Virtue or Mirage? Complexity in Exchange Rate Prediction

This paper investigates whether the “virtue of complexity” (VoC), documented in equity return prediction, extends to exchange rate forecasting. Using nonlinear Ridge regressions with Random Fourier Features (Ridge–RFF), we compare the predictive performance of complex models against linear regression and the robust random walk benchmark. Forecasts are constructed across three sets of economic fundamentals—traditional monetary, expanded monetary and non-monetary, and Taylor-rule predictors—with nominal complexity varied through rolling training windows of 12, 60, and 120 months. Our results offer a cautionary perspective. Complexity delivers only modest, localized gains: in very small samples with rich predictor sets, Ridge–RFF can outperform linear regression. Yet these improvements never translate into systematic gains over the random walk. As training windows expand, Ridge–RFF quickly loses ground, while linear regression increasingly dominates, at times even surpassing the random walk under expanded fundamentals. Market-timing analyses reinforce these findings: complexity-based strategies yield occasional short-sample gains but are unstable and prone to sharp drawdowns, whereas simpler linear and random walk strategies provide more robust and consistent economic value. By incorporating formal forecast evaluation tests—including Clark–West and Diebold–Mariano—we show that apparent gains from complexity are fragile and rarely statistically significant. Overall, our evidence points to a limited virtue of complexity in FX forecasting: complexity may help under narrowly defined conditions, but parsimony and the random walk benchmark remain more reliable across samples, predictor sets, and economic evaluations.