Data-Mining Bias: The Fool’s Gold of Objective TA
First Published Tuesday, 10th June 2008 07:59 am from Automated Trader
The following excerpt is from Chapter 6 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 5 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.
In rule data mining, many rules are back tested and the rule with the best observed performance is selected. That is to say, data mining involves a performance competition that leads to a winning rule being picked. The problem is that the winning rule's observed performance that allowed it to be picked over all other rules systematically overstates how well the rule is likely to perform in the future. This systematic error is the data-mining bias.