Managerial Learning from Decoding Noisy Stock Prices: New(s) Evidence from Billions of Internet Article Reads

81 Pages Posted: 23 Oct 2022 Last revised: 15 Feb 2024

See all articles by Alan Kwan

Alan Kwan

The University of Hong Kong

Tse-Chun Lin

The University of Hong Kong - Faculty of Business and Economics

Po-Yu Liu

The University of Hong Kong - Faculty of Business and Economics

Date Written: February 14, 2024

Abstract

A long literature argues corporate managers learn from stock prices, but a firms’ learning process is challenging to observe. We present a novel test using firm-level readership of financial media articles as a measure of managerial learning behavior. We hypothesize that reading financial media, perhaps alongside other unobserved learning activities, helps managers interpret noisy signals in stock prices. We show that the classic Q-sensitivity of R&D expenditure increases by 26% when firms’ reading of financial articles increases by one standard deviation. This relationship is driven by reading from near the headquarters and by articles likely more informative to managers.

Keywords: big data, managerial learning, market feedback effects, financial news, R&D.

JEL Classification: C55, G11, G23, G24, Q01

Suggested Citation

Kwan, Alan and Lin, Tse-Chun and Liu, Po-Yu, Managerial Learning from Decoding Noisy Stock Prices: New(s) Evidence from Billions of Internet Article Reads (February 14, 2024). Available at SSRN: https://ssrn.com/abstract=4253237 or http://dx.doi.org/10.2139/ssrn.4253237

Alan Kwan (Contact Author)

The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
China

Tse-Chun Lin

The University of Hong Kong - Faculty of Business and Economics ( email )

Pokfulam Road
Hong Kong
China

Po-Yu Liu

The University of Hong Kong - Faculty of Business and Economics ( email )

Pokfulam Road
Hong Kong
China
64706505 (Phone)

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