The Predictive Power of Past Left Tail Risk in the Estimation of Left Tail Risk in Future

Document Type : Original Article

Authors

1 Post-Doc., Department of Accounting, University of Isfahan, Isfahan, Iran.

2 Associate Prof., Department of Accounting, University of Isfahan, Isfahan, Iran

Abstract

The low (high) abnormal returns of stocks with a high (low) left tail risk is a financial anomaly studied in empirical capital asset pricing research. This anomaly is caused by undesirable and unexpected events that incur severe losses for investors, and this loss has the characteristic of continuity. Since the prediction of left-tail risk can help formulate an appropriate trading strategy, this study aims to predict the left-tail risk through past left tail risk information via portfolio analysis and Fama and Macbeth's (1973) regression. To this end, the data of 307 companies of Tehran Stock Exchange and Iran Fara Bourse from 2005 to 2020 were used. The results revealed the ability to predict the left tail risk by past risk information in the research sample. Further exploration by additional portfolio analysis suggested that the future left-tail risk prediction power by past information left-tail risk is greater among stocks with small size characteristics and high unsystematic volatility, but only a small portion of the market is devoted to stocks with these characteristics.

Keywords

References

  1. 1. Aalamifar, s., khani, a., & amiri, h. (2020). Developing Fama and French Multi-Factor Pricing Model Using a Fundamental Factor Based on Accounting Characteristics. Journal of Accounting Advances, 12 (2), 67-105.
    1. Aboura, S., & Arisoy, Y. E. (2019). Can Tail Risk Explain Size, Book‐To‐Market, Momentum, and Idiosyncratic Volatility Anomalies?  Journal of Business Finance & Accounting, 46(9), 1263-1298.
    2. Ang, A., & Bekaert, G. (2002). International Asset Allocation with Regime Shifts. The Review of Financial Studies, 15(4), 1137-1187.
    3. Ang, A., Chen, J., & Xing, Y. (2006). Downside Risk. The Review of Financial Studies, 19(4),1191-1239.
    4. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2009). High Idiosyncratic Volatility and Low Returns: International and Futher U.S. Evidence. Journal of Financial Economics, 91(1), 1- 23.
    5. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The Cross‐Section of Volatility and Expected Returns. The Journal of Finance, 61(1), 259-299.
    6. Asness, C., & Frazzini, A. (2013). The Devil in HML’s Details. Journal of Portfolio Management, 39 (1), 49–68.
    7. Atilgan, Y., Bali, T. G., Demirtas, K. O., & Gunaydin, A. D. (2020). Left-Tail Momentum: Underreaction to Bad News, Costly Arbitrage and Equity Returns. Journal of Financial Economics, 135(3), 725-753.
    8. Bai, H., Hou, K., Kung, H., Li, E. X., & Zhang, L. (2019). The CAPM Strikes Back? An Equilibrium Model with Disasters. Journal of Financial Economics, 131(2), 269-298.
    9. Bakshi, G., & Kapadia, N. (2003). Ddelta Hedged Gains and the Negative Market Volatility Risk Premium, Review of Financial Studies, 16 (2), 527-566.
    10. Ball, R., Gerakos, J., Linnainmaa, J., & Nikolaev, V. (2016). Accruals, Cash Flow, and Operating Profitability in the Cross Section of Stock Returns. Journal of Financial Economics, 121(1), 28-45.
    11. Bali, T., G., Bodnaruk, A., Scherbina, A., Tang, Y. (2018). Unusual News Flow and the Cross Section of Stock Returns. Management Science, 64(9), 4137-4155.
    12. Bali, T. G., Brown, S. J., Murray, S., Tang, Y. (2017). A Lottery-Demand-Based Explanation of the Beta Anomaly. Journal of Financial and Quantitative Analysis, 52(6), 2369-2397.
    13. Bali, T. G., Cakici, N. (2008). Idiosyncratic Volatility and the Cross Section of Expected Returns. Journal of Financial and Quantitative Analysis, 43 (1), 29-58.
    14. Bali, T., Cakici, N., & Whitelaw, R. F. (2014). Hybrid Tail Risk and Expected Stock Returns: When Does The Tail Wag the Dog? Review of Asset Pricing Studies, 4 (2), 206–246.
    15. Bali, T. G., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? Journal of Financial and Quantitative Analysis, 44(4), 883-909.
    16. Barberis, N., & Huang, m. (2001), Mental Accounting, Loss Aversion, and Individual Stock Returns, Journal of Finance, 56 (4), 1247-1292.
    17. Barroso, P., & Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics, 116(1), 111-120.
    18. Bawa, V. S., & Lindenberg E. B. (1977). Capital Market Equilibrium in a Mean-Lower Partial Moment Framework. Journal of Financial Economics, 5(2),189–200.
    19. Banz, R. W. (1981). The Relationship between Return and Market Value of Common Stocks. Journal of Financial Economics, 9(1), 3-18.
    20. Blair, K. S., Otero, M., Teng, C., Jacobs, M., Odenheimer, S., Pine, D. S., & Blair, R. J. R. (2013). Dissociable Roles of Ventromedial Prefrontal Cortex (Vmpfc) and Rostral Anterior Cingulate Cortex (Racc) In Value Representation and Optimistic Bias. Neuroimage, 78 (1), 103-110.
    21. Campbell, J. Y. (1996). Understanding Risk and Return. Journal of Political Economy, 104 (2), 298-345.
    22. Cao, V. N. (2015).What Explains the Value Premium? The Case of Adjustment Costs, Operating Leverage and Financial Leverage. Journal of Banking and Finance, 59 (1), 350–366.
    23. Chabi-Yo, F., Ruenzi, S., & Weigert, F. (2018). Crash Sensitivity and the Cross Section of Expected Stock Returns. Journal of Financial and Quantitative Analysis, 53(3), 1059-1100.
    24. Chabi-Yo, F., & Yang, J. (2010). Default Risk, Idiosyncratic Coskewness and Equity Returns.  Idiosyncratic Coskewness And Equity Returns (March 16, 2010). Resived from https://papers.ssrn.com/sol3/papers.cfm? abstract_id =1572661.
    25. Chan, W. S. (2003). Stock Price Reaction to News and No-News: Drift and Reversal after Headlines. Journal of Financial Economics, 70(1), 223- 260.
    26. Chen, Z., Petkova, R. (2012). Does Idiosyncratic Volatility Proxy for Risk Exposure? The Review of Financial Studies, 25(9), 2745-2787.
    27. Daniel, K., Jagannathan, R., & Kim, S. (2012). Tail Risk in Momentum Strategy Returns(No.W18169). National Bureau of Economic Research.
    28. Daniel, K., & Moskowitz, T. J. (2016). Momentum Crashes. Journal of Financial Economics, 122(2), 221-247.
    29. Dittmar, R. F. (2002). Nonlinear Pricing Kernels, Kurtosis Preference, and Evidence from the Cross Section of Equity Returns. The Journal of Finance, 57(1), 369-403.
    30. Fama, E. F., French, K. R. (1992). The Cross‐Section of Expected Stock Returns. The Journal of Finance, 47 (2), 427-465.
    31. Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.
    32. Fama, E.F., & French, K.R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116, 1–22.
    33. Fama, E. F., & Macbeth, J. D. (1973). Risk, Return and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.
    34. French, k. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility, Journal of Financial Economics 19 (1), 3-29.
    35. Han, Y., & Lesmond, D. (2011). Liquidity Biases and the Pricing of Cross-Sectional Idiosyncratic Volatility. The Review of Financial Studies, 24(5), 1590-1629.
    36. Harvey, C. R., Siddique, A. (2000). Conditional Skewness in Asset Pricing Tests. Journal of Finance, 55 (3), 1263- 1295.
Financial Management Perspective
Volume 12, Issue 38
July 2022
Pages 9-33

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