Testing investment forecast efficiency with textual data

Foltas, Alexander

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.

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Year of publication:	[2020]
Authors:	Foltas, Alexander
Publisher:	Berlin : Humboldt-Universität
Subject:	Forecast Efficiency \| Investment \| Random Forest \| Topic Modeling \| Prognoseverfahren \| Forecasting model \| Theorie \| Theory \| Prognose \| Forecast \| Investition

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Extent:	1 Online-Ressource (circa 35 Seiten) Illustrationen
Series:	Working Papers of the Priority Programme 1859 : Experience and Expectation : Historical Foundations of Economic Behaviour. - Berlin : Humboldt-Universität zu Berlin, Computer- und Medienservice Elektronisches Publizieren, ISSN 2510-053X, ZDB-ID 2916406-0. - Vol. no 19 (2020, July)
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper
Language:	English
Other identifiers:	10.18452/21651 [DOI] hdl:10419/223420 [Handle]
Classification:	C53 - Forecasting and Other Model Applications ; E27 - Forecasting and Simulation ; E22 - Capital; Investment (including Inventories); Capacity
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012264861