Feature importance recap and stacking models for forex price prediction

TL;DR

This paper introduces a novel feature selection method called 'feature importance recap' and employs stacking models to enhance forex price prediction accuracy using deep learning techniques.

Contribution

It proposes a new feature selection approach combining tree-based importance scores with deep learning performance, and develops a stacking model to improve prediction accuracy.

Findings

Proper feature selection significantly improves model performance.

Some features consistently have high importance scores across models.

Stacking models further enhance prediction accuracy.

Abstract

Forex trading is the largest market in terms of qutantitative trading. Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade. With the development of artificial intelligent, deep learning plays a more and more important role in forex forecasting. How to use deep learning models to predict future price is the primary purpose of most researchers. Such prediction not only helps investors and traders make decisions, but also can be used for auto-trading system. In this article, we have proposed a novel approach of feature selection called 'feature importance recap' which combines the feature importance score from tree-based model with the performance of deep learning model. A stacking model is also developed to further improve the performance. Our results shows that proper feature selection approach could significantly improve the model performance, and for financial data, some features have high importance score in many models. The results of stacking model indicate that combining the predictions of some models and feed into a neural network can further improve the performance.