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 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. To establish causality, we use NYSE's Autoquote implementation as a source of exogenous variation in AT. Our findings demonstrate AT's significant impact real economic outcomes.