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Topic Details
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.
Efficient frontier Analyzing and constructing the efficient frontier, which represents the set of optimal portfolios with the highest expected return for a given level of risk.
Portfolio constraints 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.
Backtesting 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.
Implementation Implementing portfolio optimization algorithms and methodologies using programming languages such as MATLAB, Python, or R to create efficient and robust solutions.
Portfolio rebalancing Developing strategies and algorithms for periodically adjusting portfolio holdings to maintain desired asset allocations, minimize tracking error, and manage risk effectively.

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