Using Machine Learning and Quantitative techniques to analyze the Impact of Macroeconomic Indicators on Equity Market Dynamics and Financial Security prices.

This study investigates the influence of macroeconomic factors and historical stock metrics on predicting future financial equity prices using machine learning techniques. We developed predictive models incorporating stock metrics (e.g., Ticker, Adjusted Close, Trading Volume) and macroeconomic variables (e.g., GDP Growth, Inflation). Techniques employed include LSTM, Neural Networks, SVR, Decision Trees, and Random Forest models, with data sourced from Yahoo Finance and the World Bank Economic datasets. We collected data spanning two decades on the S&P 500, IXIC, DJI, FTSE, and NYA datasets. Model accuracy was assessed through out-of-sample testing. Our study provides insights into how economic indicators affect stock market behavior, with applications in financial and socioeconomic strategy. Results reveal that the Random Forest and LSTM models achieved superior predictive accuracy across multiple indices. Macroeconomic factors like GDP Growth and Inflation were significant predictors of stock movements, highlighting their importance in enhancing prediction models and benefiting investment strategies. Our findings hold potential applications for socioeconomic strategy and investment optimization.