Browsing by Subject "Algorithmic Trading"

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.