Abstarct: Abstract In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investi-gate and compare the performance of various types of activator functions available in artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options avail-able in the Tehran Stock Exchange market (in 1400) as the network output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are compared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this research show that among the activator functions available in this research, the ReLU activator function per-forms better than other activator functions.