Predicting Capital Market Returns Using the Learning Model of Levenberg-Marquardt, Gradient descent and ARIMA Algorithm

The present study compares and predicts the predictive ability of the capital market based on the learning pattern of the Levenberg-Marquardt algorithm, the Gradient descent and the ARIMA Algorithm. For this purpose, market data were used in the period from 1394 to 1397, and more than 75% of these data were used as training data prior to 1397, and one year end data were used as data. The results of the evaluation of the research data show that artificial neural networks have a high capacity for price prediction. The results also showed that in both training data series from 1394 to 1396 and experimental of 1397 the comparison of the results and performance of ARIMA neural networks (ARIMA) showed that the neural network had higher predictive power in Comparing with the performance and prediction accuracy of two types of neural networks with the Levenberg-Marquardt learning algorithm and the Gradient descent learning algorithm using the Levenberg-Marquardt learning algorithm has been able to increase the neural network prediction accuracy And reduce its error, so, the results of the present study show, the Levenberg-Marquardt learning algorithm improves the predictive power of the neural network.