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From Melodies to Markets: Leveraging LSTM for Music-Driven Predictions in NFT Trading Dynamics
Abstract
This study investigates the correlation between the audio features of top-charting music and Non-Fungible Tokens (NFT) market dynamics, presenting a novel perspective within the realm of behavioral finance. Drawing on the regulatory focus theory and existing research on music's affective influence, the authors argue that popular music, as a reflection of society's collective regulatory focus, can significantly impact trading behaviours in NFTs, an asset class known for its susceptibility to emotional drivers and speculative activity. By employing a Long Short-Term Memory (LSTM) machine learning model and permutation importance technique, the analysis demonstrates that specific musical attributes—such as danceability, loudness, and mode—exhibit predictive power over daily NFT trading volumes. The study not only provides evidence of music's capacity to signal shifts in trading behaviors, offering innovative insights into the drivers of digital asset markets, but introduces a new interdisciplinary approach focusing on the collective regulatory focus reflected in the music.
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