[phpBB Debug] PHP Warning: in file [ROOT]/phpbb/session.php on line 594: sizeof(): Parameter must be an array or an object that implements Countable
[phpBB Debug] PHP Warning: in file [ROOT]/phpbb/session.php on line 650: sizeof(): Parameter must be an array or an object that implements Countable
[phpBB Debug] PHP Warning: in file [ROOT]/includes/functions.php on line 5277: Cannot modify header information - headers already sent by (output started at [ROOT]/includes/functions.php:3903)
[phpBB Debug] PHP Warning: in file [ROOT]/includes/functions.php on line 5277: Cannot modify header information - headers already sent by (output started at [ROOT]/includes/functions.php:3903)
[phpBB Debug] PHP Warning: in file [ROOT]/includes/functions.php on line 5277: Cannot modify header information - headers already sent by (output started at [ROOT]/includes/functions.php:3903)
[phpBB Debug] PHP Warning: in file [ROOT]/includes/functions.php on line 5277: Cannot modify header information - headers already sent by (output started at [ROOT]/includes/functions.php:3903)
113744 Online

113744 Online

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

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. 113744

The analysis covers different time horizons to predict the likelihood of failure. Significance This research paper addresses the high mortality rate

The paper explores various definitions of dead coins, ranging from standard academic interpretations to practical indicators used in the industry. The analysis covers different time horizons to predict

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

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.