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Frazzini & Pedersen (2014) document the low-beta anomaly and the Betting-Against-Beta (BAB) factor. Plain-language intro. Not investment advice.
Takeaway. High-beta names often deliver disappointing risk-adjusted returns; Betting Against Beta (BAB) longs low-beta and shorts high-beta with leverage/scaling so the portfolio is roughly market-neutral in beta.
Betting Against Beta (Andrea Frazzini, Lasse Heje Pedersen, 2014) is one of the most-cited “defensive factor” papers after-work learners meet.
Fit for learners: Low-beta anomaly / BAB factor—core defensive factor to code and stress-test.
This Hub note is a plain-language intro. Not investment advice. Read beside Carry and factor zoo cautions.
CAPM says higher beta should mean higher expected return. Empirically the security market line looks too flat: low-beta assets look “too good” on a Sharpe-like basis, high-beta too poor—once you account for leverage constraints and how people chase lottery-like names.
Again: research pattern, not a buy button.
BAB is a clean pipeline practice: estimate betas → rank → build long/short → rescale → plot cumulative toy PnL on public returns. You learn factor hygiene before fancy ML.
Tiny evening exercise: on a small panel of daily returns, estimate trailing betas vs a market index; split lowest/highest terciles; print average beta of each sleeve—stop before claiming live edge.
Landing / DOI: ScienceDirect / JFE
Not investment advice. This Hub note is educational only. BAB is an academic factor study—not a recommendation to lever long/short portfolios.
Single course CTA: SoloQuant Academy’s Introduction to Python Programming—functions and DataFrames first; then beta-sort notebooks become weekend projects, not nightmares.