Technology rhetoric and institutional ownership

Panayiotis C Andreou, Kyriakos Drivas, Dennis Philip, Geoffrey Wood

Research output: Contribution to journalArticlepeer-review

Abstract

This article compares actual R&D spend with the managerial rhetoric around technology and innovation contained within corporate disclosures of US-listed firms. We find that, whilst actual R&D spend and patents do not entice institutional investors to increase their stock holdings, firms that espouse technology and innovation in their corporate disclosures are quite successful in drawing in short-term investors. We frame this investor behaviour within the economics of expectation literature. While managers are incentivised to draw in capital, short-horizon investors are less likely to exert due diligence and are rather persuaded by a technology narrative—that is, a ‘gold rush’ effect. This explains our finding that when there is a sudden downturn with stock price crashes, short-term investors rush to withdraw their money from firms that ‘talk tech’. Our findings have implications for managerial rewards systems, especially when these encourage managerial hype.
Original languageEnglish
JournalCambridge Journal of Economics
DOIs
Publication statusPublished - 1 Nov 2024

Data Availability Statement

See the Appendix for a list of all the variables and their definitions of how they were collected and constructed. The data we employed came from several datasets, some of them are proprietary and some free. In particular, the following variables have been collected by Compustat: R&D, SIZE, MB, LEV, ROE, EARN, EARN_VOL, CAPEX, YOUNG_FIRM, TECHNOLOGY. The construction of the CRASH and GROWTH_OPTIONS variables employs both Compustat and CRSP data. The construction of the TECH_INNOV, CREATE, POS_TONE, UNC_TONE, R&D_NAR, R&D_NAR_1_7 and R&D_NAR_RED employs data from SEC Edgar. INST_TRA, INST_DED and INST_QIX employ data from Thomson Reuters Institutional (13F) Holdings and Professor Brian Bushee’s personal website. PATENTS, INNOV_EFF_UW and INNOV_EFF_W employ data from Kogan et al. (2017) and the USPTO’s Office of the Chief Economist. c.f. Kogan et al. (2017).

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