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.
Highlights
Information processes are becoming one of the most important components of human life
We used a technical method of stock market analysis and various mathematical models for predicting price performance based on the theory of time series
Neural networks are already widely used by financial corporations
Summary
Information processes are becoming one of the most important components of human life. The modern development of information technologies has resulted in increasing number of investors, who prefer interactive approaches for securities auction, which allows sending real-time orders for securities sale or purchase to their brokers using a computer. Each financial market is posed a challenge to study and put into practice various methods of analyzing and predicting a price performance of the stock market, as well as to find out which of price forecasting tools are the most accurate and reliable. We used a technical method of stock market analysis and various mathematical models for predicting price performance based on the theory of time series. The practical significance of the obtained results is the ability to analyze and predict the value of financial assets which are expected to be invested[7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
