Searcharxiv⌕ Search

arXiv subjects

Somayeh Kokabisaghi

Publications and source records attributed to Somayeh Kokabisaghi.

2 recordsLinked to original sources

To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders

In this paper we extend the investigation into the transition from sure to probabilistic sniping as introduced in Menkveld and Zoican \cite{mz2017}. In that paper, the authors introduce a stylized version of a competitive game in which high frequency traders (HFTs) interact with each other and liquidity traders. The authors then show that risk aversion plays an important role in the transition from sure to mixed (or probabilistic) sniping. In this paper, we re-interpret and extend these conclusions in the context of repeated games and highlight some differences in results. In particular, we identify situations in which probabilistic sniping is genuinely profitable that are qualitatively different from the ones obtained in \cite{mz2017}. It turns out that beyond a specific risk aversion threshold the game resembles the well-known prisoner's dilemma, in that probabilistic sniping becomes a way to cooperate among the HFTs that leaves all the participants better off. In order to turn this into a viable strategy for the repeated game, we show how compliance can be monitored through the use of sequential statistical testing. Keywords: algorithmic trading, bandits, high-frequency exchange, Nash equilibrium, repeated games, sniping, subgame-perfect equilibrium, Sequential probability ratio, transition

q-fin.MF↗

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

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

q-fin.ST↗