Advanced Portfolio Optimization Techniques
Please join an AI-generated conversation about advanced portfolio optimization techniques. We conducted a literature review and a subsequent LLM discussion on portfolio optimization techniques to generate the audio with Google’s NotebookLM.
The review synthesizes findings from numerous academic papers, focusing on Hierarchical Risk Parity (HRP) and its variations as alternatives to traditional Modern Portfolio Theory (MPT). The discussion explores various clustering algorithms used in HRP, including k-means and hierarchical clustering, and examines different methods for evaluating portfolio performance, emphasizing Sharpe ratios and risk measures. Key limitations of HRP and MPT are also analyzed, along with the role of covariance matrix estimation and the use of machine learning techniques in portfolio construction. The overall goal is to compare and contrast different portfolio strategies and to identify robust and efficient methods for portfolio optimization.
Join Bill & Mark to discuss Finance
Finance: the discipline concerned with the management, creation, and study of money, investments, and related resources.
Given, finance focuses on optimizing the use of financial resources to ensure stability, growth, and value creation, we share the tools and processes to achieve such optimization.
In support, we leverage both fundamental and quantitative disciplines to drive Top-down Macro and bottoms up processes to identify trends and specific opportunities.