The financial markets do not depend on a single factor but are the result of interaction of macroeconomic, investor sentiment, and efficient company-specific factors. It is also difficult to predict the stock market especially in the technology industry because they are nonlinear and highly dynamic. The past few years have seen machine learning methods become effective tools in the analysis of financial data and the identification of implicit relationships that cannot be captured by standard statistical models. This study explores whether machine learning models can be effective in daily returns of the technology industry based on macroeconomic indicators and market variables. The data used in this study is the Kaggle dataset known as “Tech Giants and Global Macroeconomic Indicators” ,which is a dataset that comprises financial and macroeconomic data between March 2015 and March 2026. The data is comprised of 2762 trading days and 44 financial indicators that are stock prices, market indices, volatility, and macroeconomic variables. Three prediction models based on machine learning were tested and compared in terms of predictability: Random Forest Regressor, XGBoost Regressor with support of a GPU, and a neural network of a Multi Layer Perceptron. To assess the models, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared score, and training time were used. The outcome of experiments suggests that the Random Forest model has the largest predictive accuracy, whereas XGBoost has a much higher training time and is still competitive. Its findings point to the promise of machine learning methods in financial predictions and how macroeconomic predictors can give valuable clues on predicting returns in the technology sector.
