  The macroeconomic climate influences operations with regard to, e.g., raw
material prices, financing, supply chain utilization and demand quotas. In
order to adapt to the economic environment, decision-makers across the public
and private sectors require accurate forecasts of the economic outlook.
Existing predictive frameworks base their forecasts primarily on time series
analysis, as well as the judgments of experts. As a consequence, current
approaches are often biased and prone to error. In order to reduce forecast
errors, this paper presents an innovative methodology that extends lag
variables with unstructured data in the form of financial news: (1) we apply a
variety of models from machine learning to word counts as a high-dimensional
input. However, this approach suffers from low interpretability and
overfitting, motivating the following remedies. (2) We follow the intuition
that the economic climate is driven by general sentiments and suggest a
projection of words onto latent semantic structures as a means of feature
engineering. (3) We propose a semantic path model, together with estimation
technique based on regularization, in order to yield full interpretability of
the forecasts. We demonstrate the predictive performance of our approach by
utilizing 80,813 ad hoc announcements in order to make long-term forecasts of
up to 24 months ahead regarding key macroeconomic indicators. Back-testing
reveals a considerable reduction in forecast errors.
