Leverage constraints or preference for lottery: What explains the low-risk effect in India?

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The study empirically investigates two theories that claim to explain the low-risk effect in Indian equity markets using a universe of stocks listed on the National Stock Exchange of India (NSE) from January 2000 to September 2018. Leverage constraints and preference for lottery are two major competing theories that explain the presence and persistence of the low-risk effect. While the leverage constraints theory argues that systematic risk drives low-risk anomaly and therefore risk should be measured using beta, lottery demand theory claims that irrational investor’s preference towards stocks with lottery-like payoffs is responsible for the persistence of the low-risk effect, and risk should be measured by idiosyncratic volatility. However, given that most of the risk measures are highly correlated, it is not easy to precisely measure a specific theory’s contribution to explaining the low-risk effect. The study constructs the Betting against correlation (BAC) factor to measure the contribution of leverage constraints to the low-risk effect. It further constructs the SMAX factor to untangle the contribution of lottery preference theory. The results show that leverage constraints theory predominantly explains the low-risk effect in Indian markets. This study contributes significantly to the body of literature, as this is the first such study on the Indian market, one of the major emerging markets, especially when the debate on theories explaining the low-risk effect is yet to settle.

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    • Figure 1. Descriptive statistics
    • Table 1. Performance of beta-sorted portfolios and BAB
    • Table 2. Correlation vs volatility: Risk-adjusted returns and beta
    • Table 3. Disintegrating BAB into its components: The BAC and BAV factors
    • Table 4. Betting against correlation (BAC)
    • Table 5. Performance of portfolios sorted by MAX
    • Table 6. Performance of portfolios sorted by SMax and Volatility
    • Table 7. LMAX as SMAX and TVOL
    • Table 8. The IDVOL risk factors
    • Table 9. Performance marathon among published factors
    • Table 10. Performance marathon among factors constructed using the Fama-French method
    • Table A1. Betting against volatility (BAV)
    • Table B1. LMAX and SMAX based on yearly look-back periods, namely LMAX1Y and SMAX1Y
    • Table C1. Performance marathon among factors constructed using the rank-weighted method
    • Data curation
      Shilpa Peswani, Mayank Joshipura
    • Formal Analysis
      Shilpa Peswani, Mayank Joshipura
    • Investigation
      Shilpa Peswani, Mayank Joshipura
    • Project administration
      Shilpa Peswani, Mayank Joshipura
    • Resources
      Shilpa Peswani, Mayank Joshipura
    • Software
      Shilpa Peswani, Mayank Joshipura
    • Supervision
      Shilpa Peswani, Mayank Joshipura
    • Validation
      Shilpa Peswani, Mayank Joshipura
    • Writing – original draft
      Shilpa Peswani, Mayank Joshipura
    • Writing – review & editing
      Shilpa Peswani, Mayank Joshipura