Simulating and predicting factors affecting the price of Bitcoin using the GSA crowd intelligence algorithm and artificial neural network

Subject Areas : Financial Economics

Seyed Aziz Arman ¹ , sayed amin Mansouri ^{2 *} , عارف بهروز ³

1 - Professor of economics, faculty of Economics and social science, Shahid Chamran University of Ahvaz, Iran
2 - Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 - Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Received: 2024-12-24 Accepted : 2025-04-14 Published : 2025-12-09

Keywords: Simulation, Bitcoin Remars, Crowd Intelligence Algorithm, GSA.,

Abstract :

Considering the increasing growth of Bitcoin price and the fact that the cryptocurrency market is also associated with risks; investors should carefully analyze and manage risk and make their choices with careful consideration. In this research, using the Gravity Search Algorithm (GAS) and artificial neural network, the Bitcoin price model was simulated and predicted in the previous two periods and the COVID-19 pandemic period. For this purpose, the number of daily Bitcoin transactions, the daily mining difficulty of the Bitcoin network, the number of Bitcoins in circulation, the daily price of gold, S&P500, the New York Stock Exchange Stock Price Index, the exchange rate between the dollar and the euro, the number of posts sent on Twitter, and the number of searches for words related to Bitcoin on Google were used as variables affecting the Bitcoin price. The results of the study show that based on the criteria of fitting accuracy, especially the average absolute error percentage, before the coronavirus pandemic, the superior model was the Cobb-Douglas form and had higher simulation accuracy, and with the coronavirus pandemic, the superior model did not change and was still the Cobb-Douglas form. On the other hand, it was found that the simulation accuracy of the superior model increased after the coronavirus pandemic. The error rate based on the artificial neural network is 1%, in other words, the prediction accuracy is 99%, which shows that the artificial neural network has a very high prediction accuracy.

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