Scenario-Based Forecasting Method (SBFM)
Forecast
Investment analysts communicate price targets and their associated narratives in terms of scenarios such as worst case, base case, and best case.
| Min | Worst | Likely | Best | Max |
|---|---|---|---|---|
| -52.6% | -26.0% | 5.3% | 39.0% | 93.8% |
Probability Distribution
SBFM maps these scenarios to a probability distribution.
Risk and Return
Probability distribution forecasts yield risk and return metrics.
| Mean | Stdev | Skewness | Kurtosis |
|---|---|---|---|
| 5.9% | 25.6% | 0.5 | 0.32 |
- Leverages the large amount of information embedded in human forecasts and the computationally efficient techniques of quantitative investing
- Models asymmetrical and “fat-tailed” distributions
- Avoids dependency on historical data and inductive logic (“Black Swan” problem)
- Enables stable, long-term investment forecasts
- Transparently translates analyst recommendations to portfolio positions
- Allows objective assessment of forecast accuracy and precision via proper scoring metrics