Browsing by Author "Mayew, Bill"

In 2010, the Municipal Securities Rulemaking Board proposed a rule change requiring the display of current credit ratings on the EMMA website, a centralized repository of municipal bond information. Prior to the rule change, current credit ratings were freely available on individual rating agencies’ websites or on EMMA if municipalities provided relevant continuing disclosures, making it unclear whether the rule change would benefit investors. A difference-in-difference analysis reveals the rule change is associated with an 8 basis-point decrease in investor transaction costs. This effect is concentrated among the intended beneficiaries (retail investors) when credit risk information demand is high (long-maturity bonds) and current credit rating information on EMMA is low (no continuing disclosure of rating changes was provided on EMMA). The rule change appears to have helped retail investors become aware of current credit ratings by filling a disclosure gap on EMMA for municipalities without continuing disclosures of rating changes.

Prior literature finds that algorithmic trading (AT) benefits the financial market by improving liquidity and accelerating the incorporation of existing information into prices. This paper shows that AT also has negative real effects: it reduces the sensitivity of corporate investment to stock prices. Moreover, firms’ future operating performance deteriorates following periods of high AT activity. The evidence is consistent with algorithmic traders crowding out fundamental traders’ information acquisition, leading to less information in prices for managers to learn and hence worse investment efficiency. Supporting evidence is also observed among financial analysts, who also learn less from stock prices when making forecast revisions.