Leverage real-time news sentiment, alternative data, and macroeconomic
variables to project GDP growth.
The RavenPack China GDP Nowcasting model leverages proprietary machine learning models to deliver
a prediction of China’s Economic Activity: The Nowcasting model predicts, on a daily basis, the China GDP growth rate
for the previous quarter Q–1 (Backcast), the current quarter Q (Nowcast),
and the next Q+1, Q+2, Q+3, and Q+4 quarters (Forecasts). Beside macro variables and sentiment scores, the model also draws from a broad range of alternative data sources,
from freight to electricity production. Principal components are presented similar to a PMI index to better capture any sudden changes in the data.
The RavenPack China GDP Nowcast leverages datapoints beyond macroeconomic variables such as:
as well as over 20 distinct macroeconomic factors.
The model provides information about which variables altered the final prediction, and the magnitude of its impact. Follow the forecast line
to find out more:
can help quantitative and discretionary investors:
The combination of high-frequency macro factors and traditional variables, with refined sentiment scores
to reduce noise, delivers more accurate forecasts.
The nowcast, backcast, and forecasts aid in identifying economic trends and timing portfolio rebalancing,
especially when combined with inflation and other nowcasts.
The performance of the RavenPack China GDP Nowcasting model can be measured against a nowcasting Dynamic Factor Model based solely on macroeconomic variables in a run-up against the benchmark of an auto-regressive model.
The model that produces the RavenPack nowcast improves on the DFM model by up to 9%, and produces more accurate predictions as early as 22 weeks before official figures are released.
Backcasts, nowcasts, and forecasts are the outcome of a multi-step process that includes:
RavenPack analyzes textual content from high-quality sources to
identify events that are relevant to the Chinese economy. Documents
considered include articles and press releases in English pertaining to
specific economic narratives.
Additional filters ensure the data remains focused on contemporary
observations, and relevant organizations. In addition, events that relate
to the comparison between expectations and actual economic figures
are excluded to reduce noise.
For each event identified, RavenPack uses sentiment analysis to
compute multiple scores by matching stories usually categorized by
financial experts as having a positive or negative financial or economic
Sentiment scores are averaged and smoothed over time then
introduced into a dynamic factor model as stationary monthly input
variables with daily updates. Over 20 additional factors are also
included, from macroeconomic data points to alternative data.
The model produces predictions over 6 quarters, from backcast to
forecast, with daily updates.
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