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|Mean-variance portfolio optimization
|Solving portfolio optimization problems using Markowitz's mean-variance model, identifying optimal asset allocations to achieve desired risk and return characteristics.
|Extensions of mean-variance optimization
|Exploring advanced techniques such as Black-Litterman model, incorporating investor views, Bayesian inference, and other extensions to enhance the mean-variance framework.
|Analyzing and constructing the efficient frontier, which represents the set of optimal portfolios with the highest expected return for a given level of risk.
|Incorporating various constraints such as sector weight limits, maximum turnover, exposure constraints, and other customized restrictions to tailor the portfolio optimization process according to specific requirements.
|Conducting historical performance analysis and evaluating the effectiveness of portfolio optimization strategies by testing them on past data to assess their performance and risk metrics.
|Implementing portfolio optimization algorithms and methodologies using programming languages such as MATLAB, Python, or R to create efficient and robust solutions.
|Developing strategies and algorithms for periodically adjusting portfolio holdings to maintain desired asset allocations, minimize tracking error, and manage risk effectively.