Integrating AI and Methodological Approaches for Enhanced Predictive Analytics in Financial Markets

Abstract

The incorporation of AI technology in the factors of the financial market can increase the effectiveness and precision of the predictions. This research assesses how the application of AI methodologies affects predictive analytics in the financial industry with emphasis on the following aspects; AI utilization, satisfaction level with AI-generated predictions, and AI connection to conventional financial forecasting. Thus, this research offers valuable knowledge about the fusion of AI and classical approaches to examining financial market forecasts. The study used a cross-sectional survey design with a respondent sample of 250 financial professionals such as traders, analysts, and portfolio managers. Participants were administered an AI tool usage, satisfaction, and challenges self-completion questionnaire that looked like a structured questionnaire. The outlines of the methods of data analysis include Descriptive statistics and inferential statistics: regression analysis; Analysis of variance; and Principal Component Analysis. Through the analysis of the normality tests, it was evident that normal distribution was absent in several variables thus requiring non-parametric tests. The coefficient of internal consistency using Cronbach’s Alpha proved to be very low at 0. 019 therefore implying that survey items need to be refined. From the PCA results it was predicted that the first element accounted for 37% of the variance. 49 % of the variation which means that many factors affect the adoption and efficiency of applications of AI in financial markets. Hence, the study emphasizes the ideas of artificial intelligence in the context of financial market prediction and the further development them, as well as the improvements in the survey methodology and the methods of data analysis. AI shows promise, but other extensive study is required to show the benefits of AI.