##plugins.themes.bootstrap3.article.main##

A stop-loss order is a method that can be used by investors to limit downside risk and can be explained by investors’ loss aversion. Loss aversion refers to the widely studied psychological phenomenon where expected losses have a greater impact on the investors’ preferences than expected gains. The use of stop-loss rules allows for this asymmetric profile. The stop loss hypothesis, also, states that higher returns can be obtained by limiting the downside risk of long positions. A stop-loss order can be established at a percentage of the asset value that grows over time as it rises, stopping if the asset value starts to go down (trailing stop-loss order). In this work, it is used trailing stop-loss orders (with a difference to the asset market value of 3%) with long positions in financial markets. After being stopped out in a down market, the re-entry rule is to buy the asset again as soon as the market grows by at least 3%. Whenever the portfolio is sold out by applying the trailing stop-loss rule, the investment is held in cash until the trigger is set off. In this article the rules are applied in Germany, France, Spain, and Greece during the financial crisis, to make an empirical validation of trailing stop-loss rules and re-entry rules for these markets.  It was found that the followed strategy presents mixed results, proving to beat the buy-and-hold strategy in some scenarios of rising and falling prices. But the most important result is to generate fewer losses in very volatile scenarios such as Greece in 2014.

Downloads

Download data is not yet available.

References

  1. Clare, A., Seaton, J., & Smith, P. (2013). Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500. Journal of Asset Management, 14, 182–194. https://doi.org/10.1057/jam.2013.11.
     Google Scholar
  2. Fifield, S., Power, D., & Sinclair, D. (2005). An analysis of trading strategies in eleven European stock markets. European Journal of Finance, 11(6), 531–548. https://doi.org/10.1080/1351847042000304099.
     Google Scholar
  3. Gros D., Alcidi, C., Belke, A., Coutinho, L., & Giovannini, A. (2014). Implementation of the Macroeconomic Adjustment Programmes in the Euro Area State‐Of‐Play. Centre for European Policy Studies E‐book.
     Google Scholar
  4. Islam, R. (2016). Growth Recovery in Southern Europe: A Dozen Lessons, Old and New. World Bank Policy Research. Working Paper No. 7877.
     Google Scholar
  5. Kaminski, K., & Lo, A. (2014). When do stop-loss rules stop losses? Journal of Financial Markets, 18(C), 234–254. https://doi.org/10.1016/j.finmar.2013.07.001.
     Google Scholar
  6. Keim, D. & Madhavan, A. (1995). Anatomy of the Trading Process Empirical Evidence on the Behavior of Institutional Traders. Journal of Financial Economics, 37(3), 371–398. https://doi.org/10.1016/0304-405X(94)00799-7.
     Google Scholar
  7. Kim, J., Lim, K. & Shamsuddin, A. (2011). Stock return predictability and adaptive markets hypothesis: evidence from century-long US Data. Journal of Empirical Finance, 18, 868–879. https://doi.org/10.1016/j.jempfin.2011.08.002.
     Google Scholar
  8. Klement, J. (2013). Assessing Stop-Loss and Re-Entry Strategies. The Journal of Trading. Fall 2013, 8(4), 44–53. https://doi.org/10.3905/jot.2013.8.4.044.
     Google Scholar
  9. Lei, A. & Li, H. (2009). The value of stop loss strategies. Financial Services Review. 18(1), 23–51. http://dx.doi.org/10.2139/ssrn.1214737.
     Google Scholar
  10. Lesmond, D., Ogden, J., & Trzcinka, C. (1997). A new measure of total transaction costs. Working Paper, SUNY-Buffalo, Buffalo, NY.
     Google Scholar
  11. Lo, A. (2004). The adaptive markets hypothesis: market efficiency from an evolutionary perspective. Journal of Portfolio Management (30th Anniversary Issue), 15–29. https://doi.org/10.3905/jpm.2004.442611.
     Google Scholar
  12. Montier, J. (2007). Behavioral Investing: A practitioner’s guide to applying behavioral finance. Wiley, Chichester, West Sussex, England.
     Google Scholar
  13. Neely, C., Rapach, D., Tu, J., & Zhou, G. (2014). Forecasting the Equity Risk Premium: The Role of Technical Indicators. Management Science 60(7), 1772–1791. https://doi.org/10.1287/mnsc.2013.1838.
     Google Scholar
  14. O’ Neil, W. (1988). How to Make Money in Stocks. McGraw–Hill, New York.
     Google Scholar
  15. Shefrin, h., & Statman, M. (1985). The Disposition to Sell Winners too Early and Ride Losers too Long. The Journal of Finance 40, 777-790.
     Google Scholar
  16. Taylor, N. (2014). The rise and fall of technical trading rule success. Journal of Banking & Finance, 40(C), 286–302. https://doi.org/10.1016/j.jbankfin.2013.12.004.
     Google Scholar
  17. Teweles, R., & Jones, F. (1987). The Futures Game: Who Wins? Who Loses? Why? McGraw–Hill, New York.
     Google Scholar
  18. Tooth, S. (2014). On the Efficacy of Stop-Loss Strategies. The Journal of Trading. Fall 2014, 9 (4), 100–107. https://doi.org/10.3905/jot.2014.9.4.100.
     Google Scholar
  19. Zhu, L., & Zhang S. (2010). Investment Decision Under Constraint of Loss Aversion. 3rd International Conference on Information Management, Innovation Management and Industrial Engineering. https://doi.org/10.1109/ICIII.2010.329.
     Google Scholar