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The pricing accuracy of alternative equity valuation models: Scandinavian evidence

机译:另类股权估值模型的定价准确性:斯堪的纳维亚证据

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In many decision contexts, there is a need for benchmark equity valuations, based on simplified modeling and publicly available information. Prior research on U.S. data however shows that the accuracy of such valuation models can be low and sensitive to the choice of model specifications and value driver predictions. In this paper, we test the applicability and pricing accuracy of three fundamental valuation (dividend discount, residual income, and abnormal earnings growth) models, all based on forecasts of company dividends, earnings, and/or equity book values. Extending prior research, we apply these models to Scandinavian firms with accounting data from the period 2005-2014, explicitly testing two approaches for the prediction of the value drivers-exogenously forecasted numbers versus projected historical numbers. Given access to the forecasted value drivers, the dividend discount model comes out as the most accurate valuation model. In particular, this holds in a comparison between the most parsimonious model specifications. The residual income valuation model generates the best pricing accuracy given the prediction of value drivers based on historical financial numbers. Notably, we observe pricing errors that in general are lower than what has been reported in prior U.S.-based research for the dividend discount and the residual income valuation models. The pricing accuracy of the abnormal earnings growth models is surprisingly weak in the Scandinavian setting. However, these models improve somewhat after a couple of complexity adjustments, in particular with value driver predictions based on the projected history setting.
机译:在许多决策环境中,需要基于简化的模型和可公开获得的信息进行基准股权估值。但是,先前对美国数据的研究表明,此类评估模型的准确性可能较低,并且对模型规格和价值驱动因素预测的选择敏感。在本文中,我们测试了三种基本估值模型(股息折扣,剩余收益和异常收益增长)的适用性和定价准确性,这些模型均基于对公司股息,收益和/或股本账面价值的预测。扩展先前的研究,我们将这些模型应用于具有2005-2014年期间会计数据的斯堪的纳维亚公司,明确测试了两种预测价值动因的方法-外生预测数与预测历史数。有了预测价值驱动因素,股息折现模型便成为最准确的估值模型。特别是,这在最简约的模型规格之间进行了比较。在基于历史财务数据预测价值驱动因素的情况下,残差收入评估模型可产生最佳定价准确性。值得注意的是,我们观察到的定价误差通常低于美国先前针对股息折现和剩余收益估值模型的研究报告的误差。在斯堪的纳维亚地区,异常收入增长模型的定价准确性令人惊讶地薄弱。但是,这些模型在进行了几项复杂性调整后会有所改善,尤其是基于基于预测历史记录设置的值驱动程序预测时。

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