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Title: Machine Readership and Financial Reporting Decisions
Citation Type: Miscellaneous
Publication Year: 2023
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Abstract: Machine learning and AI technologies can identify data patterns related to financial misreporting that traditional methods cannot detect. With rising machine readership of corporate financial statements, managers may have less incentive to engage in financial misreporting. This study empirically investigates this possibility and finds a reduction in financial misreporting when machine readership is higher. These results hold after addressing potential identification issues. The impact of machine readership is more pronounced in cases where machine learning offers greater advantages, such as complex financial statements and the availability of alternative data. Notably, for misreporting patterns detectable by traditional linear models, machine readership offers no incremental disciplining, indicating that the strength of machines, instead, lies in recognizing non-linear and high-dimensional patterns. Furthermore, we observe an overall decrease in misstatements, suggesting that machine readership enhances overall financial reporting quality rather than prompting managers to shift misreporting to areas beyond machine detection. This paper highlights the disciplining effect of machine adoption in the capital market on financial reporting.
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Authors: Cao, Sean; Liang, Ying; Moon, Jason
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Data Collections: IPUMS USA
Topics: Education, Labor Force and Occupational Structure, Methodology and Data Collection
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