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Optimization of Ensemble Machine Learning Models for Forex Exchange Rate Forecasting Using Bayesian Hyperparameter Tuning.

Hakan Khan
Volume 5

Abstract

Foreign exchange rate forecasting is a highly complex financial prediction problem due to the nonlinear, nonstationary, and volatile behavior of currency markets, which are strongly influenced by macroeconomic indicators, interest rate differentials, geopolitical events, inflation dynamics, market sentiment, and global liquidity conditions. Conventional econometric models often struggle to capture these complex dependencies, while standalone machine learning models may suffer from limited robustness, overfitting, and sensitivity to manually selected hyperparameters. This paper proposes an optimized ensemble machine learning framework for accurate Forex exchange rate forecasting using Bayesian hyperparameter tuning. The proposed framework integrates multiple predictive learners, including Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, Support Vector Regression, and Extra Trees, within a unified ensemble architecture designed to improve forecasting stability and generalization across different currency pairs and market regimes. Historical Forex data for major currency pairs, such as EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD, and USD/CAD, were used together with technical indicators including moving averages, exponential moving averages, Bollinger Bands, relative strength index, MACD, stochastic oscillator, average true range, momentum indicators, and lagged return features. A comprehensive preprocessing pipeline was applied, including missing value treatment, outlier detection, normalization, stationarity-aware feature engineering, rolling-window construction, and chronological train-validation-test splitting to prevent look-ahead bias. Bayesian optimization was employed to automatically tune critical model parameters, such as tree depth, learning rate, number of estimators, regularization coefficients, subsampling ratios, kernel parameters, and ensemble weights, thereby reducing the computational cost associated with exhaustive grid search while improving convergence toward optimal model configurations. Experimental results demonstrate that the proposed Bayesian-optimized ensemble framework achieves superior forecasting performance compared with traditional models, including ARIMA, GARCH, standalone SVR, Random Forest, XGBoost, and LSTM baselines, across multiple forecasting horizons. The optimized ensemble records lower RMSE, MAE, and MAPE values, while achieving higher directional accuracy and stronger robustness under volatile market conditions. Furthermore, SHAP-based explainability analysis was incorporated to identify the most influential forecasting variables, revealing that lagged exchange rates, volatility indicators, moving average crossovers, momentum features, and interest rate-related variables play a dominant role in predicting future currency movements. The proposed framework provides a reliable, adaptive, and interpretable decision-support tool for Forex traders, financial analysts, portfolio managers, and algorithmic trading systems, offering improved forecasting accuracy and practical applicability in dynamic global currency markets.

Keywords

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