The Good and Evil of Algos: Investment-to-Price Sensitivity and the Learning Hypothesis

Abstract

We investigate how firm managers’ learning from share prices is influenced by two different types of algorithmic trading (AT) activities in their shares. We find that liquidity supplying AT enhances managers’ ability to learn from share prices by encouraging information acquisition in markets, leading to increased investment sensitivity to share prices. However, liquidity-demanding AT impairs this learning process by discouraging information acquisition. Firm operating performance correspondingly improves with liquidity-supplying AT and deteriorates with liquidity-demanding AT in firm’s shares. Using NYSE’s staggered Autoquote implementation as an exogenous variation in AT, we establish causality. Our findings demonstrate AT’s significant impact on real economic outcomes.

Keywords: Algorithmic trading, investment-to-price sensitivity, real effects of algorithmic trading, Managerial learning

JEL Classification: G14, G31, G12