Purpose

The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.

Design/methodology/approach

Archival in nature using a combination of regression analysis and binomial tests.

Findings

The binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.

Research limitations/implications

The results are generalizable to US firms and may not extend to non-US firms.

Practical implications

The TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.

Social implications

Awareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.

Originality/value

Although a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

中文翻译：

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OLS 和 Theil-Sen 模型相对于 na.ı.ve 不变模型的样本外收益预测

目的

本文的目的是使用收益预测设置来扩大 Theil-Sen (TS) 方法对学术界、分析师和从业者社区的影响。该研究包括一个以非常基本的术语描述 TS 模型的附录和 SAS 代码，以帮助读者实施 TS 模型。该研究还提出了一种替代方法来缩小或缩放变量。

设计/方法/方法

使用回归分析和二项式检验相结合的自然存档。

发现

二项式检验结果支持这样的假设，即当收益波动性较低时，朴素不变模型的预测性能至少等于或优于普通最小二乘 (OLS) 模型。然而，结果不支持 TS 模型的相同假设，也不支持当现金流波动率高时 OLS 和 TS 模型将优于朴素无变化模型的假设。尽管如此，该研究对文献做出了显着贡献，因为结果表明，在 20 个二项式检验中的 18 个中，朴素模型的性能至少与 OLS 和 TS 模型一样好。此外，结果表明 TS 模型的性能始终优于 OLS 模型。

研究限制/影响

结果可推广到美国公司，可能不适用于非美国公司。

实际影响

TS 方法对 OLS 有利，因为结果对异常值观察具有鲁棒性，并且不存在异方差性。鉴于使用迄今为止很少受到关注的模型（即 TS），以及提供模型力学的细节，研究人员会发现这项研究很有用。研究人员的一个好处是该研究包括用于实施该程序的 SAS 代码。

社会影响

对替代预测方法的认识可能会导致在某些情况下改进预测结果。该研究还有助于整个金融界，因为提高预测能力对所有资本市场参与者都很重要，因为它们可以提高市场效率。

原创性/价值

尽管存在用于检查样本外收益预测的健康文献，但在进行额外分析之前，文献缺乏一个简单问题的答案：结果是否比天真的无变化预测的结果更好？目前的研究强调，朴素无变化预测是可能的最基本模型，研究人员必须首先确定更复杂模型的优越性，然后再进行进一步分析。