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Is there macroeconomic predictive power in Swedish business news?

Ris, Erik LU (2021) NEKH01 20211
Department of Economics
Abstract
This thesis explores if, and how, what is written in the newspaper can be used to forecast macroeconomic variables such as inflation, unemployment and consumption. A large data set consisting of articles from the largest Swedish business newspaper is transformed using different methods from the Natural Language Processing field. The focus lies on topic modelling by Latent Dirichlet Allocation as well as sentiment analysis. The newspaper data are represented as a combination of the distribution over topics covered in the newspaper as well as sentiment scores of prominent articles. The data representations of the newspaper are created, explored and later used to predict the movement of the economic variables using Lasso regression, a method... (More)
This thesis explores if, and how, what is written in the newspaper can be used to forecast macroeconomic variables such as inflation, unemployment and consumption. A large data set consisting of articles from the largest Swedish business newspaper is transformed using different methods from the Natural Language Processing field. The focus lies on topic modelling by Latent Dirichlet Allocation as well as sentiment analysis. The newspaper data are represented as a combination of the distribution over topics covered in the newspaper as well as sentiment scores of prominent articles. The data representations of the newspaper are created, explored and later used to predict the movement of the economic variables using Lasso regression, a method automatically selecting important input variables. The newspaper data on stand-alone basis have not been shown to have predictive power for these macroeconomic variables. But, when allowing the model to also be trained on the lagged economic variable interesting observations are made. The predictive performance is improved by the newspaper data, in comparison to only using the lagged economic variable. This is the case for expected inflation, CIPF-inflation and unemployment. (Less)
Please use this url to cite or link to this publication:
author
Ris, Erik LU
supervisor
organization
course
NEKH01 20211
year
type
M2 - Bachelor Degree
subject
keywords
Topic modelling, Sentiment Analysis, Newspaper Data, NLP, Lasso Regression
language
English
id
9064242
date added to LUP
2021-10-14 10:21:06
date last changed
2021-10-14 10:21:06
@misc{9064242,
  abstract     = {{This thesis explores if, and how, what is written in the newspaper can be used to forecast macroeconomic variables such as inflation, unemployment and consumption. A large data set consisting of articles from the largest Swedish business newspaper is transformed using different methods from the Natural Language Processing field. The focus lies on topic modelling by Latent Dirichlet Allocation as well as sentiment analysis. The newspaper data are represented as a combination of the distribution over topics covered in the newspaper as well as sentiment scores of prominent articles. The data representations of the newspaper are created, explored and later used to predict the movement of the economic variables using Lasso regression, a method automatically selecting important input variables. The newspaper data on stand-alone basis have not been shown to have predictive power for these macroeconomic variables. But, when allowing the model to also be trained on the lagged economic variable interesting observations are made. The predictive performance is improved by the newspaper data, in comparison to only using the lagged economic variable. This is the case for expected inflation, CIPF-inflation and unemployment.}},
  author       = {{Ris, Erik}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Is there macroeconomic predictive power in Swedish business news?}},
  year         = {{2021}},
}