A Risk-Analysis Approach to Optimize Investment Portfolios

Abstract

This research explores a comprehensive approach to portfolio construction and optimization by integrating Multi-Criteria Decision-Making (MCDM) techniques with advanced risk management strategies. The study begins with extensive data collection from reputable financial sources and applies MCDM methods such as TOPSIS, VIKOR, ARAS, and COPRAS to assess key financial indicators of companies listed on the NYSE. The results highlight the importance of diversification and strategic asset allocation in achieving an optimal balance between risk and return. The portfolio selection process is guided by performance rankings derived from MCDM analysis, covering various sectors, including technology, pharmaceuticals, and aerospace. To ensure adaptability to market changes, continuous monitoring and periodic rebalancing are implemented. The study also addresses challenges related to data accuracy and decision-making complexity by suggesting solutions such as advanced data analytics, machine learning integration, and strategic collaborations to enhance portfolio management. Ultimately, this research underscores the effectiveness of MCDM techniques in portfolio optimization by incorporating multiple criteria and stakeholder perspectives. Sensitivity analysis played a crucial role in designing a well-diversified portfolio that manages risk while optimizing returns. Additionally, risk management measures such as hedging and periodic adjustments helped strengthen the portfolio's resilience to market volatility.