Development of an Information and Analytical System for Predicting Price Fluctuations and Long-Term Trends Using Generative-Adversarial Neural Networks

Abstract

Deep learning methods (DML) have been widely used in financial fields recently, such as stock market forecasting, balance the portfolio, financial information processing, and transaction execution strategies. Stock market forecasting and effective trading strategy construction, when using deep learning, are the most popular ways of applying DML in the field of finance. Against the background of the general development of the Russian stock market, the study and investigation of its price dynamics is a highly promising direction for analyzing and forecasting the value of financial assets in which it is planned to invest money. In this study, a new architecture of a conditional generative-adversarial neural network (GAN) with a multi-level perceptron (MLP) as a discriminator and a long short-term memory (LSTM) as a generator for determining trends is proposed. The Box-Jenkins method (ARIMA) is used to determine the confidence interval.