Factor Augmented Artificial Neural Network vs Deep Learning for Forecasting Global Liquidity Dynamics

Abstract

This paper develops a global liquidity prediction model based on financial and macroeconomic information from different geographical areas. The methodology of the Factor Augmented Artificial Neural Network Model is applied to improve the predictive capacity of liquidity models compared to traditional econometric methodologies. This hybrid methodology based on dynamic factor models and neural networks is compared with Deep Learning methodologies such as Deep Recurrent Convolutional Neural Network and Deep Neural Decision Trees, which has recently shown great results. Our results show the superiority of the precision capacity of Factor Augmented Artificial Neural Network Model over the applied Deep Learning methodology, which demonstrates the importance of data treatment in International Macroeconomics and Finance with techniques from the Vector Autoregressive model. Our conclusions also show the importance of the impact of monetary policy, financial stability, and the real activity of the economy in the behavior of liquidity. This work may be useful for those interest groups in public and macroeconomic policy, showing the potential in the combination of conventional statistical methods with the envelope of Machine Learning techniques.