General-Purpose Technology and Speculative Bubble Detection
Haiqiang Chen, Li Chen, Difang Huang, Yuexin Li, Zhengjun Zhang
TLDR
A new method for detecting speculative bubbles accounts for general-purpose technology adoption, correcting existing test distortions and re-evaluating historical rallies.
Key contributions
- Shows leading bubble tests are severely distorted by general-purpose technology (GPT) adoption.
- Proves GPT adoption makes fundamental prices locally explosive, contaminating test distributions.
- Proposes a fundamental-versus-speculative decomposition using observable technology proxies.
- Empirically, the new method eliminates AI rally speculation and confirms the dot-com bubble peak.
Why it matters
This paper offers a crucial improvement for detecting speculative bubbles, especially with transformative technologies like AI. It prevents misinterpreting fundamental growth as speculation, providing a more accurate tool for policymakers and investors to assess market stability.
Original Abstract
We show that the leading bubble test suffers 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, contaminating the test's limit distribution with a non-centrality parameter proportional to the shock's peak. We propose a fundamental-versus-speculative decomposition that projects prices onto observable technology proxies and applies the test to the residual. Empirically, the decomposition eliminates evidence of speculation in the 2020-2025 AI rally while confirming a speculative peak confined to December 1999-March 2000 in the dot-com episode.
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