Paper: arXiv 2409.00416

Abstract

Frazzini and Pedersen (2014) Betting Against Beta (BAB) factor is based on the idea that high beta assets trade at a premium and low beta assets trade at a discount due to investor funding constraints. However, as argued by Campbell and Vuolteenaho (2004), beta comes in “good” and “bad” varieties. While gaining exposure to low-beta, BAB factors fail to recognize that such a portfolio may tilt towards bad-beta. We propose a Betting Against Bad Beta factor, built by double-sorting on beta and bad-beta and find that it improves the overall performance of BAB strategies though its success relies on proper transaction cost mitigation.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves advanced theoretical finance math (ICAPM, VAR decompositions, precise beta definitions) but is heavily grounded in empirical backtesting with CRSP data, transaction cost analysis, and replication of existing factors, showing a blend of theoretical and practical rigor.

Research Flowchart

  flowchart TD
  subgraph A["Research Goal"]
      G1["Does ignoring 'bad beta'<br>limit Betting Against Beta<br>performance?"]
  end

  subgraph B["Key Methodology"]
      M1["Database: CRSP & Compustat<br>Data: US Stocks, 1963-2013"]
      M2["Double Sort Strategy<br>1. Sort by Market Beta<br>2. Sort by Bad Beta (Covariance with<br>bad macro states)"]
  end

  subgraph C["Computational Process"]
      C1["Reconstruct Standard BAB<br>(Frazzini & Pedersen 2014)"]
      C2["Construct BABB: Betting Against<br>Bad Beta Factor<br>(Long Low Bad-Beta / Short High Bad-Beta)"]
  end

  subgraph D["Key Findings & Outcomes"]
      F1["Performance:<br>BABB generates higher risk-adjusted<br>returns than standard BAB"]
      F2["Limitation:<br>Success depends on strict<br>transaction cost mitigation"]
      F3["Conclusion:<br>Controlling for Bad Beta improves<br>BAB strategies"]
  end

  A --> B
  B --> C
  C --> D

Cited References (36)

  1. Structural VARs and noninvertible macroeconomic models (2019) — link
  2. Where do Betas Come From? Asset Price Dynamics and the Sources of Systematic Risk (1993) — link
  3. An Intertemporal Capital Asset Pricing Model (1973)
  4. Does Information Help Recovering Structural Shocks from Past Observations? (2006)
  5. Growth or glamour? Fundamentals and systematic risk in stock returns (2010) — link
  6. What Drives Booms and Busts in Value? (2023)
  7. Return decomposition (2009) — link
  8. Sufficient information in structural VARs (2014) — link
and 28 more
  1. Common shocks in stocks and bonds (2021) — link
  2. Hard times (2013) — link
  3. An intertemporal CAPM with stochastic volatility (2018) — link
  4. A Variance Decomposition for Stock Returns (1991) — link
  5. Stock Prices, Earnings, and Expected Dividends (1988)
  6. The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors (1988) — link
  7. Return decomposition (2009) — link
  8. Sufficient information in structural VARs (2014) — link
  9. Bad Beta, Good Beta (2004) — link
  10. Large Bayesian vector auto regressions (2010) — link
  11. A Stock Return Decomposition Using Observables (2022) — link
  12. Structural Vector Autoregressive Analysis (2017)
  13. FRED-MD: A Monthly Database for Macroeconomic Research (2016)
  14. Betting against beta (2014) — link
  15. Betting against betting against beta (2022) — link
  16. Zeroing In on the Expected Returns of Anomalies (2023)
  17. Assaying Anomalies (2024)
  18. A five-factor asset pricing model (2015) — link
  19. Absolving beta of volatility’s effects (2018) — link
  20. Simpler Better Market Betas (2019) — link
  21. Risk measurement when shares are subject to infrequent trading (1979) — link
  22. A NOTE ON USING CROSS-SECTIONAL INFORMATION IN BAYESIAN ESTIMATION OF SECURITY BETAS (1973) — link
  23. Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data (2009) — link
  24. A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices (2012) — link
  25. Market Microstructure Invariance: Empirical Hypotheses (2016) — link
  26. A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices (2017) — link
  27. A Taxonomy of Anomalies and Their Trading Costs (2015) — link
  28. A Transaction-Cost Perspective on the Multitude of Firm Characteristics (2020) — link

0 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.