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Journal of Corporate Finance Vol. 94 2025

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

Nihad Aliyev1; Fariz Huseynov2,3; Khaladdin Rzayev4,5,6,7

1 University of Technology Sydney · 2 Dakota State University · 3 North Dakota State University · 4 Koç University · 5 Systemic Risk Centre · 6 London School of Economics and Political Science · 7 University of Edinburgh

open access

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.

DOI
10.1016/j.jcorpfin.2025.102834
Volume
94
Pages
102834
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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