Talk · Procedia Computer Science · January 1, 2024

Assessment of the impact of big data analysis on decision-making in stock trading processes

Руслан Калашников, Amandyk Zhankozhauly Kartbayev

Abstract

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation.

Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesForecasting Techniques and Applicationsstock price predictionrisk assessmentartificial neural networks

Assessment of the impact of big data analysis on decision-making in stock trading processes

Abstract

More by Amandyk Kartbayev