*5.1. Experimental Design*

We used well-known data for our experiments; the historical prices and financial information about the stocks within the Standard and Poor's 500 (S&P500) index. The officially reported financial information was used to build criterion performances.

Data from some of the most recent ninety months were used as input in the experiments, i.e., from November 2013 to April 2021. This dataset contains both uptrends and downtrends, so it is convenient for the kind of tests performed here. From these periods, sixty are used to prepare (say, train) the algorithms, and the rest are used to assess the approach performance in a window-sliding manner. For example, the information on November 2013–October 2018 is used to determine the investments that should be carried out at the beginning of November 2018, and these investments are maintained the whole month. Then, the performance of the approach (i.e., the returns) is calculated at the end of November 2018 using Equation (4). Such a performance is compared to the benchmarks in that period. Later, the investments are neglected, and, independently, the lapse is slid one period; thus, now, the information of the sixty months—December 2013–November 2018—is used to determine the investments for December 2018, where the new approach performance is calculated and compared to the benchmarks. This procedure is repeated thirty times; so, the conclusions can shed light on the robustness and overall performance of the approach with a high degree of confidence.
