Theoretical research suggests potential cross-currency predictability in currency exchange markets, but empirical findings with macro-based models show mixed results. This has led to a growing focus on micro-based models which explore how trading integrates fundamental information into exchange rates. This study stands out by using detailed limit order book data for the foreign exchange market, enabling micro-level insights. It covers multiple currency pairs, assessing cross-currency predictability. Emphasizing short-term forecasts from one minute to one hour, it introduces factor-augmented regressions, including unsupervised and supervised principal components analysis to tackle data dimensionality, and contrasts those to LASSO and random forest methods. Our findings reveal generally low predictability across various models, supporting the efficient market hypothesis. We find that cross-currency variables offer limited additional insight, but certain microstructure variables like order flow show short-term predictive power, suggesting transient market inefficiencies.
Petrova et al. (Thu,) studied this question.