Financial market in oil-dependent countries has been always influenced by any changes in international energy market, In particular, oil price. It is therefore of considerable interest to investigate the impact of oil price on financial markets. The aim of this paper is to model the impact of oil price volatility on stock and industry indices by considering gas and gold price, exchange rate and trading volume as explanatory variables. We also propose Feed-forward networks as an accurate method to model non-linearity. We use data from 2009 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward networks perform well in predicting variables and oil price volatility has a significant impact on stock and industry market indices. The result is more robust in the post-sanction period and global financial crisis in 2014. Herein, it is important for financial market analysts and policy makers to note which factors and when influence the financial market, especially in an oil-dependent country such as Iran with uncertainty in the international politics. This research analyses the results in two different periods, which is important in the terms of oil price shock and international energy sanction. Also, using neural networks in methodology gives more accurate and reliable results.

Additional Metadata
Keywords Feed-forward networks, Industry index, International energy sanction, Oil price volatility
Journal Computational Economics
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.