Abstarct: Forecasting and investigating price behavior in the financial market has always been a challenge for researchers and activists in this field, on the other hand, due to the advancement of technology in the field of computer science and its widespread use in various sciences, it has provided fields for the use of artificial neural networks. Artificial neural networks are mathematical models that are inspired by the nervous system and the human brain, which have attracted the attention of researchers due to the more realistic performance of these models, and their various types have been used for prediction. In this research, to investigate the echo mode network and combining it with the PSO optimization algorithm known as PSO-ESN. We discuss their features and then use neural network models on the stock prices of domestic and foreign stock markets. We have considered the three financial markets of the Iranian Stock Exchange, the American Stock Exchange, and the Forex market as the target of this study, and one representative from each financial market (Mobarake Sepahan Steel Company shares from the Iranian Stock Exchange, Amazon Company shares from the American Stock Exchange, and the Euro-Dollar currency pair) America from the forex market (we have chosen the echo mode network and PSO-ESN hybrid model to check, and finally the echo mode network with the PSO-ESN hybrid model, according to the error measurement criteria that include: mean squared error, We compare and discuss the average absolute error, the criterion of the coefficient of determination and the level of accuracy, and finally we conclude that the combined PSO-ESN model and the ESN model have performed in price prediction.