Loss aversion, stop-loss, and the behavior of the modeler

Most systematic trading systems have some form of stop-loss management. For example, a sell stop is set below the entry price of a buy order and represents the price that a trader will exit a long position. Many have looked at the setting of a stop-loss as a math problem based on the volatility of the markets and the expected loss on a position. These math frameworks are immensely useful, but it does not explain the wide range of stops used by traders.

The stop loss levels are closely associated with the level of loss aversion of the system designer.  You can senate the designer from the model. The fears and anxieties of the model builder will be represented in the stop-loss settings. This is one of the key features that distinguishes returns for CTA's. You our buying the manager's utility function as expressed through a well-formed set of rules.

This is where behavior finance enters the world of disciplined system trading. There is no such thing as a model system that does not embody the value system of the designer. Loss aversion is one of the key features of behavioral finance. In fact, the whole idea of prospect theory as a subset of utility theory is based on the idea that investors are more sensitive to losses than gains. This aversion is one of the reasons for why traders will hold losers and have profit targets versus spending considerable time focused on loss management and letting profits rise. The pain of taking a loss is greater than the benefit from a gain.

Since every modeler of a system has there own utility function, even if they want to cut losses and ride gains, each modeler will manage it in a different way. Hence, understanding the aversion to losses and risk are important in understanding how models are built. For example, are stop-losses adjusted after gains are made? Are there profit targets? Do stops reflect time? The model codifies the behavior of the modeler, so getting inside of the head of the modeler is relevant, no different than learning how a discretionary trader behaves.