Regression models for Statistical Arbitrage: Predicting Currency Exchange Rates from News Media

EasyChair Preprint 2415

Abstract

In this paper, we explore the application of regression models for predicting bilateral Foreign Exchange Rates utilizing the sentiment from news articles and prominent macroeconomic indicators. Using a random forest regression, we were able to predict foreign exchange rates with an average error of 7.8%. In addition, news articles were an important feature in the majority of these random forest regressions. The novelty of our project relies on utilizing the Latent Dirichlet Allocation to cluster news articles into topics and further understand the semantic meanings behind each topic and applying it to foreign exchange rates.

Keyphrases: Random Forest Regressor, Regression, Statistical Arbitrage, foreign currency exchange rate, machine learning

@booklet{EasyChair:2415,
  author    = {S Sathish Kumar and N Thirumala Rao and Kama Srikanth},
  title     = {Regression models for Statistical Arbitrage: Predicting Currency Exchange Rates from News Media},
  howpublished = {EasyChair Preprint 2415},
  year      = {EasyChair, 2020}}