Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2025

Abstract

With the rapid expansion of the blockchain ecosystem, crypto asset valuation has become an essential area of study for investors and institutions. Here, we introduce a deep learning framework that was designed to predict the value index of crypto assets by integrating intrinsic value variables and decomposing market prices into value and sentiment components. The Crypto Asset Value-indexing Model (CAVM) was applied to Ethereum’s cryptocurrency ETH to demonstrate its effectiveness. Four econometric tests were conducted to verify the informativeness, predictiveness, and reasonability of the generated value indices, as well as the efficiency of price decomposition. Our findings suggested that the value index can serve as a reliable proxy for the intrinsic value of crypto assets, offering a benchmark for investment decisions, consumption, financial reporting, and potential tax implications. Additionally, this research contributes to the literature on asset valuation by proposing a novel method that applies deep learning techniques to intangible assets traded in secondary markets. By utilizing the end-to-end nature and directed acyclic graph structure inherent to deep learning models, we enhance the modeling process with customized loss functions and regularization mechanisms.

Keywords

asset valuation, block chain, crypto asset, deep learning, market sentiment

Discipline

Econometrics | Finance

Research Areas

Macroeconomics

Publication

Quantitative Finance and Economics

Volume

9

Issue

2

First Page

479

Last Page

505

ISSN

2573-0134

Identifier

10.3934/QFE.2025016

Publisher

AIMS Press

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.3934/QFE.2025016

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