Review of Quantitative Portfolio Optimization Research for Emerging Market Asset Management Strategies

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@article{1714305,
      author = {Elikem Kwasi Agbosu, Lovelyn Ekpedo},
      title = {Review of Quantitative Portfolio Optimization Research for Emerging Market Asset Management Strategies},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {2},
      number = {6},
      pages = {219-233},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714305.pdf},
      abstract = {The growing integration of emerging markets into global financial systems has intensified interest in quantitative portfolio optimization techniques tailored to their unique risk–return characteristics. Emerging market assets are often characterized by higher volatility, liquidity constraints, structural breaks, regulatory frictions, and pronounced exposure to macroeconomic and geopolitical shocks, posing significant challenges to traditional portfolio optimization frameworks developed for mature markets. This study provides a comprehensive review of quantitative portfolio optimization research with a specific focus on emerging market asset management strategies. Drawing on a broad body of empirical and methodological literature, the review examines the evolution of classical mean–variance optimization and its extensions, including downside risk measures, robust optimization, Bayesian approaches, and multi-period allocation models. Particular attention is given to how these techniques address estimation risk, non-normal return distributions, and unstable correlation structures prevalent in emerging markets. The review further synthesizes evidence on the application of advanced methods such as regime-switching models, shrinkage estimators, and machine learning–based optimization frameworks, highlighting their comparative performance under conditions of market stress and data limitations. Empirical findings suggest that constrained, robust, and adaptive optimization approaches generally outperform unconstrained mean–variance portfolios in emerging market contexts, especially during periods of heightened volatility. However, the review also identifies persistent gaps in the literature, including limited out-of-sample validation, underrepresentation of frontier markets, and insufficient consideration of transaction costs, currency risk, and regulatory constraints. By consolidating existing knowledge and identifying methodological limitations, this review contributes to a clearer understanding of how quantitative portfolio optimization can be effectively adapted for emerging market asset management. The study concludes by outlining future research directions aimed at developing context-sensitive optimization frameworks that enhance risk-adjusted performance, resilience, and practical implement ability for institutional and professional investors operating in emerging economies.},
      keywords = {Quantitative Portfolio Optimization; Emerging Markets; Asset Management; Risk-Adjusted Returns; Robust Optimization; Investment Strategy},
      month = {December},
  }