Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features

TL;DR

This paper applies Multiple Kernel Learning to combine financially motivated features for currency price movement prediction, demonstrating improved accuracy and interpretability over individual features.

Contribution

It introduces a set of financially motivated kernels for currency prediction and shows MKL's effectiveness in combining these features for better accuracy and feature importance insights.

Findings

MKL outperforms individual kernels in prediction accuracy

Kernel weights reveal the most informative financial features

The approach improves currency movement forecasting

Abstract

Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset's price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks.