Academic Alpha

Betting Against Beta: How Leverage Constraints Flatten the Risk-Return Curve

This paper shows that investors with limited ability to borrow tend to chase high-risk assets. As a result, low-risk (low-beta) assets offer better risk-adjusted returns. A strategy that goes long low-beta assets and shorts high-beta ones—called “Betting Against Beta”—consistently beats the market.

Key Performance Metrics

📊 How Well Does This Strategy/Model Perform?

  • US Stocks Sharpe Ratio (BAB Factor): 0.78
  • Treasury BAB Sharpe Ratio: 0.81
  • Corporate Credit BAB Sharpe Ratio: 0.82
  • Alpha: Significant in 18 of 19 developed equity markets

💡 Takeaway:
Betting against beta delivers strong, positive, and consistent returns across time, countries, and asset classes—outperforming traditional equity factors like value and momentum.

Key Idea: What Is This Paper About?

The paper explains why low-beta assets outperform on a risk-adjusted basis. Investors who can’t use leverage are forced to chase high-beta assets for higher expected returns, pushing their prices up and expected returns down. In contrast, low-beta assets are underpriced. A market-neutral portfolio that goes long low-beta assets (leveraged) and shorts high-beta assets earns high returns.

Economic Rationale: Why Should This Work?

📌 Relevant Economic Theories and Justifications:

  • Leverage Constraints: Investors unable to borrow overweight risky (high-beta) assets instead of leveraging safer ones.
  • Flattened Security Market Line: When many agents are constrained, the CAPM becomes too flat—high-beta assets have lower alpha.
  • Funding Liquidity Risk: When liquidity tightens, the BAB strategy suffers short-term losses but earns higher returns afterward.
  • Market Clearing Mechanism: Unconstrained investors arbitrage by leveraging low-beta assets and shorting overpriced high-beta ones.

📌 Why It Matters:
It challenges the classic CAPM and redefines the role of leverage and risk-taking in asset pricing. The results reshape portfolio construction and performance attribution.

How to Do It: Data, Model, and Strategy Implementation

Data Used

  • Asset Classes: US & international equities, Treasuries, corporate bonds, credit indices, FX, commodities
  • Period: US equities from 1926, global from 1980s, others vary (1950s–2012)
  • Sources: CRSP, MSCI, Xpressfeed, Barclays Bond Hub, AQR internal data

Model / Methodology

  • Key Model Feature: Multiple investor types with different leverage constraints and risk aversion
  • BAB Construction:
    1. Sort assets by beta
    2. Go long low-beta assets (leveraged to beta=1)
    3. Short high-beta assets (de-leveraged to beta=1)
    4. Result: Market-neutral portfolio capturing the beta anomaly

Trading Strategy (BAB Factor)

  • Signal Generation: Use historical rolling beta to sort assets
  • Portfolio Construction:
    • Long side: leverage low-beta assets
    • Short side: de-leverage high-beta assets
  • Rebalancing: Monthly
  • Risk Targeting: Equal beta exposure long and short, volatility-scaled

Key Table or Figure from the Paper

📌 Explanation:

  • Plots annualized Sharpe ratios of BAB strategies across stocks, bonds, credit, FX, and commodities
  • Almost all asset classes show positive Sharpe ratios, with US stocks and Treasuries exceeding 0.80
  • Highlights the consistency and robustness of the BAB effect across global markets and asset types

Final Thought

💡 You don’t need to take more risk to earn more—you just need to bet against beta. 🚀

Paper Details (For Further Reading)

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