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The analysis covers different time horizons to predict the likelihood of failure. Significance

The authors employ multiple models to forecast the probability of death, including traditional credit scoring models, machine learning models, and time-series methods. 113744

The study builds upon the Zero Price Probability model developed by Fantazzini et al. (2008) to compute the probability of default based solely on market prices. The analysis covers different time horizons to predict

This research paper addresses the high mortality rate of cryptocurrency projects. It focuses on developing models to forecast the probability of a "crypto coin" (specifically, cryptocurrencies and tokens) becoming "dead"—meaning they lose significant value, are abandoned by developers, or are delisted from exchanges. Key Aspects of the Paper (2008) to compute the probability of default based

The paper explores various definitions of dead coins, ranging from standard academic interpretations to practical indicators used in the industry.

This paper is significant for investors and analysts trying to navigate the volatile cryptocurrency market, as it provides a framework to quantify risk in a space where many assets fail.