The initial round of testing for two new versions of Synthetic Systems is complete. For now let’s call the version we’ve been using for the past few years “Synthetic Systems” (SS), the next version “Symmetric Synthetic Systems” (SSS), and the newest version “Systems” (S). Sorry for the confusing naming, but it’s just temporary to distinguish them from each other. We’ll settle on a final name when it’s time for regular publication.
To quantify the relative accuracy of the Synthetic Systems embodiments, here are the aggregated results of 24 semiannual test runs for each of the three from the beginning of 2010 to the middle of 2021. The numbers represent a weighted sum of 64 squared differences between the projected forecast and the actual outcome over the year following the run date, for each of the five asset classes over the combined 24 runs. So a lower number indicates greater accuracy. The raw numbers of course don’t have much intuitive meaning, but the comparisons between versions shows their relative accuracy.
As you can see, versus SS, both SSS & S represent a reduction of about 25% in aggregate forecasting error. The newest version S has the advantage of being more compact and runs in a fraction of the time of the others. The slight apparent accuracy advantage of SSS is statistically negligible, and the reduced run time for S will ultimately translate into better accuracy anyway because it will facilitate fine tuning.
Here are the charts as of 2022.25 for each of the three versions.
Despite their similar overall accuracy scores SSS and S vary in individual forecasts. Pending final testing, I may publish both … comparing the two can shed light on the areas of most uncertainty. As of 2022.25, however, both of these more accurate versions agree on a more moderate summer rally in stocks and copper than SS, as well as a more positive outlook for bonds. It’s for this reason that I want to call readers attention to them before the next regular update at mid year.
Both of the newer versions represent a fundamental departure from the original in that the original wasn’t intended to produce explicit market forecasts. It was designed to estimate a smoothed current rate of return for each asset class, and that was my original use for it. It became apparent that an explicit forecast was only a couple steps away though, and that that format would easier for readers to relate to without a thicket of mathy interpretation guidance. So the explicit forecast charts were practically a by-product. But the new versions are designed the other way around … they produce explicit forecasts by design, a first for Synthetic Systems.