Using Mathematical Models to Predict the Efficiency of Crypto Projects in the Virtual Economy

Abstract

The study's relevance is stipulated by the need to improve the efficiency of analysis and forecasting of crypto assets in the modern digital economy, which is characterized by high volatility and dynamism of development. Traditional mathematical models have limitations in considering the multifactorial nature of market conditions, which creates gaps in long-term forecasting.

The study aims to analyze an integrative mathematical model that combines crypto projects' technical, financial, and market characteristics and accurately forecasts their efficiency under different virtual economy scenarios.

The research methods include reviewing modern mathematical models, analyzing success evaluation criteria, and creating a new conceptual model. To illustrate the practical significance, examples of well-known crypto projects such as Ethereum, Binance Smart Chain, and Polkadot were used.

The study's results show that the proposed model provides a comprehensive approach to performance analysis. It describes the relationship between innovative technologies, market activity, and prospects for long-term project development. Examples demonstrate how the model can assess risks and determine optimal strategies.

The conclusions state that the proposed model helps to reduce uncertainty in management decision-making and the formation of investment strategies. It is considered a promising forecasting tool due to its adaptability to the market conditions of different blockchains.

Prospects for further research include improving the proposed model by integrating additional parameters, such as user social interactions and the impact of regulatory changes. In addition, it is advisable to expand its capabilities to process large amounts of data in real-time, which will increase practical efficiency and provide more accurate forecasts in the current crypto asset market.