Low-risk effect: evidence, explanations and approaches to enhancing the performance of low-risk investment strategies

  • Received February 8, 2020;
    Accepted May 19, 2020;
    Published May 29, 2020
  • Author(s)
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  • Article Info
    Volume 17 2020, Issue #2, pp. 128-145
  • Cited by
    3 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The authors offer evidence for low-risk effect from the Indian stock market using the top-500 liquid stocks listed on the National Stock Exchange (NSE) of India for the period from January 2004 to December 2018. Finance theory predicts a positive risk-return relationship. However, empirical studies show that low-risk stocks outperform high-risk stocks on a risk-adjusted basis, and it is called low-risk anomaly or low-risk effect. Persistence of such an anomaly is one of the biggest mysteries in modern finance. The authors find strong evidence in favor of a low-risk effect with a flat (negative) risk-return relationship based on the simple average (compounded) returns. It is documented that low-risk effect is independent of size, value, and momentum effects, and it is robust after controlling for variables like liquidity and ticket-size of stocks. It is further documented that low-risk effect is a combination of stock and sector level effects, and it cannot be captured fully by concentrated sector exposure. By integrating the momentum effect with the low-volatility effect, the performance of a low-risk investment strategy can be improved both in absolute and risk-adjusted terms. The paper contributed to the body of knowledge by offering evidence for: a) robustness of low-risk effect for liquidity and ticket-size of stocks and sector exposure, b) how one can benefit from combining momentum and low-volatility effects to create a long-only investment strategy that offers higher risk-adjusted and absolute returns than plain vanilla, long-only, low-risk investment strategy.

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    • Figure 1. Time-varying sector exposure of long-only low-risk portfolios
    • Table 1. Main results (annualized) for quintile portfolios based on historical volatility
    • Table 2. Three-factor (Fama-French) and four-factor (Fama-French-Carhart) style regression analysis for risk quintile portfolios
    • Table 3. Double-sorted results
    • Table 4. Performance of low-risk and high-risk portfolios controlling for sector effect (macro effect)
    • Table 5. Performance statistics of momentum blended risk-quintile portfolios
    • Table 6. Sector exposure statistics and one-way turnover for low-risk investment strategies
    • Conceptualization
      Mayank Joshipura, Nehal Joshipura
    • Data curation
      Mayank Joshipura, Nehal Joshipura
    • Formal Analysis
      Mayank Joshipura, Nehal Joshipura
    • Methodology
      Mayank Joshipura, Nehal Joshipura
    • Validation
      Mayank Joshipura, Nehal Joshipura
    • Writing – original draft
      Mayank Joshipura, Nehal Joshipura
    • Writing – review & editing
      Mayank Joshipura, Nehal Joshipura
    • Supervision
      Mayank Joshipura
    • Software
      Nehal Joshipura