Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2007.14-B.2.127

Integrated Multiple Simulation for Optimizing Performance of Stock Trading Systems based on Neural Networks  

Lee, Jae-Won (성신여자대학교 컴퓨터정보학부)
O, Jang-Min (MIN 데이터마이닝랩)
Publication Information
The KIPS Transactions:PartB / v.14B, no.2, 2007 , pp. 127-134 More about this Journal
Abstract
There are many researches about the intelligent stock trading systems with the help of the advance of the artificial intelligence such as machine learning techniques, Though the establishment of the reasonable trading policy plays an important role in the performance of the trading systems most researches focused on the improvement of the predictability. Also some previous works, which treated the trading policy, treated the simplified versions dependent on the predictors in less systematic ways. In this paper, we propose the integrated multiple simulation' as a method of optimizing trading performance of stock trading systems. The propose method is adopted in the NXShell a development environment for neural network based stock trading systems. Under the proposed integrated multiple simulation', we simulate the multiple tradings for all combinations of the neural network's outputs and the trading policy parameters, evaluate the learning performance according to the various metrics and establish the optimal policy for a given prediction module based on the resulting performance. In the experiment, we present the trading policy comparison results using the stock value data from the KOSPI and KOSDAQ.
Keywords
Stock Trading; Intelligent System; Multiple Simulation; Trading Policy; Performance Optimization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. W. Lee, J. O, 'A Multi-agent Q-learning framework for Optimizing Stock Trading Systems,' Proceedings of the International Conference on Database and Expert Systems Applications, pp. 153-162, 2002
2 J. S. Zirilli, Financial Prediction using Neural Networks, Thomson Computer Press, 1997
3 J. Moody and M. Saffell, 'Learning to Trade via Direct Reinforcement', IEEE Transactions on Neural Networks, 12(4), pp.875-889, 2001   DOI   ScienceOn
4 J. O. J. Lee, J. W. Lee, and B. Zhang, 'Adaptive Stock Trading with Dynamic Asset Allocation using Reinforcement Learning,' Information Sciences 176, pp.2121-2147, 2006   DOI   ScienceOn
5 J. W. Lee, S. D. Kim, J. Lee, J. Chae, 'An Intelligent Stock Trading System based on Reinforcement Learning, IEICE Transactions on Information and Systems E86-D(2), pp. 296-305, 2003
6 G. Armano, M. Marchesi, A. Murru, A Hybrid Genetic-neural Architecture for Stock Indexes Forecasting, Information Sciences 170(1), pp.3-33, 2005   DOI   ScienceOn
7 R. S. T. Lee, 'iJADE Stock Advisor: An Intelligent Agent based Stock Prediction System using Hybrid RBF Recurrent Networks,' IEEE Transactions on Systems, Man, and Cyberneties, Part A:Systems and Humans 34(3), pp. 421-428, 2004   DOI   ScienceOn
8 A. Fan, M. Palaniswami, 'Stock Selection using Support Vector Machines,' Proceedings of the Internation Joint Conference on Xeural Networks, pp. 1793-1798, 2001   DOI
9 A. W. Lo, Long Term Memory in Stock Market Prices, Econometrica 59(5)   DOI   ScienceOn
10 S. M, Kendall and K. Ord, Time Series, Oxford, New York, 1997
11 S. Cottle, R. F. Murray, F. E. Block, and D. L. Dodd, Graham and Dodd's Security Analysis(5th edition), McGraw-Hill, 1988
12 R. D. Edwards and J. Magee, Technical Analysis of Stock Trends, John Magee, Inc., 1974