Stability of Cryptocurrency Using Machine Learning Algorithm

Abstract

The acceptance of cryptocurrencies as a medium of exchange has long been hindered by the volatility of their pricing. In this study, we investigate the utility of rebasing as a tool for maintaining bitcoin prices. To achieve price stability, we present a mathematical method with use of machine learning algorithm that periodically modifies the total supply of the cryptocurrency based on a selected benchmark. By modelling several market circumstances, we assess the performance of our suggested rebasing strategy and compare it to other price stabilisation techniques already in use. Our research demonstrates that rebasing can successfully control a cryptocurrency's price while preserving the integrity of its decentralised architecture. However, we also point out significant restrictions and difficulties in putting rebasing rules into practise, such as potential manipulation concerns and the requirement for community agreement. Our research adds to the ongoing discussion over whether cryptocurrencies are a practical form of exchange and offers guidance to investors, policymakers, and cryptocurrency enthusiasts. In contrast to conventional cryptocurrencies like Bitcoin or Ethereum, which are renowned for their extreme volatility, stable cryptocurrencies aim to offer a more dependable and predictable store of value. Stablecoins are frequently employed as a store of wealth, a payment method, or a hedge against swings in the cryptocurrency market. There are many ways artificial intelligence (AI) can be applied to improve the stability of cryptocurrencies. Algorithmic modifications to the money supply are one of the keyways that stablecoins use AI.