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Simultaneous optimization of artificial neural networks for financial forecasting

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Abstract

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.

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Authors and Affiliations

  1. Department of Management Information Systems, Dongguk University_Seoul, 3-26 Pil-dong, Chung-gu, Seoul, 100-715, Korea

    Kyoung-jae Kim

  2. School of Management Information Systems, Kookmin University, 861-1, Jeongneung-dong, Seongbuk-gu, Seoul, 136-702, Korea

    Hyunchul Ahn

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  1. Kyoung-jae Kim
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  2. Hyunchul Ahn
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Correspondence to Kyoung-jae Kim or Hyunchul Ahn.

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Kim, Kj., Ahn, H. Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36, 887–898 (2012). https://doi.org/10.1007/s10489-011-0303-2

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