To evaluate the two methods, we performed 1,000 random trials of training and out-of-sample testing. For each trial, we drew a random half from the historical sample to use as training data—the other half was used as testing data. From the training data, we identified a turbulent subsample by calculating the turbulence index, and subsequently selecting the periods with the highest quartile (the highest turbulence index values). Using the full training sample, we build an unconditioned optimal portfolio that did not account for turbulence. Using the turbulent sample, combined with some information from the full training sample, we built a conditioned optimal portfolio that was expected to be more resistant to turbulence than an unconditioned portfolio. We then used the testing data to test both the unconditioned and the conditioned portfolios, and performed two types of testing: tone on the full testing sample and the other on a turbulent subsample within the testing sample.