Forecasting of Financial Market Indicators Using Artificial Intelligence Systems

Financial markets are the foundation of the modern economy, facilitating the movement of capital between its owners and borrowers, thereby enabling businesses to develop and innovate.These markets provide a platform for price discovery and risk management.At their core, they act as intermediaries between those who have surplus funds and those who need financing.The functioning of financial markets provides borrowers with a mechanism for accessing capital and capital owners with an opportunity to earn a return on their investments.This interaction between capital owners and borrowers is essential for economic growth and development.Rapid changes in the global economy and the impact of information technology have brought significant changes to the way financial markets function, simplifying communication between market participants and increasing efficiency.However, these changes have also created a number of risks caused by uncertainty.The use of artificial intelligence systems in building a model for forecasting financial indicators based on processing a large data set can significantly improve the accuracy and quality of forecast data.The authors have conducted a comparative analysis of forecasting methods, identifying their respective strengths and weaknesses, and the potential for their application to forecasting indicators.Based on the findings of this study, the authors propose a model for forecasting financial market indicators using artificial intelligence systems.The model comprises two components: 1) a time series LSTM, a network with a long short-term memory, designed to analyse and forecast financial time series data by capturing time dependencies and patterns inherent in market indicators; and 2) an NLP-Enhanced LSTM, another LSTM network supplemented with the results of BERT processing, which is specifically designed to analyse text № 6(34) 2024 718 data and their impact on financial market dynamics.The proposed model, which combines the capabilities of LSTM time series analysis with information obtained from BERT-processed headlines, enables the identification and consideration of the relationship between market trends, sentiment and external factors.Testing the proposed model on the data of Tesla has demonstrated its suitability for forecasting the price of Tesla's shares.