Non-Fungible Token Bubble Prediction using Extended Log-Periodic Power Law Model

Abstract

Non-fungible token (NFT) bubbles are a problematic issue, and this study aims to predict NFT bubbles using an extended log-periodic power law singularity (LPPLS) model. The classic LPPLS model targets the endogenous nature of bubbles caused by the mimetic behavior of investors without external influences; however, the extended model attempts to incorporate exogenous influences. First, we compare the performance of the two models for NFT price prediction. The exogeneous variable in the extended model is cryptocurrency volatility. Then, we calculate the bubble confidence using both models. The results show that the explanatory power and forecasting accuracy of the extended model are superior in all projects. We also find that the bubble confidence indicator reinforces the results of bubble prediction.