  Fast, global, and sensitively reacting to political, economic and social
events of any kind, these are attributes that social media like Twitter share
with foreign exchange markets. The leading assumption of this paper is that
information which can be distilled from public debates on Twitter has
predictive content for exchange rate movements. This assumption prompted a
Twitter-based exchange rate model that harnesses regARIMA analyses for
short-term out-of-sample ex post forecasts of the daily closing prices of
EUR/USD spot exchange rates. The analyses used Tweet counts collected from
January 1, 2012 - September 27, 2013. To identify concepts mentioned on Twitter
with a predictive potential the analysis followed a 2-step selection. Firstly,
a heuristic qualitative analysis assembled a long list of 594 concepts, e.g.,
Merkel, Greece, Cyprus, crisis, chaos, growth, unemployment expected to covary
with the ups and downs of the EUR/USD exchange rate. Secondly, cross-validation
using window averaging with a fixed-sized rolling origin was deployed to select
concepts and corresponding univariate time series that had error scores below
chance level as defined by the random walk model. With regard to a short list
of 17 concepts (covariates), in particular SP (Standard & Poor's) and risk, the
out-of-sample predictive accuracy of the Twitter-based regARIMA model was found
to be repeatedly better than that obtained from both the random walk model and
a random noise covariate in 1-step ahead forecasts of the EUR/USD exchange
rate. This advantage was evident on the level of forecast error metrics (MSFE,
MAE) when a majority vote over different estimation windows was conducted. The
results challenge the semi-strong form of the efficient market hypothesis
(Fama, 1970, 1991) which when applied to the FX market maintains that all
publicly available information is already integrated into exchange rates.
