Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI

Indranil Ghosh, Esteban Alfaro-Cortés, Matías Gámez and Noelia García-Rubio

International Review of Financial Analysis, 2023, vol. 87, issue C

Abstract: Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.

Keywords: Non fungible tokens (NFT); Decentralized finance (DeFi); Feature engineering ensemble machine learning; Explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C53 F37 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:87:y:2023:i:c:s1057521923000741

DOI: 10.1016/j.irfa.2023.102558

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