The efficient market hypothesis when time travel is possible
Joshua S. Gans
Economics Letters, 2025, vol. 248, issue C
Abstract:
This paper extends the Efficient Markets Hypothesis (EMH) into a novel setting in which traders can travel back in time to exploit future information. We consider a fully specified trading model with risk-neutral, rational investors and a single, infinitely-lived asset. Agents can, at a fixed cost, build time machines, travel to the past, and trade using knowledge of future dividends and prices. Under a self-consistent, single-timeline theory of time travel, we show that no arbitrage opportunities can persist. In equilibrium, the asset price fully reflects not only all current and past information but also all future information that could have been acted upon by backward-travelling arbitrageurs. We state and prove an Extended Efficient Markets Hypothesis (EEMH), showing that time travel does not undermine but rather reinforces the no-arbitrage conditions at the heart of the EMH. We conclude by discussing the relevance of known constraints on the EMH, alternative theories of time travel and the challenges of empirically identifying time-travelling traders. Journal of Economic Literature Codes: G14, Z10.
Keywords: Time travel; Efficient market hypothesis; Single timeline; No-arbitrage condition; Preemption (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176525000461
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:248:y:2025:i:c:s0165176525000461
DOI: 10.1016/j.econlet.2025.112209
Access Statistics for this article
Economics Letters is currently edited by Economics Letters Editorial Office
More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().