Algorithmic Short Selling with Python by Laurent Bernut
Author:Laurent Bernut
Language: eng
Format: epub
Tags: COM044000 - COMPUTERS / Neural Networks, COM051360 - COMPUTERS / Programming Languages / Python, COM018000 - COMPUTERS / Data Processing
Publisher: Packt
Published: 2021-09-29T21:51:57+00:00
Losses
The risk associated with mean reversion strategies is in the tails: a few titanic losses. Mean reversion market participants do not like stop losses. They are sometimes triggered before stocks start to behave as expected. The win rate drops, average loss rises, expectancy tanks. One approach is to have wider stop losses. They will warrant smaller positions but they will also prevent devastating losses.
Mean reversion market participants are often tempted to add to losing positions on the premise that they will eventually revert. For example, if a stock was expected to revert at 2.5 standard deviations from the mean, at 4 standard deviations, it would even be more attractive. This may work most of the time, but when it does not, losses can be devastating. LTCM tried that. Martingale works until it does not.
A statistically more robust yet counterintuitive approach is to reduce size when market participants would feel like adding. Assume that past a certain point, inefficiencies will not correct but persist. If instead of adding to a loser at 4 standard deviations, the position was reduced, then either it would revert and offset the losses or it would detract less thereafter. Either way, it would tilt the skew of losses toward breaking even.
Another practical reason why market participants should reduce instead of adding to losing positions is the deterioration of GE. Adding to a loser directly increases the loss rate and average loss. It generally works until it breaks.
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