Testing investment forecast efficiency with textual data - Alexander Foltas (Humboldt University Berlin, 2020)
I use textual data to model German professional macroeconomic forecasters' information sets and use machine-learning techniques to analyze the efficiency of forecasts. To this end, I extract information from forecast reports using a combination of topic models and word embeddings. I then use this information and traditional macroeconomic predictors to study the efficiency of investment forecasts.
urn:nbn:de:kobv:11-110-18452/22538-3
Keywords: C53; E27; E22; 330; Forecast Efficiency; Investment; Random Forest; Topic Modeling