Kwon Yung-Keun, Moon Byung-Ro
School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea.
IEEE Trans Neural Netw. 2007 May;18(3):851-64. doi: 10.1109/TNN.2007.891629.
In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction.
在本文中，我们提出了一种用于股票交易的混合神经遗传系统。具有一个隐藏层的递归神经网络（NN）被用作预测模型。输入特征由金融专家使用的一些技术指标生成。遗传算法（GA）在二维编码和交叉下优化神经网络的权重。我们设计了一种基于上下文的神经网络集成方法，该方法根据测试日的上下文动态变化。为了减少处理海量数据的时间，我们使用消息传递接口在Linux集群系统上对遗传算法进行了并行化处理。我们从1992年到2004年对纽约证券交易所（NYSE）和纳斯达克（NASDAQ）的36家公司进行了13年的测试。神经遗传混合模型在平均水平上比买入并持有策略有显著改进，基于上下文的集成方法进一步改善了结果。我们还观察到，一些公司比其他公司更具可预测性，这意味着所提出的神经遗传混合模型可用于金融投资组合构建。
