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| Definitions | |||||
The model maps implied equity forwards onto index movements, capturing market expectations in forecast outcomes. Forecasts are generated via simulation, allowing a probability distribution to be constructed around the projected index path and enabling estimation of the likelihood that the index reaches or exceeds a given threshold over the forecast horizon.
|Date |Forecast (2027-06-30) |:----------|:--------------------- |2026-01-09 |340.26 |2026-01-20 |344.47 ± 6.80
Forecast probabilities reflect historical variations, which may not capture all sources of future uncertainty.
Comment deleted on Jan 21, 2026 03:32AM UTC
Comment deleted on Jan 21, 2026 03:32AM UTC
The model estimates forward seed funding levels by mapping clinical activity to historical funding levels, producing an implied funding trajectory through the forecast horizon. Given the volatile and asymmetric nature of these dynamics, simulations are used to estimate the probability that total seed funding reaches predefined thresholds. This approach reflects the inherent uncertainty in capital flows, producing more informative forecasts than linear projection.
|Date |Forecast (2025 + 2026) |:----------|:--------------------- |2026-01-20 |$798.2M ± $128.4M
Uncertainty is derived from the fitted relationship, which implicitly assumes that the historical linkage between clinical activity and funding intensity remains broadly stable throughout the forecast period. The limited number of observations may amplify uncertainty and reduce the reliability of extreme-value forecasts.
The model combines historical trends with macroeconomic indicators to estimate the probability that China’s year-over-year GDP growth will meet or exceed 5% over the forecast horizon. While domestic and international trade conditions provide some support, investment trends appear to weigh on upside potential.
|Date |Forecast (2026-Q2) |:----------|:--------------------- |2026-01-09 |3.66% ± 0.81 pp
While simulation-based forecasts improve its ability to capture asymmetric outcomes, the model remains sensitive to unforeseen events outside the sampling window.
The model maps implied equity forwards onto index movements, capturing market expectations in forecast outcomes. Forecasts are generated via simulation, allowing a probability distribution to be constructed around the projected index path and enabling estimation of the likelihood that the index reaches or exceeds a given threshold over the forecast horizon.
|Date |Forecast (2027-06-30) |:----------|:--------------------- |2026-01-09 |340.26
Forecast probabilities reflect historical variations, which may not capture all sources of future uncertainty.
The model combines historical trends with leading indicators to estimate the probability that the average OECD employment rate will fall within the predefined ranges over the forecast horizon.
|Date |Forecast (2025) |:----------|:--------------------- |2025-12-27 |70.7% ± 0.44 pp
The reliability of the model is constrained by the quality and completeness of historical data, as well as by structural breaks or unforeseen events that deviate from past conditions.
Why do you think you're right?
The model combines historical trends with macroeconomic indicators to estimate the probability that China’s year-over-year GDP growth will meet or exceed 5% over the forecast horizon. To preserve asymmetry observed in the data, forecasts are generated via simulation, reflecting a range of plausible future paths and potential outcomes. Findings suggest that while domestic and international trade provide some support, investment trends limit upside potential.
Why might you be wrong?
While simulation-based forecasts improve its ability to capture asymmetric outcomes, the model remains sensitive to unforeseen events outside the sampling window.