Predictive Modeling of U.S. Stock Market Trends Using Hybrid Deep Learning and Economic Indicators to Strengthen National Financial Resilience

Abstract

The stock markets are too complex to predict, despite their non-linearity, volatility, and multifactorial nature. Current econometric and deep learning models often do not take into account the macroeconomic context, which restricts predictive accuracy at times of financial volatility. This paper attempts to fill this gap by proposing a Hybrid Deep Learning Model (HDLM), a machine that combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and an attention mechanism that can jointly include both the dynamic behavior of the market and the economic links in the trends of the U.S. market.  The analysis is based on a strictly tested data set of 20102023, which was retrieved from the Federal Reserve Economic Data (FRED), Yahoo Finance, and the Bureau of Economic Analysis. These include all significant equity indexes such as the S&P 500, NASDAQ Composite, and Dow Jones Industrial Average, prime macroeconomic indicators such as the GDP growth, inflation rates, interest rates, and unemployment levels.  Correlation analysis, multiple regression, Granger causality tests, Johansen cointegration procedures, and volatility modeling using ARCH GARCH models are the statistical methods used. The findings show that GDP growth has a statistically significant positive impact on market returns ( = 0.58, p = 0.001), though inflation ( = -0.21, p = 0.021), interest rates ( = -0.34, p = 0.002), and unemployment ( = -0.42, p = 0.001) have significant negative predictive power (R 2 = 0.68). The HDLM achieves a better predictive performance with RMSE = 1.89, MAPE = 4.9% and DA = 83.6% > 23% better than the baseline LSTM and CNN- LSTM settings. The model minimizes prediction error by 36.1% in cases of strong market shocks, which occur under simulated stress conditions, confirming an increased financial strength. Taken together, the results of the studies support the idea that the incorporation of macroeconomic intelligence into hybrid neural systems significantly enhances forecast reliability and stability of the system. The current research, therefore, adds a strong, interpretive, and policy-related framework to the research fields of predictive finance and resilience engineering systems to predict market upheavals.