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

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