Algorithmic Trading and Forward-Looking MD&A Disclosures

This study examines how algorithmic trading (AT) affects forward-looking disclosures in Management Discussion and Analysis (MD&A) of annual reports. We predict and find evidence that AT relates negatively to modifications in year-over-year forward-looking MD&A disclosures. This evidence is consistent with AT reducing investors' demand for fundamental information, which reduces managers' incentives to supply costly forward-looking disclosures. Cross-sectional tests show that this negative relation is more pronounced for firms with higher product market power, those in good news settings, and those facing lower proprietary costs. Finding stronger evidence in settings where theory and prior research predict the relation should be more pronounced helps to strengthen our conclusion. We further validate our conclusion by demonstrating that investors' fundamental information searches are a channel through which AT affects forward-looking disclosures and by using the SEC's Tick Size Pilot Program as an exogenous shock to AT. Overall, our study demonstrates that AT is a contributing factor to regulators' concerns over the diminishing usefulness of forward-looking information in MD&A disclosures.