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When Do Stop-Loss Rules Stop Losses?

Author

Listed:
  • Kaminski, Kathryn

    (Risk & Portfolio Management)

  • Lo, Andrew W.

    (MIT Sloan School of Management)

Abstract

Stop-loss rules-predetermined policies that reduce a portfolio's exposure after reaching a certain threshold of cumulative losses-are commonly used by retail and institutional in- vestors to manage the risks of their investments, but have also been viewed with some skep- ticism by critics who question their e±cacy. In this paper, we develop a simple framework for measuring the impact of stop-loss rules on the expected return and volatility of an arbitrary portfolio strategy, and derive conditions under which stop-loss rules add or subtract value to that portfolio strategy. We show that under the Random Walk Hypothesis, simple 0/1 stop-loss rules always decrease a strategy's expected return, but in the presence of momen- tum, stop-loss rules can add value. To illustrate the practical relevance of our framework, we provide an empirical analysis of a stop-loss policy applied to a buy-and-hold strategy in U.S. equities, where the stop-loss asset is U.S. long-term government bonds. Using monthly returns data from January 1950 to December 2004, we find that certain stop-loss rules add 50 to 100 basis points per month to the buy-and-hold portfolio during stop-out periods. By computing performance measures for several price processes, including a new regime- switching model that implies periodic "flights-to-quality", we provide a possible explanation for our empirical results and connections to the behavioral finance literature.

Suggested Citation

  • Kaminski, Kathryn & Lo, Andrew W., 2008. "When Do Stop-Loss Rules Stop Losses?," SIFR Research Report Series 63, Institute for Financial Research.
  • Handle: RePEc:hhs:sifrwp:0063
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    Cited by:

    1. Andrew Clare & James Seaton & Peter N Smith & Stephen Thomas, 2013. "Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500," Journal of Asset Management, Palgrave Macmillan, vol. 14(3), pages 182-194, June.
    2. Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Białkowski, Jędrzej, 2020. "Cryptocurrencies in institutional investors’ portfolios: Evidence from industry stop-loss rules," Economics Letters, Elsevier, vol. 191(C).
    4. Yang, Chunpeng & Zhang, Zhanpei, 2021. "Realization utility with stop-loss strategy," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 261-275.
    5. Chun-Hao Chen & Yu-Hsuan Chen & Vicente Garcia Diaz & Jerry Chun-Wei Lin, 2023. "RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm," Electronic Commerce Research, Springer, vol. 23(1), pages 3-42, March.
    6. Bochuan Dai & Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2021. "Risk reduction using trailing stop‐loss rules," International Review of Finance, International Review of Finance Ltd., vol. 21(4), pages 1334-1352, December.
    7. Žikica Lukić & Bojana Milošević, 2024. "Change-point analysis for matrix data: the empirical Hankel transform approach," Statistical Papers, Springer, vol. 65(9), pages 5955-5980, December.
    8. Jessica James & Louis Yang, 2010. "Stop-losses, maximum drawdown-at-risk and replicating financial time series with the stationary bootstrap," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 1-12.
    9. Dimitrios Vezeris & Themistoklis Kyrgos & Christos Schinas, 2018. "Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System," JRFM, MDPI, vol. 11(3), pages 1-23, September.
    10. Henderson, Vicky & Hobson, David & Tse, Alex S.L., 2018. "Probability weighting, stop-loss and the disposition effect," Journal of Economic Theory, Elsevier, vol. 178(C), pages 360-397.
    11. John Hua Fan & Tingxi Zhang, 2024. "Commodity premia and risk management," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1097-1116, July.
    12. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.
    13. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    14. Ardia, David & Boudt, Kris & Hartmann, Stefan & Nguyen, Giang, 2022. "Properties of the Margrabe Best-of-two strategy to tactical asset allocation," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Gregory Gadzinski & Markus Schuller & Shabnam Mousavi, 2022. "Long-lasting heuristics principles for efficient investment decisions," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 14(4), pages 570-583, March.
    16. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
    17. Hong, Xin & Pang, Ningjing & Wang, Zhibin, 2022. "Stop-loss early termination clause and hedge fund performance," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    18. Rui Wang, 2021. "Discriminating modelling approaches for Point in Time Economic Scenario Generation," Papers 2108.08818, arXiv.org.
    19. Sadaqat, Mohsin & Butt, Hilal Anwar, 2023. "Stop-loss rules and momentum payoffs in cryptocurrencies," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    20. Gerritsen, Dirk F., 2016. "Are chartists artists? The determinants and profitability of recommendations based on technical analysis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 179-196.

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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