Machine learning improves accounting: discussion, implementation and research opportunities
成果类型：
Article
署名作者：
Bertomeu, Jeremy
署名单位：
Washington University (WUSTL)
刊物名称：
REVIEW OF ACCOUNTING STUDIES
ISSN/ISSBN：
1380-6653
DOI：
10.1007/s11142-020-09554-9
发表日期：
2020
页码：
1135-1155
关键词：
摘要：
Machine learning has been growing in importance in empirical accounting research. In this opinion piece, I review the unique challenges of going beyond prediction and leveraging these tools into generalizable conceptual insights. Taking as springboard Machine learning improves accounting estimates presented at the 2019 Conference of the Review of Accounting Studies, I propose a conceptual framework with various testable implications. I also develop implementation considerations panels with accounting data, such as colinearities between accounting numbers or suitable choices of validation and test samples to mitigate between-sample correlations. Lastly, I offer a personal viewpoint toward embracing the many low-hanging opportunities to bring the methodology into major unanswered accounting questions.
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