Seminar 217, Risk Management: Backtest overfitting, stock fund design and forecast performance

Seminar | October 17 | 11 a.m.-1 p.m. | 639 Evans Hall

 Speaker: David Bailey, LBNL and UC Davis

 Center for Risk Management Research

Backtest overfitting means the usage of backtests (historical market data) to construct an investment strategy, fund or portfolio, when the number of variations explored exceeds limits of statistical reliability. We show that backtest overfitting is inevitable when computer programs are employed to explore millions or even billions of parameter variations (as is typical) to select an optimal variant. We illustrate this by showing that while it is a simple matter to design a stock fund, based only on a weighted collection of S&P500 stocks, that achieves any desired performance profile, these funds typically perform erratically at best when confronted with new, out-of-sample data. Similarly, we present results of a recent study of market forecasters, most of whom employ some sort of historical market data analysis, and show that few, if any, have a positive long-term record.