Adaboost-based Integration Framework Coupled Two-stage Feature Extraction with Deep Learning for Multivariate Exchange Rate Prediction

The foreign exchange market plays an important role in the financial field. Accurately predicting the exchange rate appears to be difficult on account of the characteristics of time variability and randomness. This study proposes an Adaboost-based reinforcement ensemble learning framework, which combines two-stage feature extraction with deep learning models to perform multivariate exchange rate prediction. Considering the impact of data information hidden in other financial markets on the foreign exchange market, multiple exogenous variables are introduced as input factors of the proposed model. Auto-encoder and Self-organizing map, as the main two-stage feature extraction models, have their advantages in simplifying model input and clustering similar feature data respectively. Feature extraction paves the way for the subsequent establishment of deep recurrent neural network (DRNN) for prediction, which improves the robustness of the model while improving the prediction accuracy. Finally, the Adaboost algorithm is utilized to integrate the DRNN prediction results. The empirical results reveal that the proposed model has higher accuracy in exchange rate prediction. The prediction effect of the model is significantly better than comparable models and it is a promising way of forecasting exchange rates.

Automated summary

This study introduces an Adaboost-based reinforcement ensemble learning framework for multivariate exchange rate prediction, combining two-stage feature extraction and deep learning models. By incorporating exogenous variables and using auto-encoder and self-organizing map for feature extraction, the model demonstrates improved accuracy and robustness in exchange rate prediction.