I study the effect of the implementation of the SEC’s EDGAR system on information asymmetry among investors. The SEC adopted EDGAR to decrease acquisition costs of mandatory filings. However, disclosure theory suggests that, even when acquisition costs are low, integration costs (i.e., costs necessary to filter and interpret information signals) may be so high that less sophisticated investors are disadvantaged, relative to their sophisticated peers. Consistent with this theory, I find evidence that EDGAR increased information asymmetries among investors. This result is more pronounced for firms with higher integration costs—i.e., those with more complex filings and filings that have higher information content—as well as for firms with lower analyst coverage. Overall, my results suggest that, although EDGAR lowered acquisition costs for all investors, it also benefited some investors at the expense of others. Data Availability: Data are available from the public sources cited in the text.

We study firms’ decisions to provide non-GAAP income statements and the information environment consequences of Securities and Exchange Commission (SEC) comment letters directing them to stop disclosing such statements. We find firms voluntarily disclose non-GAAP income statements when firm and disclosure complexity, analyst following, and institutional ownership are higher. Using a difference-in-differences design, we find that, after firms stop disclosing full non-GAAP income statements at the direction of the SEC, the informativeness of non-GAAP earnings and overall earnings announcements decreases, information asymmetry increases, and analyst forecasts become less accurate and more disperse. We also find that firms not receiving a comment letter are more likely to stop providing non-GAAP income statements after an industry peer receives a non-GAAP income statement comment letter. Overall, our evidence suggests SEC comment letters about non-GAAP income statements appear to worsen the information environment for firms previously providing those statements. Data Availability: Data are available from the public sources cited in the text.

Less-informed investors face greater costs of processing earnings news into actionable information. Our findings suggest financial analysis on social media reduces less-informed investors’ disclosure processing costs. We document an attenuated spike in earnings announcement (EA) information asymmetry for quarters containing more financial analysis on social media in the weeks prior to the EA. Cross-sectional evidence suggests this finding is stronger when coverage from traditional intermediaries is lower, for financial analyses written by more credible authors, and for financial analyses that are more likely relevant to evaluating the EA. Further evidence suggests retail trades, but not institutional trades, at EAs are significantly more profitable in quarters with greater financial analysis on social media, consistent with financial analysis on social media benefitting traders who are otherwise less-informed. Overall, our evidence suggests that financial analysis on social media plays an important role in aiding less-informed investors by helping them better process EA news.

We examine commonly used indicators of aggressive non-GAAP exclusions and find that the majority perform poorly at identifying low-quality exclusions in terms of decision usefulness for investors. We propose a new firm-quarter-specific indicator that identifies instances in which GAAP earnings quality is high (i.e., when firms have less need to provide non-GAAP metrics) but managers disclose non-GAAP earnings anyway. Our new indicator is easy to calculate, requires minimal data, and performs far better at identifying low-quality exclusions than indicators used in prior research. Using our indicator, we find instances in which managers exclude earnings components that are decision useful, consistent with regulators’ concerns about the quality of some non-GAAP earnings disclosures. Our results are robust to a variety of specification checks. Data Availability: Data are derived from a combination of publicly available sources referenced in the article and third-party subscription data bases.