Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set

Autor:	John W. Goodell, Haoran Li, Yuejiao Duan, Xinming Li
Rok vydání:	2022
Předmět:	Information set business.industry Monetary policy Machine learning computer.software_genre Quantile regression Economic risk Financial information Systemic risk Economics Artificial intelligence Set (psychology) Construct (philosophy) business computer Finance
Zdroj:	Finance Research Letters. 46:102273
ISSN:	1544-6123
DOI:	10.1016/j.frl.2021.102273
Popis:	While data sets used for forecasting can now be greatly improved, expanding data and information size also exposes weaknesses in traditional forecast models. We assess machine learning methods for forecasting monetary policy actions and concomitant macroeconomic risks. We construct an expanded information set on Chinese systemic risk, confirming that this set contains additional information useful for macroeconomic forecasting. We find that machine learning processes offer significant improvement for macroeconomic forecasting, with quantile regression forest exhibiting superior out-of-sample prediction accuracy compared with traditional methodologies. These findings will be of great interest to policy makers and investors.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ca36407982993a325bbec5947a8966ed https://doi.org/10.1016/j.frl.2021.102273 Zobrazit plný text záznamu Full Text from ScienceDirect