Finance Forecasting Using Financial Indicators with Advanced Machine Learning on End-User Mobile Devices

Financial forecasting plays a pivotal role in supporting data-driven decision-making for investors, traders, and financial institutions. This research proposes an advanced machine learning framework for accurate and efficient financial forecasting, optimized for deployment on mobile devices. The model leverages the power of Extreme Gradient Boosting (XGBoost) combined with comprehensive feature engineering techniques, incorporating both technical indicators (e.g., Moving Averages, Relative Strength Index) and fundamental economic variables (e.g., interest rates, GDP growth). Extensive experiments conducted on a proprietary dataset comprising historical stock prices, foreign exchange rates, and cryptocurrency data demonstrate the model’s superior predictive performance compared to traditional statistical models (ARIMA, GARCH) and other machine learning approaches (SVR, Random Forest). The model achieves high accuracy, as evidenced by metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and $R^{2}$ scores. Furthermore, the study introduces model optimization techniques, including quantization and pruning, to enable real-time inference with minimal computational overhead on mobile platforms. The mobile deployment results confirm the feasibility of real-time financial forecasting with low latency, efficient memory usage, and reduced battery consumption. This research bridges the gap between advanced machine learning and mobile technology, offering practical solutions for real-time financial analytics and intelligent decision-making in dynamic market environments.