机器学习用于绩效归因陈述的可行性分析

IF 4.1 3区 管理学 Q2 BUSINESS International Journal of Accounting Information Systems Pub Date : 2023-03-01 DOI:10.1016/j.accinf.2022.100597
Anil Berkin , Walter Aerts , Tom Van Caneghem
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引用次数: 1

摘要

我们研究了机器学习方法在企业报告中用于归因内容和框架分析的可行性。我们在自上而下的三级层次设置中测试了五个广泛使用的监督机器学习分类器(天真贝叶斯、逻辑回归、支持向量机、随机森林、决策树)的性能,以(1)识别性能相关语句;(2) 检测其中的归因;以及(3)对归因陈述的内容进行分类。该训练集包括来自上市公司管理评论报告语料库的手动编码语句。归因包括句内和句间归因陈述。结果表明,对于句内和句间属性,我们最准确的分类器(即支持向量机)的F1分数在76%到94%之间变化,这取决于属性的识别、检测和分类水平以及属性的内容特征。此外,我们评估了分类器的分层性能,为属性陈述的更全面的分类过程提供了见解。总的来说,我们的研究结果表明,机器学习方法可以通过提供一种更有效的方法来检测和分类与绩效相关的归因陈述,从而促进叙事披露分析。我们的研究结果为实施机器学习方法研究归因行为和相关印象管理提供了基础,从而为会计和管理文献做出了贡献。
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Feasibility analysis of machine learning for performance-related attributional statements

We investigate the feasibility of machine learning methods for attributional content and framing analysis in corporate reporting. We test the performance of five widely-used supervised machine learning classifiers (naïve Bayes, logistic regression, support vector machines, random forests, decision trees) in a top-down three-level hierarchical setting to (1) identify performance-related statements; (2) detect attributions in these; and (3) classify the content of the attributional statements. The training set comprises manually coded statements from a corpus of management commentary reports of listed companies. The attributions include both intra- and inter-sentential attributional statements. The results show that for both intra- and inter-sentential attributions, F1-scores of our most accurate classifier (i.e., support vector machines) vary in the range of 76% up to 94%, depending on the identification, detection and classification levels and the content characteristics of attributions. Additionally, we assess the hierarchical performance of classifiers, providing insights into a more holistic classification process for attributional statements. Overall, our results show how machine learning methods may facilitate narrative disclosure analysis by providing a more efficient way to detect and classify performance-related attributional statements. Our findings contribute to the accounting and management literature by providing a basis for implementing machine learning methodologies for research investigating attributional behavior and related impression management.

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来源期刊
CiteScore
9.00
自引率
6.50%
发文量
23
期刊介绍: The International Journal of Accounting Information Systems will publish thoughtful, well developed articles that examine the rapidly evolving relationship between accounting and information technology. Articles may range from empirical to analytical, from practice-based to the development of new techniques, but must be related to problems facing the integration of accounting and information technology. The journal will address (but will not limit itself to) the following specific issues: control and auditability of information systems; management of information technology; artificial intelligence research in accounting; development issues in accounting and information systems; human factors issues related to information technology; development of theories related to information technology; methodological issues in information technology research; information systems validation; human–computer interaction research in accounting information systems. The journal welcomes and encourages articles from both practitioners and academicians.
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