Robust approach to earnings forecast: A comparison

IF 3.4 3区经济学 Q1 ECONOMICS

Journal of Forecasting Pub Date : 2024-02-21 DOI:10.1002/for.3085

Xiaojian Yu, Xiaoqian Zhang, Donald Lien

引用次数: 0

Abstract

This paper applies three robust approaches, namely, the MM estimation, the Theil–Sen estimation, and the quantile regression, to generate earnings forecasts in Chinese financial market and evaluates the forecast accuracy of these three methods based on three forecasting criteria. We examine six forecasting models where the predicted variables include earnings per share, net income, and three profitability measures. We show that the three robust methods significantly outperform the OLS method. Moreover, the MM estimation and the quantile regression have better forecast accuracy than the Theil–Sen approach.

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稳健的盈利预测方法：比较

本文采用 MM 估计、Theil-Sen 估计和量子回归三种稳健方法生成中国金融市场的盈利预测，并根据三种预测标准评估了这三种方法的预测准确性。我们研究了六个预测模型，预测变量包括每股收益、净利润和三个盈利能力指标。我们发现，这三种稳健方法的预测结果明显优于 OLS 方法。此外，MM 估计法和量化回归法的预测准确性也优于 Theil-Sen 方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Forecasting Multiple-

CiteScore

5.40

自引率

5.90%

发文量

期刊介绍： The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.

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