---
title: Forecasting GCC Financial Stress with Neural Networks
url: https://www.ml-quant.com/papers/repec/kap-apfinm-v-30-y-2023-i-3-d-10-1007-s10690-022-09387-3/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:kap:apfinm:v:30:y:2023:i:3:d:10.1007_s10690-022-09387-3
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10690-022-09387-3%3Bh%3Drepec%3Akap%3Aapfinm%3Av%3A30%3Ay%3A2023%3Ai%3A3%3Ad%3A10.1007_s10690-022-09387-3
featured: 2023-08-17
citations: unknown
topic: Econometrics & Forecasting
---


# Forecasting GCC Financial Stress with Neural Networks

The research uses a One-Dimensional Convolutional Neural Network to predict financial stress in the GCC oil, stock, and bond markets, and finds that financial stress indices and oil significantly improve forecasting performance and risk hedging.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10690-022-09387-3%3Bh%3Drepec%3Akap%3Aapfinm%3Av%3A30%3Ay%3A2023%3Ai%3A3%3Ad%3A10.1007_s10690-022-09387-3
- Identifier: RePEc:kap:apfinm:v:30:y:2023:i:3:d:10.1007_s10690-022-09387-3
- Released: 2023-08-17
- First featured: Quant Letter No. 12 (2023-08-17): https://www.ml-quant.com/issues/2023-08-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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