On the Effectiveness of Stop-Loss Rules: An Analytical Framework Based on Modeling Overnight Gaps and the Stationary Bootstrap

Abstract

A stop-loss rule is a risk management tool whereby the investor predefines some condition that, upon being triggered by market dynamics, imply the liquidation of her outstanding position. Such a tool is widely used by practitioners in financial markets with the hope of improving their investment performance by cutting losses and consolidating gains. But, do stop-loss rules really add value to an investment strategy? In this work we give an answer to this question for four popular implementations of stop-loss rules using two different statistical methods, which complement each other and lead to a more robust answer. On the one hand, we use a model-based approach in which we present two different models for the price of a New York Stock Exchange equity, which consider the phenomenon of overnight gaps; on the other hand, we complement the previous approach with a data-based framework by using the stationary bootstrap to obtain different replicas of the financial time series corresponding to the historical price of several NYSE stocks. As a general conclusion we find that, in rising markets, stop-loss rules improve the expected risk-adjusted return according to most metrics, while improving absolute expected return in falling markets. Furthermore, we find that in general the fixed percentage stop-loss rule may be the most powerful among the popular rules that we consider in this work, followed by the RSI-based stop-loss rule.