical evaluation of genetic algorithm-optimized technical trading rules. In Shu-Heng Chen, editor, Evolutionary Computation in Economics and Finance, pages 287--310. Physica-Verlag, 2002.
M. Hashem Pesaran and Allan Timmermann. Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50:1201--1228, 1995.
Mukund Seshadri. Comprehensibility, overfitting and co-evolution in genetic programming for technical trading rules. Master`s thesis, Worcester Polytechnic Institute, 6 Castle Road, Northboro, MA 10532, USA, 2003.
M. Smith. Neural Networks for Statistical Modeling. Van Nostrand Reinhold, 1993.
M. Stone. Cross-validatory choice and assessment of statistical predictors. Journal of the Royal Statistical Society, B36:111--147, 1974.
G. G. Szpiro. Forecasting chaotic time series with genetic algorithms. Physical Review E, 55:2557--2568, 1997.
G. G. Szpiro. A search for hidden relationships: Data mining with genetic algorithms. Computational Economics, 10(3):267--277, 1997.
G. G. Szpiro. Tinkering with genetic algorithms: Forecasting and data mining in finance and economics. In Shu-Heng Chen, editor, Evolutionary Computation in Economics and Finance, pages 273--286. Physica Verlag, 2002.
R. Tsang and P. Lajbcygier. Optimization of technical trading strategy with split search gen abu-metic algorithms. In Shu-Heng Chen, editor, Evolutionary Computation in Economics and Finance, pages 333--358. Physica-Verlag, 2002.
H. R. Varian. Microeconomic Analysis. Norton, 1992.
Jun Wang. Trading and hedging in s&p 500 spot and futures markets using genetic programming. The Journal of Futures Markets, 20(10):911--942, 2000.
Halbert White. Economic prediction using neural networks, the case of IBM daily stock returns. In Proceedings of IEEE International Conference on Neural Networks, v. 2, pages 451--458. IEEE, 1988.
Gwoing Tina Yu. An Analysis of the Impact of Functional Programming Techniques on Genetic Programming. PhD thesis, University College, London, Gower Street, London, WC1E 6BT, 1999.
Tina Yu. Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genetic Programming and Evolvable Machines, 2(4):345--380, December 2001. |
| Description: | 博士
國立政治大學
經濟研究所
87258502
93 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0087258502 |
| Data Type: | thesis |
| Appears in Collections: | [經濟學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| index.html | 0Kb | HTML2 | 315 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
