mutual-fund-analysis

What problem does it solve?

It helps you stop relying on “past-best performance” by systematically evaluating mutual funds using performance, risk-adjusted metrics, style-box regression, and style-drift detection, then guiding FOF (fund-of-funds) construction and ETF selection.

Core Features & Use Cases

• Multi-metric fund screening: Compare annualized returns, Alpha vs benchmark, information ratio, Sharpe/Sortino/Treynor, drawdown, volatility, and Calmar to rank funds against clear quality thresholds.
• Sharpe style-box regression & validation: Determine whether a fund’s realized exposure matches its declared style using a nine-grid value/balance/growth and size mapping, including R²-based clarity.
• Style drift detection: Use rolling-window regression to flag significant style shifts (|Δβ| thresholds, R² trends) so you can detect closet-to-index behavior or manager-driven style changes.
• FOF portfolio construction: Build diversified allocations across asset classes and select complementary style exposures, then apply a rebalancing rule based on deviation thresholds.
• ETF selection framework: Evaluate ETFs via tracking error, fee structure, liquidity, and size to choose efficient and tradable passive/strategy vehicles.

Quick Start

Use the mutual-fund-analysis skill to generate a fund screening report that includes performance ranking, style-box regression results with R², drift assessment over rolling windows, and a recommended FOF allocation plan.