In this note, Marcos Lopez de Prado and Alex Lipton highlight three lessons that quantitative researchers could learn after many quantitative firms suffered substantial losses during the covid-19 crisis selloff, including among funds that offer market neutral strategies.
1. More nowcasting, less forecasting
2. Develop theories, not trading rules
3. Avoid all regime strategies
Nowcasting is the future
Nowcasting models use unstructured datasets to make: direct measurements, for which the target variable is directly observed (e.g. the basket of products used to estimate inflation); and short-range predictions, for which the target variable is not directly observed (e.g., parking lot occupancy used to estimate revenue)
The advantages relative to forecasts are that direct measurements always hold true (they do not rely on a statistical lead-lag relationship), and short-range predictions are statistically more reliable than long-range predictions. In both cases, estimates involve millions of recent observations.
Most published discoveries in finance are false
It is common for academics and practitioners to run tens of thousands of historical backtests in order to identify a promising investment strategy. The best performing backtest is then reported as if a single trial had taken place, and selected for publication, or for launching a new fund
As a result of this selection bias, most published discoveries in finance are false. This fact explains why many funds have not performed as expected, including but not limited to the recent performance of quant funds during the COVID-19 crisis. Researchers should develop theories without backtesting, for example, with feature importance analysis methods that are robust to overfitting.
A functional theory explains a phenomenon by exposing a precise cause-effect mechanism. The validity of this cause-effect mechanism must be tested through backtesting (adjusted for selection bias), and collecting evidence against the ultimate implications of the proposed theory. Backtesting trading rules is not enough.
Don’t look for an all-regime holy grail
Academics and practitioners usually search for investment strategies that would have performed well across all market regimes. The likelihood that genuine “all-regime” strategies exist is rather slim, because markets are adaptive, and investors learn from mistakes. Even if all-regime strategies existed, they are likely to be a rather insignificant subset of the population of strategies that work across one or more regimes.
Regime-specific investment strategies are common among market makers. It allows them to adapt to new market conditions quickly. Thanks to nowcasting, funds can apply the same approach to strategy deployment.