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首页> 外文期刊>Review of Accounting and Finance >The equity premium puzzle: an artificial neural network approach
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The equity premium puzzle: an artificial neural network approach

机译：股权溢价之谜：人工神经网络方法

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摘要

Purpose – In recent years, equity premiums have been unusually large and efforts to forecast them have been largely unsuccessful. This paper presents evidence suggesting that artificial neural networks (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well. Design/methodology/approach – This study replicates out-of-sample estimates of regression using ANN with economic fundamentals as inputs. The theory states that recent large equity premium values cannot be explained (the equity premium puzzle). Findings – The dividend yield variable was found to produce the best out-of-sample forecasts for equity premium. Research limitations/implications – Although the equity premium puzzle can be partly explained by fundamentals, they do not imply immediate policy prescriptions since all forecasting techniques including ANN are susceptible to joint assumptions of the techniques and the models used. Practical implications – This result is useful in capital asset pricing model and in asset allocation decisions. Originality/value – Unlike the findings from previous research that are unable to explain equity premium behavior, this paper suggests that equity premium can be reasonably forecasted.
机译：目的–近年来，股票溢价异常大，预测其溢价的努力基本上没有成功。本文提供的证据表明，人工神经网络（ANN）优于传统的统计方法，并且可以很好地预测股票溢价。设计/方法/方法–这项研究使用具有经济基本面作为输入的ANN复制样本外的回归估计。该理论指出，无法解释近期的大量股权溢价（股权溢价之谜）。调查结果–发现股息收益率变量可产生最佳的股本溢价预测。研究的局限性/含义-尽管可以用基本原理部分解释股权溢价之谜，但它们并不意味着立即制定政策处方，因为包括ANN在内的所有预测技术都容易受到所用技术和模型的共同假设的影响。实际意义–该结果在资本资产定价模型和资产分配决策中很有用。原创性/价值–与以往研究无法解释股本溢价行为的发现不同，本文建议可以合理地预测股本溢价。

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