fund-analysis

To calculate Sharpe and Sortino ratios for fund performance evaluation, you can use this skill to compute risk-adjusted returns directly from historical fund data, requiring pandas and scipy for the underlying statistical regression analysis.

Regression-based style analysis detects fund style drift by using Sharpe style analysis to regress a fund's historical returns against asset class benchmarks, verifying if actual holdings consistently match the stated investment strategy over time.

Yes, you can build and rebalance a Fund-of-Funds portfolio by selecting complementary assets and monitoring manager consistency, leveraging numpy for statistical computation to optimize FOF portfolio allocations based on historical fund data.

Yes, you need pandas, numpy, and scipy installed, as these dependencies provide the foundational statistical computation and regression analysis required to evaluate alpha generation and manager skill from historical fund data.

The best way to assess manager skill and sustainable alpha is through quantitative multi-dimensional performance metrics, evaluating risk-adjusted returns and style attribution to detect hidden risks and verify consistency in public and private funds.

Limitations of using quantitative metrics for FOF portfolio allocations include reliance on accurate historical fund data for regression analysis, as statistical computation cannot predict sudden market shifts or completely eliminate undetected style drift.