Systematic Testing of Systematic Trading Strategies

Abstract

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 from testing multiple rules concurrently over the same history. The eradication of this DMB with the use of statistical methodologies is currently a relevant topic in investment research, illustrated by papers written by Chordia et al. (2017), Harvey and Liu (2014), Novy-Marx (2016) and Peterson (2015). This study collectively reviews the various statistical methodologies 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.

Keywords: Data-Mining Bias, Systematic Trading, Backtesting, Multiple Hypothesis Testing, False Discovery Rate, Family-Wise Error Rate, White's Reality Check, Monte Carlo Permutation

JEL Classification: C01, C02, C12, C15, C20, C3, C52, C53, C63, G00, G11, B41