Testing Investment Forecast Efficiency with Forecasting Narratives

Autor: Alexander Foltas
Rok vydání: 2022
Předmět:
Zdroj: Jahrbücher für Nationalökonomie und Statistik. 242:191-222
ISSN: 2366-049X
0021-4027
Popis: I analyze the narratives that accompany business cycle forecasting reports of three German institutes using topic models. To this end, I gather multiple similar topics into different economic subject categories, allowing me to map shifting prioritizations within and between these subjects. Subsequently, I examine whether forecasting narratives contain additional information not captured by traditional indicators and include them in a random forest-based investment-forecast efficiency analysis. I find multiple correlations between narratives and forecast errors and conclude that forecasters inefficiently incorporate qualitative information in these cases. I raise the idea that further investigations with more precise identification of forecasting narratives could improve qualitative information processing or lead to scientific guidelines for forecast adjustments.
Databáze: OpenAIRE
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