Technology Fundamentals and False Bubble Detection: Evidence from Dot-Com and AI Episodes

Haiqiang Chen, Li Chen, Difang Huang, Yuexin Li and Zhengjun Zhang

Papers from arXiv.org

Abstract: We show that widely used bubble tests, most prominently the PSY framework, suffer severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, thereby altering the asymptotic null distribution of the test statistic and causing the standard bubble test to overreject. We propose a technology-adjusted diagnostic that removes an estimated technology component from measures of productivity, IT-investment, and patents before testing the residual. The adjustment is conservative: because a boom can itself raise these technology measures, a rejection remains robust to such feedback, whereas a non-rejection only bounds residual explosiveness. Dot-com residual explosiveness concentrates in December 1999-March 2000; the 2020-2025 AI rally shows no residual explosiveness in our sample across baseline and sensitivity checks.

Date: 2026-04, Revised 2026-07
New Economics Papers: this item is included in nep-ecm and nep-fdg
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