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Using mixture design and neural networks to build stock selection decision support systems

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Abstract

There are three disadvantages of weighted scoring stock selection models. First, they cannot identify the relations between weights of stock-picking concepts and performances of portfolios. Second, they cannot systematically discover the optimal combination for weights of concepts to optimize the performances. Third, they are unable to meet various investors’ preferences. This study aimed to more efficiently construct weighted scoring stock selection models to overcome these disadvantages. Since the weights of stock-picking concepts in a weighted scoring stock selection model can be regarded as components in a mixture, we used the simplex-centroid mixture design to obtain the experimental sets of weights. These sets of weights are simulated with US stock market historical data to obtain their performances. Performance prediction models were built with the simulated performance data set and artificial neural networks. Furthermore, the optimization models to reflect investors’ preferences were built up, and the performance prediction models were employed as the kernel of the optimization models, so that the optimal solutions can now be solved with optimization techniques. The empirical values of the performances of the optimal weighting combinations generated by the optimization models showed that they can meet various investors’ preferences and outperform those of S&P’s 500 not only during the training period but also during the testing period.

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Authors and Affiliations

  1. Department of International Business, Tamkang University, 151 Yingzhuan Rd., Tamsui Dist., New Taipei City, 25137, Taiwan

    Yi-Cheng Liu

  2. Department of Civil Engineering, Tamkang University, 151 Yingzhuan Rd., Tamsui Dist., New Taipei City, 25137, Taiwan

    I-Cheng Yeh

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  1. Yi-Cheng Liu
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  2. I-Cheng Yeh
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Correspondence to I-Cheng Yeh.

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Liu, YC., Yeh, IC. Using mixture design and neural networks to build stock selection decision support systems. Neural Comput & Applic 28, 521–535 (2017). https://doi.org/10.1007/s00521-015-2090-x

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  • DOI: https://doi.org/10.1007/s00521-015-2090-x

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