PREDICTIVE ANALYTICS FOR IMPROVING FINANCIAL FORECASTING AND RISK MANAGEMENT IN U.S. CAPITAL MARKETS

Abstract

This study addresses the problem that, despite widespread investment in advanced analytics, there is limited empirical evidence on how predictive analytics actually improves financial forecasting and risk management in U.S. capital markets. The purpose is to quantify the contribution of predictive analytics capability to forecasting accuracy and risk management effectiveness at the institutional level. A quantitative cross-sectional, case-based survey design was applied to 214 professionals from 65 U.S. capital market enterprise cases using predictive analytics on cloud enabled trading and risk platforms. Key variables included predictive analytics capability, perceived forecasting accuracy, risk management effectiveness, data quality, analytical capability, governance strength, and risk management culture, each measured with reliable Likert scales. The analysis plan combined descriptive statistics, Pearson correlations, multiple regression, and moderation tests. Results show that predictive analytics capability is strongly associated with forecasting accuracy (r = 0.62, β = 0.51, p < 0.001, R² = 0.49) and risk management effectiveness (r = 0.58, β = 0.47, p < 0.001, R² = 0.45). Overall, 68.2 percent of respondents agreed that predictive analytics improved forecasts and 64.0 percent agreed that it enhanced risk management. Data quality and analytical capability significantly strengthen forecasting gains, while governance strength and risk culture significantly reinforce risk management benefits. These findings imply that U.S. capital market institutions should treat predictive analytics as an integrated strategic capability and prioritize investments in data governance, skilled analytics teams, and robust risk governance to convert model outputs into consistently better forecasts and more resilient risk control.