One of us (Marcos Lopez de Prado) has published the article Mathematics and economics: A reality check in the *Journal of Portfolio Management*. The article is open-access — there is no fee for viewing or downloading.

Lopez de Prado argues that while economics is arguably one the most mathematical of the social sciences, the mathematical methods of economists may not be up to the task of modeling the complexity of the social institutions and the business/finance world. Outdated and inappropriate statistical methods are of particular concern, with economists and econometricians often drawing very dubious conclusions from the available data.

The author suggests that graph theory, topology and even information theory and signal processing may be significantly more appropriate for these models. Machine learning methods may also be useful here.

Another issue is the inappropriate utilization of experimental methods, in particular the backtest. As we have demonstrated in several recent papers, backtest overfitting, selection bias and other errors are increasingly common in the field. In fact, in a time when one can write simple computer programs to explore millions or billions of variations of a proposed financial strategy, testing each based on historical backtests, and then only selecting the very best option, then that optimal variation is virtually certain to be statistically overfit. And if such a strategy is actually deployed, then the results could be disastrous.

Lopez de Prado then suggests that perhaps financial academics should commit a portion of their salaries to a validation of their proposed strategies and analysis — a documented track record…

Full details are in the published article.