Leverage constraints or preference for lottery: What explains the low-risk effect in India?
-
DOIhttp://dx.doi.org/10.21511/imfi.18(2).2021.05
-
Article InfoVolume 18 2021, Issue #2, pp. 48-63
- Cited by
- 802 Views
-
376 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)G11, G12, G14
-
References24
-
Tables13
-
Figures1
-
- 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
-
- Agarwalla, S., Jacob, J., & Varma, J. (2013). Four factor model in Indian equities market (Working Paper W.P. No. 2013-09-05). Indian Institute of Management, Ahmedabad.
- Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2006). The Cross Section of Volatility and Expected Return. Journal of Finance, 61(1), 259-299.
- Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2009). High Idiosyncratic Volatility and Low Returns: International and Further US Evidence. Journal of Financial Economics, 91(1), 1-23.
- Asness, C., Frazzini, A., Gormsen, N. J., & Pedersen, L. (2018). Betting Against Correlation: Testing Theories of the Low-Risk Effect (CEPR Discussion Paper No. DP12686).
- Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. Financial Analysts Journal, 67(1), 40-54.
- Bali, T., Brown, S., Murray, S., & Tang, Y. (2017). A Lottery Demand-Based Explanation of the Beta Anomaly. Journal of Financial and Quantitative Analysis, 52(6), 2369-2397.
- Bali, T., Cakici, N., & Whitelaw, R. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446.
- Barberis, N., & Huang, M. (2008). Stocks as lotteries: the implications of probability weighting for security prices. American Economic Review, 98(5), 2066-2100.
- Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. Journal of Business, 45(3), 444-455.
- Black, F. (1993). Beta and Return. The Journal of Portfolio Management, 20(1), 74-77.
- Black, F., Jensen, M., & Scholes, M. (1972). The Capital Asset Pricing Model: Some Empirical Tests. In M. C. Jensen (Ed.), Studies in the Theory of Capital Markets. New York: Praeger
- Brunnermeier, M. K., Gollier, C., & Parker, J. (2007). Optimal Beliefs, Asset Prices, and the Preference for Skewed Returns. American Economic Review, 97(2), 156-165.
- Centre for Monitoring Indian Economy (CMIE). Prowess IQ.
- Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
- Frazzini, A., & Pedersen, L. (2014). Betting against Beta. Journal of Financial Economics, 111(1), 1-25.
- Haugen, R., & Heins, J. (1975). Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles. The Journal of Financial and Quantitative Analysis, 10(5), 775-784.
- Liu, J., Stambaugh, R., & Yuan, Y. (2018). Absolving Beta of Volatility’s Effects. Journal of Financial Economics, 128(1), 1-15.
- Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
- Novy-Marx, R., & Velikov, M. (2018). Betting Against Betting Against Beta (SSRN Working Paper No. 3300965).
- Peswani, S. G. (2017). Returns to Low-Risk Investment Strategy. Applied Finance Letters, 6(1), 2-15.
- Reserve Bank of India. (n.d.). Official site.
- Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3), 425-442.
- Stambaugh, R., Jianfeng, Y., & Yu Yuan. (2015). Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle. Journal of Finance, 70(5), 1903-1948.
- Vasicek, O. (1973). A Note on Using Cross-Sectional Information in Bayesian Estimation of Security Betas. Journal of Finance, 28(5), 1233-1239.