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. Read more about his research: http://www.davidhbailey.com/
- Slides
- Start date: 2017-10-17 11:00:00
- End date: 2017-10-17 12:30:00
- Venue: 639 Evans Hall at UC Berkeley
- Address: 639 Evans Hall, Berkeley, CA, 94720