Analysis of Crypto-assets, Blockchain Investor Protection, and U.S. Market Risks using the Mlogit Classifier Model

Abstract

We present insights into novel and complex issues regarding cryptocurrency activities, the related investor protection, and blockchain market risks. Crypto digital assets embody global economic ambition with their significant growth and creativity levels. This study employs a novel research approach using multinominal logit (mlogit) classifier modeling techniques to present unique findings regarding crypto-assets. The machine learning model confirmed better accuracy compared to previous research studies. These findings could contribute to a better understanding of the impact of business consumers on cryptocurrencies and blockchain used by business experts and policymakers worldwide. The research results should help future studies develop more machine learning models to ensure more accurate findings and discussions. The mlogit method research presented here confirms that business artificial intelligence methods and human domain knowledge interpretation can help current business leaders to better understand essential business decisions and their significant role in modern business behavioral prescriptive analytics. We derive important perspectives about cryptocurrency and blockchain strategy improvements, which may produce positive policy changes by enhancing the quality of investor protection in blockchain worldwide.