In this paper, we model the impact of oil price volatility on Tehran stock 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

Additional Metadata
Keywords Feed-forward networks, Industry index, International energy sanction, Oil price volatility
Journal Computational Economics
Citation
Kokabisaghi, K, Ezazi, M, Tehrani, R, & Yaghoubi, N. (2019). Sanction or financial crisis? An artificial neural network-based approach to model the impact of oil price volatility on stock and industry indices - Under review. Computational Economics.