On the efficacy of stop-loss rules in the presence of overnight gaps

A stop-loss rule is a risk management tool whereby the investor predefines some condition that, upon being triggered by market dynamics, implies 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. We analyze in this work the performance of four popular implementations of stop-loss rules applied to asset prices whose returns are modeled with consideration of overnight gaps, that is, jumps from the closing price of one day to the opening price of the next trading day. In addition, our models include acute momentary price drops (flash crashes), which are often believed to erode the performance gains that might be derived from stop-loss rules. For this analysis we consider different models of asset returns: random walk, autoregressive and regime-switching models. In addition, we test the performance of the considered stop-loss rules in a non-parametric, data-driven framework based on the stationary bootstrap. As a general conclusion we find that, even when including overnight gaps and flash crashes in our price models, 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 simple fixed percentage stop-loss rule may be, in risk-adjusted terms, the most powerful among the popular rules that this work considers.