Bias-Corrected Feature Selection for Short-Horizon FX Trading: Evidence from Liquid Currency Pairs

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis.