Machine Learning in investment strategies. Stock Price prediction through Support Vector Machines

Stock price prediction is a challenging yet crucial aspect of finance, facilitating informed investment decisions amidst market uncertainties. This study focuses on developing a robust investment strategy utilizing Machine Learning (ML) techniques, specifically Support Vector Machines (SVM). The objective is to create a scalable and adaptable trading algorithm applicable across various investment scenarios. The study adopts a systematic approach comprising optimization and testing phases. Through parameter fine-tuning and rigorous evaluation using separate datasets, the model's predictive accuracy and economic profitability are assessed via statistical metrics and real-world return measures. The study outlines a systematic process for constructing and refining investment strategies. Results demonstrate the superiority of SVM-based ML methods in outperforming market benchmarks and generating significant returns. Moreover, the study emphasizes the importance of economic performance indicators in algorithm selection and optimization. In summary, this research underscores the potential of SVM approaches in stock price prediction and investment strategy development, bridging the gap between ML techniques and practical investment practices.