Interpretable deep learning LSTM model for intelligent economic decision-making

Abstract

For sustainable economic growth, information about economic activities and prospects is critical to decision-makers such as governments, central banks, and financial markets. However, accurate predictions have been challenging due to the complexity and uncertainty of financial and economic systems amid repeated changes in economic environments. This study provides two approaches for better economic prediction and decision-making. We present a deep learning model based on the long short-term memory (LSTM) network architecture to predict economic growth rates and crises by capturing sequential dependencies within the economic cycle. In addition, we provide an interpretable machine learning model that derives economic patterns of growth and crisis through efficient use of the eXplainable AI (XAI) framework. For major G20 countries from 1990 to 2019, our LSTM model outperformed other traditional predictive models, especially in emerging countries. Moreover, in our model, private debt in developed economies and government debt in emerging economies emerged as major factors that limit future economic growth. Regarding the economic impact of COVID-19, we found that sharply reduced interest rates and expansion of government debt increased the probability of a crisis in some emerging economies in the future.