fund-analysis

Risk-adjusted return metrics evaluate fund performance by quantifying returns relative to volatility. This skill calculates Sharpe, Sortino, and Information ratios using pandas and numpy to assess mutual fund and ETF performance systematically.

Style regression analysis detects style drift by comparing a fund's actual return patterns against its declared strategy benchmarks. This skill uses Sharpe style box regression to verify if a fund's holdings match its stated investment style over time.

Evaluating fund manager skill requires moving beyond simple past performance to identify sustainable alpha. This skill systematically assesses manager skill by isolating true alpha from style factors using statistical modeling with scipy.

Yes, you can build and rebalance multi-asset FOF portfolios using top-tier fund selection criteria. This skill provides a framework for constructing diversified portfolios based on risk-adjusted metrics and style consistency.

Yes, pandas, numpy, and scipy are required dependencies for quantitative fund analysis. These libraries handle data processing and statistical modeling for calculating performance metrics and running style regression analysis.

You should not rely solely on past performance when past returns do not indicate sustainable alpha sources. This skill addresses that limitation by detecting style drift and evaluating risk-adjusted metrics to verify strategy consistency.