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Estimating the distribution of enterprise values with quantile neural networks

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Abstract

The probability density function of enterprise values may be more precise and useful in the cases of corporate investment, financing, or transactions. Although the quantile regression analysis can generate a set of models for a series of quantiles, it cannot generate the probability density function of the dependent variable. Therefore, this paper proposes a novel method of employing prediction results of the quantile neural networks to build probability density functions with which we can effectively assess enterprise values. Empirical evidence reveals that the estimated cumulative lognormal distribution curves of the price-to-book value ratio (PBR) and the data are well matched. In addition, the corporate market value is equal to the PBR multiplied by the corporate stockholders equity. Thus, the corporate market value is also a lognormal distribution. PBR distributions of building and construction industries are more tilted to the left, implying that enterprise values of building and construction industries are lower than those of other industries with the same stockholders equity and return on equity.

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Authors and Affiliations

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

    I-Cheng Yeh

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

    Yi-Cheng Liu

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

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Yeh, IC., Liu, YC. Estimating the distribution of enterprise values with quantile neural networks. Soft Comput 24, 13085–13097 (2020). https://doi.org/10.1007/s00500-020-04726-w

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