Publication

Abstract : In today's volatile financial markets, with high uncertainty and economic fluctuations. Sectoral portfolio optimization poses a great challenge to the investor who seeks to optimize returns while managing risks across the diversified industries. Traditional methods, such as the Mean-Variance model, often disregard sector-specific complexities and fail to address extreme market scenarios well, leading to significant portfolio vulnerabilities. This paper presents an innovative approach based on the Mean-Value at Risk model, that merges mean-variance optimization with more advanced risk metrics including Value at Risk(VaR) and Conditional Value at Risk(CVaR). This methodology uses historical data for stock prices to derive sector-wise expected returns while making risk assessments using standard deviation and Value at Risk. Further, it provides a means of assessing tail risks by using Conditional Value at Risk(CVaR). Monte Carlo simulations(MCS) are used to explore portfolio weight combinations over a wide range of portfolios, thus giving a comprehensive risk-return analysis relevant to sector dynamics. The significant results are that sectors like Health Care offer attractive returns for moderate risk, and cyclical sectors such as Metals and Mining have higher volatility as well as risk exposure. The Mean-Value at Risk model allows the construction of diversified portfolios with balanced results, where the combined portfolio return of 14.79% which is impressive and a moderate level risk of 0.2133.