Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices

TL;DR

This study uses neural networks to analyze how oil price volatility affects Tehran's stock and industry indices during sanctions and post-sanction periods, revealing significant impacts and predictive insights.

Contribution

It introduces a neural network-based model to predict stock and industry indices considering oil price volatility in the context of sanctions, highlighting period-specific predictive performance.

Findings

Neural networks predict indices effectively during sanctions and post-sanction periods.

Oil price volatility significantly impacts stock and industry indices.

Post-sanction period shows better prediction accuracy for industry indices.

Abstract

In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward neural networks perform well in predicting stock market and industry, which means oil price volatility has a significant impact on stock and industry market indices. During post-sanction and global financial crisis, the model performs better in predicting industry index. Additionally, oil price-stock market index prediction performs better in the period of international sanctions. Herein, these results are, up to some extent, important for financial market analysts and policy makers to understand which factors and when influence the financial market, especially in an oil-dependent country such asIran with uncertainty in the international politics. Keywords: Feed-forward neural networks,Industry index,International energy sanction,Oil price volatility,Tehran stock index