From Data to Decisions: Portfolio Topology Optimization Framework Case study: Automotive Industry

Abstract Portfolio management in the automotive industry plays a crucial role in optimizing resource allocation, risk management, and financial performance. This article presents an integrated optimization framework that combines the Markowitz model, multi-discipline optimization, and deep learning techniques to enhance portfolio decision-making in the automotive sector. By leveraging advanced analytics and artificial intelligence, companies can develop data-driven strategies to maximize returns, minimize risks, and achieve competitive advantage. A comprehensive methodology for implementing the integrated optimization framework in Python is provided, including data preprocessing, model development, performance analysis, and practical implementation. Empirical results using a real-world dataset demonstrate the effectiveness of the proposed approach in improving portfolio management practices in the automotive industry.