IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v82y2023ics0927538x23002433.html
   My bibliography  Save this article

Technical trading rules, loss avoidance, and the business cycle

Author

Listed:
  • Ergun, Lerby
  • Molchanov, Alexander
  • Stork, Philip

Abstract

We show that simple technical trading rule (TTR) strategies substantially reduce investment left tail risk. An investor following a TTR strategy can also avoid a high percentage of extremely negative returns. This percentage increases substantially during recessions. Interestingly, tail risk reduction does not come at a cost of lower performance – risk adjusted returns of TTR strategies are in fact higher than those of a buy-and-hold strategy. Our findings are robust to changes in trading strategy specifications. They hold in 38 international equity markets, as well as in a large sample of individual US stocks, and survive a reality check bootstrap.

Suggested Citation

  • Ergun, Lerby & Molchanov, Alexander & Stork, Philip, 2023. "Technical trading rules, loss avoidance, and the business cycle," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:pacfin:v:82:y:2023:i:c:s0927538x23002433
    DOI: 10.1016/j.pacfin.2023.102172
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X23002433
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2023.102172?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ritter, Jay R., 2005. "Economic growth and equity returns," Pacific-Basin Finance Journal, Elsevier, vol. 13(5), pages 489-503, November.
    2. Gregory-Allen, Russell & Lu, Helen & Stork, Philip, 2012. "Asymmetric extreme tails and prospective utility of momentum returns," Economics Letters, Elsevier, vol. 117(1), pages 295-297.
    3. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    4. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    5. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    6. Cihak, Martin & Demirguc-Kunt, Asli & Feyen, Erik & Levine, Ross, 2012. "Benchmarking financial systems around the world," Policy Research Working Paper Series 6175, The World Bank.
    7. Emil N. Siriwardane, 2019. "Limited Investment Capital and Credit Spreads," Journal of Finance, American Finance Association, vol. 74(5), pages 2303-2347, October.
    8. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    9. Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    12. Gul, Faruk, 1991. "A Theory of Disappointment Aversion," Econometrica, Econometric Society, vol. 59(3), pages 667-686, May.
    13. Kewei Hou & Chen Xue & Lu Zhang, 2020. "Replicating Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2019-2133.
    14. Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2017. "Time series momentum and moving average trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 405-421, March.
    15. Danielsson, Jon & Jorgensen, Bjorn N. & Sarma, Mandira & de Vries, Casper G., 2006. "Comparing downside risk measures for heavy tailed distributions," Economics Letters, Elsevier, vol. 92(2), pages 202-208, August.
    16. Ang, Andrew, 2014. "Asset Management: A Systematic Approach to Factor Investing," OUP Catalogue, Oxford University Press, number 9780199959327.
    17. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    18. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    19. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    20. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    21. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    22. Juhani T Linnainmaa & Michael R Roberts, 2018. "The History of the Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2606-2649.
    23. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
    24. Lesmond, David A. & Schill, Michael J. & Zhou, Chunsheng, 2004. "The illusory nature of momentum profits," Journal of Financial Economics, Elsevier, vol. 71(2), pages 349-380, February.
    25. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    26. Sander, Magnus, 2018. "Market timing over the business cycle," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 130-145.
    27. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    28. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    29. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    30. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    31. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, October.
    3. Jin, Xiaoye, 2022. "Testing technical trading strategies on China's equity ETFs: A skewness perspective," Emerging Markets Review, Elsevier, vol. 51(PA).
    4. Benjamin R. Auer, 2021. "Have trend-following signals in commodity futures markets become less reliable in recent years?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 533-553, December.
    5. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    6. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    7. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
    8. Baltussen, Guido & Swinkels, Laurens & Van Vliet, Pim, 2021. "Global factor premiums," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1128-1154.
    9. Osman Kilic & Joseph M. Marks & Kiseok Nam, 2022. "Predictable asset price dynamics, risk-return tradeoff, and investor behavior," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 749-791, August.
    10. Ebert, Sebastian & Hilpert, Christian, 2019. "Skewness preference and the popularity of technical analysis," Journal of Banking & Finance, Elsevier, vol. 109(C).
    11. Cristiana Tudor & Andrei Anghel, 2021. "The Financialization of Crude Oil Markets and Its Impact on Market Efficiency: Evidence from the Predictive Ability and Performance of Technical Trading Strategies," Energies, MDPI, vol. 14(15), pages 1-19, July.
    12. Enoch Cheng & Clemens C. Struck, 2019. "Time-Series Momentum: A Monte-Carlo Approach," Working Papers 201906, School of Economics, University College Dublin.
    13. Eom, Cheoljun & Park, Jong Won, 2023. "Price behavior of small-cap stocks and momentum: A study using principal component momentum," Research in International Business and Finance, Elsevier, vol. 65(C).
    14. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Asymmetric Risk Impacts of Chinese Tourists to Taiwan," Documentos de Trabajo del ICAE 2018-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    15. Kai Li, 2014. "Asset Price Dynamics with Heterogeneous Beliefs and Time Delays," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2014.
    16. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    17. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021.
    18. 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.
    19. Strobel, Marcus & Auer, Benjamin R., 2018. "Does the predictive power of variable moving average rules vanish over time and can we explain such tendencies?," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 168-184.
    20. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.

    More about this item

    Keywords

    Tail risk; Technical trading rules; Loss avoidance;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:pacfin:v:82:y:2023:i:c:s0927538x23002433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.