Decoding herding dynamics in the generative AI investment amid key technological advancements: A timeline perspective

Haibo Wang

Finance Research Letters, 2024, vol. 64, issue C

Abstract: This study, using two herding dynamics metrics and Glosten–Jagannathan–Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model, forecasts market trends, captures asymmetric volatility, and reveals the generative AI (GenAI) ecosystem's impact on individual assets’ returns. Results of this study highlight distinctive traits of each GenAI equity, crucial for strategic positioning, especially for investors in tech stocks tied to GenAI. Herding behavior exhibits greater strength in the initial four months post-announcement of ChatGPT, gradually diminishing. GJR-GARCH reports that most of GenAI stocks do not exhibit statistically significant leverage effects. These findings provide valuable insights to navigate the dynamic landscape of GenAI investments.

Keywords: Herding dynamics; Generative AI (GenAI); Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH) model; Market trends; Leverage effects; Drawdown; ChatGPT; Transformer; Cross-sectional standard deviation (CSSD); Cross-sectional absolute deviation (CSAD) (search for similar items in EconPapers)
JEL-codes: C51 G10 G11 G41 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004628

DOI: 10.1016/j.frl.2024.105432

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