The good and evil of algos: Investment-to-price sensitivity and the learning hypothesis

Nihad Aliyev, Fariz Huseynov and Khaladdin Rzayev

Journal of Corporate Finance, 2025, vol. 94, issue C

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

Keywords: Managerial learning; Investment-to-price sensitivity; Algorithmic trading; Real effects of algorithmic trading (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:corfin:v:94:y:2025:i:c:s0929119925001026

DOI: 10.1016/j.jcorpfin.2025.102834

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