The traditional approach to asset allocation using mean-variance optimization (MVO) has been a major breakthrough but it is not without criticism, particularly for private market investments. Recognizing the need for innovative solutions, StepStone seeks to explore alternatives, including Michaud's resampling method. The aim is to develop a more efficient and robust strategy, specific to the unique characteristics and demands of private market portfolios, prividing the basis for a comprehensive comparative analysis.

The work included developing a resampling-based asset allocation analysis tool using Python, tailored for private market investments. A comprehensive literature review guided the comparison between traditional mean-variance optimization (MVO) and Michaud's resampling method. Simulations were conducted to estimate efficiency frontiers, and the results were thoroughly analyzed. Limitations in the study design and complexity were recognized, setting the stage for further research.

The research successfully developed a resampling-based asset allocation analysis tool targeted to private market investments. Comparative analysis between the traditional mean-variance optimization (MVO) method and Michaud's resampling method was conducted, highlighting the complexity of both approaches. While the tool's efficiency and adaptability were acknowledged, the study did not observe a clear outperformance of one method over the other. Minor differences in returns and risks were observed, but statistical significance remained elusive. The findings also underscored the tool's potential sensitivity to input factors and emphasized the need for a nuanced approach in portfolio analysis. Future research directions were identified to deepen understanding and application in the investment strategy landscape.