When I was younger I believed in a kind of physical determinism. I thought that if you knew exactly where everything was and exactly how fast it was going, you could perfectly predict everything. I think this belief persists with many folks in data science, especially those that are also software developers like myself. Anyone who hits the inference button on a statistical package is in danger of being seduced by the arbitrary precision in their output and confusing it with probable reality. Knowing the difference between significant and insignificant figures is something that was ingrained in me when I studied mechanical engineering and the gist of it is applicable here, too.
Interesting post. As you model financial markets, what approach or approaches do you take to avoid model over-specification?