Testing Tactical Investment Rules

How can investment strategy researchers best address the randomness inherent in market data and the ability of investors/markets to adapt to changing conditions? In his September 2019 paper entitled “Tactical Investment Algorithms”, Marcos Lopez de Prado reviews three methods for testing the performance of an investment rule:

1. Walk-forward (WF) tests a rule against an actual historical data series, implicitly assuming that market behaviors are neither largely random nor changing (that the rule being tested is “all-weather”).
2. Resampling (RS) addresses randomness in market behaviors by assuming that resampling of past observations can usefully generate possible future price paths. 
3. Monte Carlo (MC) addresses both randomness and adaptation in market behaviors by simulating possible future price paths based on models of price generation derived from theory and statistical analysis of actual data.

Based on his knowledge of financial markets and testing methods, he concludes that: (more…)