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Predicting Forex Rates using Sentiment Analysis on Financial Articles

Gramer, Arvid LU and Danielsson, Simon LU (2023) NEKH01 20222
Department of Economics
Abstract
We develop a deep learning model that uses financial news articles and historical exchange rates to predict the rate for the Euro/US Dollar currency pair one hour ahead. From the articles’ titles and bodies we extract sentiment scores using two different transformer based classifiers – one for short text and one for long text. The long-text classifier is built and trained by us. These sentiment scores are used as a proxy for the market participants’ mood. We combine the mood with technical indicators derived from historical exchange rates to make the future prediction. Our long-text sentiment classifier obtains 72% test accuracy, and our predictive Forex model achieves 44% improved test loss compared to a similar model not leveraging... (More)
We develop a deep learning model that uses financial news articles and historical exchange rates to predict the rate for the Euro/US Dollar currency pair one hour ahead. From the articles’ titles and bodies we extract sentiment scores using two different transformer based classifiers – one for short text and one for long text. The long-text classifier is built and trained by us. These sentiment scores are used as a proxy for the market participants’ mood. We combine the mood with technical indicators derived from historical exchange rates to make the future prediction. Our long-text sentiment classifier obtains 72% test accuracy, and our predictive Forex model achieves 44% improved test loss compared to a similar model not leveraging sentiment from article bodies. Moreover, the Forex model heavily outperforms a model not using financial articles at all. Using these results we discuss the efficiency of the EUR/USD currency market. It hints at some inefficient tendencies in the market, but more data and further experiments are needed before any conclusion can be drawn. (Less)
Please use this url to cite or link to this publication:
author
Gramer, Arvid LU and Danielsson, Simon LU
supervisor
organization
course
NEKH01 20222
year
type
M2 - Bachelor Degree
subject
keywords
Forex, efficient market hypothesis, sentiment analysis, transformer, long-short term memory
language
English
id
9109093
date added to LUP
2023-03-28 09:00:32
date last changed
2023-03-28 09:00:32
@misc{9109093,
  abstract     = {{We develop a deep learning model that uses financial news articles and historical exchange rates to predict the rate for the Euro/US Dollar currency pair one hour ahead. From the articles’ titles and bodies we extract sentiment scores using two different transformer based classifiers – one for short text and one for long text. The long-text classifier is built and trained by us. These sentiment scores are used as a proxy for the market participants’ mood. We combine the mood with technical indicators derived from historical exchange rates to make the future prediction. Our long-text sentiment classifier obtains 72% test accuracy, and our predictive Forex model achieves 44% improved test loss compared to a similar model not leveraging sentiment from article bodies. Moreover, the Forex model heavily outperforms a model not using financial articles at all. Using these results we discuss the efficiency of the EUR/USD currency market. It hints at some inefficient tendencies in the market, but more data and further experiments are needed before any conclusion can be drawn.}},
  author       = {{Gramer, Arvid and Danielsson, Simon}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Predicting Forex Rates using Sentiment Analysis on Financial Articles}},
  year         = {{2023}},
}