Stock Portfolio Design and Backtest Overfitting

We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform erratically on more recent, out-of-sample data, which is symptomatic of selection bias. One implication of these results is that so-called smart beta funds, which are designed in-sample to deliver a desirable performance pro file, are likely to disappoint out-of-sample