here. Very simply put, this means that algorithms assume that the rules haven’t changed, or won’t change due to some event in the future.
Surprisingly, this goes against the basic admonition that almost all professional investors bake into their fine print, especially the one that says, “Past performance is no predictor of future performance."The paradox is that finding patterns and then using them to make useful predictions is what ML is all about in the first place.
But static assumptions have meant that the data sets used to train ML models haven’t included anything more than elementary “worst case" information.