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


The KIPS Transactions:PartB , Vol. 14, No. 2, pp. 127-134, Apr. 2007
10.3745/KIPSTB.2007.14.2.127,   PDF Download:

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 system, 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 proposed method is adopted in the NNshell, 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 trading 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.


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Cite this article
[IEEE Style]
J. W. Lee and J. M. O, "Integrated Multiple Simulation for Optimizing Performance of Stock Trading Systems based on Neural Networks," The KIPS Transactions:PartB , vol. 14, no. 2, pp. 127-134, 2007. DOI: 10.3745/KIPSTB.2007.14.2.127.

[ACM Style]
Jae Won Lee and Jang Min O. 2007. Integrated Multiple Simulation for Optimizing Performance of Stock Trading Systems based on Neural Networks. The KIPS Transactions:PartB , 14, 2, (2007), 127-134. DOI: 10.3745/KIPSTB.2007.14.2.127.