UC-049 From Forecast to Fortune: Portfolio Optimization and Prediction

This project explores the intersection of time series forecasting and portfolio optimization to support data-driven investment strategies. Historical price data from 30 individual stocks was analyzed using two forecasting models: ARIMA and Prophet. Each model’s performance was evaluated using key accuracy metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Directional Accuracy (MDA). Results showed that ARIMA performed better on error-based metrics, while Prophet excelled at predicting directional trends. In parallel, historical return data was used to construct optimized portfolios using Modern Portfolio Theory. Two strategies were implemented: one minimizing overall volatility and another maximizing the Sharpe ratio. The optimized asset weights were translated into a simulated $10,000 portfolio, allocating shares based on recent prices. This dual analysis highlights the strengths of different forecasting approaches and demonstrates how predictive modeling can enhance real-world investment decisions.