The directional news impact curve (DNIC) is a relationship between returns and the probability of next period's return exceeding a certain threshold—zero in particular. Using long series of S&P500 index returns and a number of parametric models suggested in the literature, as well and flexible semiparametric models, we investigate the shape of the DNIC and forecasting abilities of these models. The semiparametric approach reveals that the DNIC has complicated shapes characterized by nonsymmetry with respect to past returns and their signs, heterogeneity across the thresholds, and changes over time. Simple parametric models often miss some important features of the DNIC, but some nevertheless exhibit superior out‐of‐sample performance.

中文翻译：

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定向新闻影响曲线

定向新闻影响曲线（DNIC）是收益与下一期间收益超过特定阈值（尤其是零）的概率之间的关系。使用长期的S＆P500指数回报率和文献中建议的许多参数模型以及灵活的半参数模型，我们研究了DNIC的形状和这些模型的预测能力。半参数方法表明，DNIC具有复杂的形状，其特征是相对于过去的收益及其符号，跨阈值的异质性以及随时间变化的不对称性。简单的参数模型通常会遗漏DNIC的一些重要功能，但其中一些仍具有出色的样本外性能。