ABSTRACT I investigate whether algorithmic trading (AT) affects the provision of management guidance. Existing research finds that AT decreases fundamental information acquisition before earnings announcements and consequently reduces the informativeness of prices. To compensate for reduced information acquisition, I predict and find that managers at firms with more AT activity increase the quantity and quality of guidance issued at earnings announcements. Evidence is consistent with managers responding to reduced information acquisition, as opposed to changes in liquidity, and results suggest guidance in response to AT is effective at reducing information asymmetry. These findings identify a new channel through which AT affects stock price informativeness by documenting a link to managers’ disclosure decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; G19; G10.

ABSTRACT We provide initial evidence that stock exchange procedures around closing auctions advantage speed traders at the expense of auction participants. We show that, on Nasdaq and NYSE Arca, 4:00 pm earnings releases result in informed trading in the continuous regular-hour session in the short window between 4:00 pm and the closing auction; this trading subsequently moves closing prices in the direction of the earnings news. The ability of speed traders to submit 4:00-pm-news orders to the auction through the continuous session earns them up to 1.5 percent profit and creates an unlevel playing field because most auction participants are not allowed to cancel their orders. When stock exchanges recommended that firms delay disclosures until after the market closes, those with higher institutional ownership were more likely to voluntarily do so. Our study has implications regarding the timing of information releases and the design of the closing process.

This study investigates whether brokerage houses appear to provide stock tips to politicians. Our results indicate that trades by politicians who are politically connected to the brokerage house where the trade is executed are more profitable. Our estimates suggest that these connected trades earn an incremental 0.3% over a five-day window relative to the politician's average profitability. Given the average number of trades our sample politicians execute in a year, the 0.3% return per trade translates to an incremental $3,411 in trading profits each year. We provide additional support by investigating the frequency and differential profitability of politicians' trades immediately before the brokerage house issues a revised recommendation, as well as during a period when Goldman, Sachs & Co. was sanctioned for providing stock tips to high priority clients. Additional tests suggest that brokerages may provide stock tips to politicians in exchange for favorable legislative outcomes or political information.

We examine whether investor inattention influences managers’ non-GAAP earnings disclosures. Hirshleifer and Teoh’s (2003) theoretical model predicts that managers are more likely to disclose upwardly-biased non-GAAP metrics when investors are inattentive. Employing a measure that captures exogenous variation in institutional investor inattention, we find that managers are more likely to provide non-GAAP disclosures, particularly those where non-GAAP earnings are greater than GAAP earnings, when inattention is high. Moreover, inattention is positively associated with the magnitude of non-GAAP exclusions, which suggests managers present better non-GAAP performance to inattentive investors. Consistent with managers exploiting investors’ limited attention, we find that stock prices respond more strongly to non-GAAP exclusions when investors are inattentive and that managers are more likely to respond to inattention with income increasing non-GAAP disclosures if they sell shares following the disclosure. Taken together, these findings suggest that managers respond opportunistically to inattentive institutions by disclosing aggressive non-GAAP earnings metrics.