Published Paper Details:

The field of portfolio construction has evolved significantly, driven by the increasing complexity of financial markets, investor behavior, and emerging risks. Traditional models such as the Markowitz Mean-Variance framework and the Sharpe Single Index Model, while foundational, often fall short in volatile and illiquid environments, particularly in emerging markets. This study explores optimum portfolio construction by integrating both classical and modern approaches across multiple industries and market contexts. Using Sharpe ratio optimization, cut-off rate methods, and statistical tools such as t-tests, correlation matrices, and risk-return analysis, the research identifies the most efficient portfolio combinations from 20 companies spanning four sectors. The study is supported by extensive literature, ranging from early beta-based models to modern advancements incorporating machine learning, Bayesian inference, ESG filters, and risk-aware rebalancing strategies. Empirical findings confirm that diversified, data-driven portfolios significantly outperform traditional allocation methods in terms of Sharpe ratio, total risk reduction, and sectoral balance. The research contributes to a deeper understanding of how modern tools can adapt classical theories for more robust and resilient portfolio performance. It provides a strategic roadmap for investors navigating uncertain, high-risk markets with the goal of achieving superior risk-adjusted returns through scientifically grounded portfolio construction.