Journal of Investment Strategies

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Systematic testing of systematic trading strategies

Kovlin Perumal and Emlyn Flint

  • Statistical methodologies to eradicate data-mining bias (DMB) were critically evaluated under controlled conditions.
  • The relationships between DMB present against the variables affecting market conditions were identified.
  • Under our simulation process, the methods of White’s Reality Check and Monte-Carlo Permutation were shown to be the most effective in eradicating data-mining bias. The Step-M procedure was also shown to have merit.
  • Bound manipulation methods were shown to be inferior to those that directly produce p-values.

Systematic trading is a method that is currently extremely popular in the investment world. The testing of systematic trading rules is usually done through backtesting and is at high risk of spurious accuracy as a result of the data-mining bias (DMB) present when testing multiple rules concurrently over the same historical period. The eradication of this DMB through the use of statistical methodologies is currently a relevant topic in investment research, as is illustrated by papers written by Chordia, Goyal and Saretto in 2017, by Harvey and Liu in 2014, by Novy-Marx in 2016 and by Peterson in 2015. This study reviews the various statistical methodologies that are in place to test multiple systematic trading strategies and implements these methodologies under simulation with known artificial trading rules in order to critically compare and evaluate them.

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