  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
