Backtesting Strategies Based on Multiple Signals

Strategies selected by combining multiple signals suffer severe overfitting biases, because underlying signals are typically signed such that each predicts positive in-sample returns. "Highly significant" backtested performance is easy to generate by selecting stocks on the basis of combinations of randomly generated signals, which by construction have no true power. This paper analyzes t-statistic distributions for multi-signal strategies, both empirically and theoretically, to determine appropriate critical values, which can be several times standard levels. Overfitting bias also severely exacerbates the multiple testing bias that arises when investigators consider more results than they present. Combining the best k out of n candidate signals yields a bias almost as large as those obtained by selecting the single best of n^k candidate signals