利用非财务信息披露改进公司业绩预测:一种机器学习方法

IF 3.2 Q1 BUSINESS, FINANCE
Usman Sufi, Arshad Hasan, Khaled Hussainey
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引用次数: 0

摘要

目的本研究的目的是检验将非财务信息披露(如叙述性信息披露基调和公司治理指标)纳入财务预测模型是否能增强对公司业绩的预测。本研究利用随机森林和随机梯度提升两种机器学习技术,通过这三种模型进行预测。研究结果表明,叙述性信息披露基调和公司治理指标都显著提高了公司业绩财务预测模型的准确性。实际意义我们的研究结果通过将非财务信息披露确立为未来公司业绩的可靠预测指标,为恢复投资者对高度不确定的巴基斯坦市场的信心提供了启示。因此,我们鼓励投资者在做出投资决策时更多地关注这些信息披露。此外,他们还敦促监管机构促进和加强此类非财务信息的报告。原创性/价值本研究解决了非财务信息披露在公司业绩预测中被忽视的问题,以及与使用机器学习技术相关的公司治理文献稀缺的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the prediction of firm performance using nonfinancial disclosures: a machine learning approach

Purpose

The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Design/methodology/approach

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Findings

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Practical implications

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

Originality/value

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

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来源期刊
CiteScore
5.80
自引率
13.00%
发文量
38
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