factor-investing
Factor Investing teaches you to apply multifactor models—from CAPM through Fama-French 3- and 5-factor frameworks plus momentum—to portfolio construction and fund evaluation. Regress fund returns against factors to separate true alpha from passive exposure, detect closet indexing, and assess smart-beta product purity and implementation quality.
Factor Investing decomposes fund returns into factor exposures to isolate manager skill from passive style drift.
AI-generated summary based on this skill's SKILL.md
Install
JoelLewis/finance_skills/factor-investing · repository language: Python
git clone https://github.com/JoelLewis/finance_skills
cp -r finance_skills/plugins/wealth-management/skills/factor-investing ~/.claude/skills/factor-investingFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I run a Fama-French factor model regression?
Factor Investing teaches you to regress fund returns against the Fama-French factors—market (Rm-Rf), size (SMB), value (HML), profitability (RMW), and investment (CMA)—to isolate alpha and factor loadings. The regression output shows which factors drive performance, their statistical significance via t-statistics, and the unexplained alpha. This decomposition separates manager skill from passive style exposure.
What does Factor Investing reveal about closet indexing?
Factor Investing helps you detect closet indexing by analyzing factor loadings, R-squared values, and fee-to-tracking-error ratios. A high R-squared with factor exposures matching the benchmark, combined with high fees relative to tracking error, signals that a fund is behaving like an index despite active management claims. Factor loadings expose hidden style drift and passive replication.
How can I evaluate smart-beta and factor-tilted products?
Factor Investing guides evaluation of smart-beta ETFs and factor-tilted funds by assessing purity (how cleanly the product isolates its target factor), costs (expense ratios and implementation drag), and implementability (turnover, liquidity, and rebalancing frequency). Multifactor regression reveals whether the product delivers its promised factor exposure or drifts into unintended exposures.
Is my fund manager adding skill or just factor exposure?
Factor Investing separates true alpha from factor returns through multifactor regression. If a fund's returns are fully explained by its factor loadings (high R-squared, insignificant alpha), the manager is delivering style exposure, not skill. Positive, statistically significant alpha after controlling for factors indicates genuine outperformance beyond passive factor tilts.
What should I know about factor premiums and their durability?
Factor Investing teaches that factor premiums—value, momentum, profitability, and investment—vary in magnitude and persistence across time and markets. Understanding factor cyclicality, crowding risk, and post-publication decay helps you time factor tilts and avoid overcrowded strategies. Expected returns from factor loadings depend on premium durability and market regime.
How do factor loadings and alpha inform portfolio construction?
Factor Investing uses multifactor regression output—loadings, alpha, and significance—to build portfolios with intentional factor tilts and controlled risk. By understanding your current factor exposures and their expected premiums, you can rebalance toward undervalued factors, hedge undesired exposures, and construct portfolios aligned with your return objectives and risk tolerance.
SKILL.md
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Factor Investing
Core Concepts
From CAPM to Multifactor Models
CAPM prices a single source of risk: E(R_i) - R_f = beta * (E(R_m) - R_f). Persistent anomalies — small caps, cheap (high book-to-market) stocks, and recent winners earning more than beta predicts — motivated adding factors. Fama-French
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plugins/wealth-management/skills/factor-investing/SKILL.md
plugins/wealth-management/skills/factor-investing/scripts/factor_investing.py