performance-attribution

Portfolio return decomposition breaks down performance into allocation, selection, and timing effects. This analysis isolates the specific drivers of returns, helping identify which investment decisions contributed most to overall strategy results.

Performance attribution evaluates investment strategies by decomposing returns into market exposure, factor alpha, and beta. Factor analysis quantifies how specific risk factors drive portfolio performance relative to benchmarks, isolating true manager skill.

Yes, market-timing evaluation relies on numpy, pandas, and scipy to process financial data. These dependencies support the statistical analysis required to assess timing abilities and run benchmark comparisons effectively.

Benchmark comparison evaluates strategy performance against standard indices by decomposing returns into allocation and timing effects. Running the analyze_portfolio script provides a structured framework to measure relative performance and identify deviation sources.

Yes, performance attribution requires historical financial data to execute statistical analysis. The framework needs portfolio returns and benchmark data to calculate factor exposure, market-timing models, and performance decomposition accurately.