Risk-managed industry momentum and momentum crashes

Abstract

This paper investigates Barroso and Santa-Clara’s [J. Financ. Econ., 2008, 116, 111–120] risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx [J. Financ. Econ., 2012, 103, 429–453]. We moreover examine the impact of different variance forecast horizons on average pay-offs and also Daniel and Moskowitz’s [J. Financ. Econ., 2016, 122, 221–247] optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject to optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the ‘echo effect’ in industries is not robust in subsamples as the strategy works only during the most recent subsample.

Keywords:

• Asset pricing momentum crash
• Industry momentum
• Optionality effect

JEL classification:

• G12
• G14

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgements

We are grateful for the comments received from the participants of the INFINITI Conference 2017 in Valencia (Spain). We also appreciate the useful comments from the participants at the Accounting and Finance seminar 2016 at the University of Vaasa. In particular we would like to thank Seppo Pynnönen and Vitaly Orlov for their helpful comments. Finally, we would like to thank an anonymous referee for useful comments.

Notes

^§ Other recent work on the Barroso and Santa-Clara (2015) strategy or similar volatility scaling techniques includes Dudler et al. (2015), Jacobs et al. (2015), Kim et al. (2016), Baltas (2015), Baltas and Kosowski (2015) and Moskowitz et al. (2012).

^† Jegadeesh and Titman’s (1993) investigate a strategy that cumulates returns from month t-6 (J = 6) until t – 1 (L = 1), and holds the portfolios one month ahead, that is, until t + 1 (K = 1). This strategy is referred to as 6-1-1 (J/L/K). In contrast, Novy-Marx (2012) proposes a momentum strategy that cumulates returns from month t – 12 (J = 12) until t-7 (L = 7), and holds the portfolios one month ahead, that is, until t + 1 (K = 1). This strategy is referred to as 12-7-1 (J/L/K). In the same manner, the 12-1-1 is a strategy that cumulates returns from month t – 12 until t – 1, and holds the portfolios one month ahead, that is, until t + 1, whereas the 1-0-1 strategy is a strategy that cumulates returns based upon the previous month’s return, and the portfolios one month ahead.

^† The realized volatilities for the 6-1-1 and 12-7-1 strategies are very similar and available from the authors upon request.

^† Using a three-month and one-month time window to estimate the variance forecasts yields very similar results. The results are available from the authors upon request.

^† A possible explanation could be the evolution of available industries, as shown in figure A2 in the Appendix 1.

^† The results presented in table 7 are for a risk-managed strategy with a target chosen to have the same ex-post standard deviation as the original strategy’s volatility, i.e. 22.55%. By so doing the comparisons with crash risks are valid. The volatility of original strategy is obtained using a variance forecast with a one-month time window and a scaling factor of 9.12%.

^† Interestingly, the plain industry momentum strategies’ monthly net returns (i.e. after costs for turnovers) are estimated at 1.00, 0.67, and 0.68% for 12-1-1, 6-1-1, and 12-7-1 strategies, respectively, which means that risk-managing is only beneficial for the 12-1-1 and 6-1-1 strategies, given our conservative assumptions.