The Capital Market Effects of Machine-Readable Data Errors: Evidence from XBRL US Data Quality Committee Rules

Motivated by Section 5825 of the 2022 Financial Data Transparency Act, I investigate the capital market effects of XBRL errors. Using XBRL US Data Quality Committee (DQC) rule errors, the SEC’s indicator of low-quality, machine-readable data, in 10-K XBRL filings, I find no evidence of an increase in information asymmetry or a decrease in the efficiency of stock price formation for filings with an error. Additional analyses reveal the findings are driven more by a weak association between DQC rule errors and a material decrease in the precision of value-relevant accounting information and less by limited investor usage of machine-readable financial data. Overall, this study provides valuable insights given this component of financial reporting quality is under increasing regulatory scrutiny, yet I document no adverse capital market effects between filings with and without XBRL errors. This suggests that investors will not see significant capital market benefits from the regulatory focus on increasing the quality level of XBRL filings through the elimination of DQC rule errors.