An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction.

Area of Science:

• Quantitative Finance
• Machine Learning
• Computational Finance

Background:

• Equity market modeling is crucial for informed investment decisions and risk reduction.
• Traditional stock market analysis faces challenges due to high stock price correlation and batch processing limitations.
• Machine learning advances offer opportunities to integrate forecasting theory with risk optimization.

Purpose of the Study:

• To develop a novel two-stage framework for enhanced equity market modeling and stock price prediction.
• To minimize investment risk through optimal portfolio construction.
• To accurately forecast future stock price movements using advanced machine learning techniques.

Main Methods:

• A two-stage framework integrating mean-variance portfolio construction and online machine learning.
• Utilizing the mean-variance approach for risk-minimizing portfolio selection.
• Employing a combination of perceptron and passive-aggressive algorithms for stock price movement prediction.

Main Results:

• The proposed framework was evaluated on 20 health sector indices across four geographical regions.
• Performance metrics included classification reports, AUC score, accuracy, and Hamming loss.
• Numerical comparisons demonstrated the effectiveness of the learning-based ensemble strategies with portfolio selection.

Conclusions:

• The developed two-stage framework shows significant effectiveness in equity market analysis.
• Ensemble machine learning strategies combined with portfolio selection offer a robust approach to investment modeling.
• The findings suggest a promising direction for improving stock trading strategies and risk management.