At its heart, quantitative investing is about leading with data and following what works. While this approach must make peace with the alternative – namely, the potential upside of smart individual stock selection – it can’t entirely escape the nagging sense that it is making bad choices. What if the data is wrong? Or right but no longer applicable to today’s market and investor sentiment? Or that ‘what works’ is just a set of spurious correlations?
Above all, the quant’s curse is the feeling that somewhere out there, among the reams of price data, financial ratios and analyst estimates, there is a better way to apply, combine or tweak her chosen metrics. After all, if there are good and bad stockpickers, why can’t there be good and bad stock-screeners?
Some version of this fear is present across each of our screens. But it’s especially acute when a once-brilliant methodology starts to misfire.