Market neutral statistical arbitrage strategy by factor models in Tehran stock exchange
Subject Areas : Financial Knowledge of Securities AnalysisFarimah Mokhatab Rafiei 1 , Kamyar Nourbakhsh 2
1 - Associate Professor of Tarbiat Modares university, faculty of industrial engineering and systems
2 - Master Student of financial engineering at Tarbiat Modares university, faculty of industrial engineering and systems
Keywords: Statistical Arbitrage, Market neutral, Ornstein-uhlenbeck, Mean reverting, principal component analysis, long position,
Abstract :
forecasting price movements is a challenging issue. So different statistical arbitrage strategies are devised to trade in exchanges. Some of these strategies are market neutral. Market neutral strategies are neutral to market movements and make profits in any situation. These strategies use long and short positions at the same time and this makes them unusable in exchanges like Tehran stock exchange that only long position is available. Purpose of this paper is devising a market neutral statistical arbitrage strategy which can be used in Tehran stock exchange. In devising this strategy we use principal component analysis to estimate market movements and calculate stocks idiosyncratic movements. To forecasting stocks idiosyncratic movements, which have mean reverting properties, we use Ornstein-uhlenbeck model. The strategy made 35% average annual return by considering transaction cost which is more than Tehran exchange index in the study period. results show this method is a good framework for devising statistical arbitrage strategies.
* Avellaneda, M. and Lee, J.H. (2010). Statistical arbitrage in the US equities market. Quantitative Finance, 10(7), pp.761-782.
* Bertram, W.K. (2010). Analytic solutions for optimal statistical arbitrage trading. Physica A: Statistical Mechanics and its Applications, 389(11), pp.2234-2243.
* Caldeira, J. and Moura, G.V. (2013). Selection of a portfolio of pairs based on cointegration: A statistical arbitrage strategy.
* Caporale, G.M., Gil-Alana, L. and Plastun, A. (2017). Searching for inefficiencies in exchange rate dynamics. Computational Economics, 49(3), pp.405-432.
* Cui, L., Huang, K. and Cai, H.J. (2015). Application of a TGARCH-wavelet neural network to arbitrage trading in the metal futures market in China. Quantitative Finance, 15(2), pp.371-384.
* de Moura, C.E., Pizzinga, A. and Zubelli, J. (2016). A pairs trading strategy based on linear state space models and the Kalman filter. Quantitative Finance, 16(10), pp.1559-1573.
* Drakos, S., (2016). Statistical Arbitrage in S&P500. Journal of Mathematical Finance, 6(01), p.166.
* Focardi, S.M., Fabozzi, F.J. and Mitov, I.K. (2016). A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance. Journal of Banking & Finance, 65, pp.134-155.
* Gatev, E., Goetzmann, W.N. and Rouwenhorst, K.G. (2006). Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), pp.797-827.
* Göncü, A. (2015). Statistical arbitrage in the Black–Scholes framework. Quantitative Finance, 15(9), pp.1489-1499.
* Hogan, S., Jarrow, R., Teo, M. and Warachka, M., (2004). Testing market efficiency using statistical arbitrage with applications to momentum and value strategies. Journal of Financial economics, 73(3): 525-565.
* Meucci, A. (2009). Review of statistical arbitrage, cointegration, and multivariate Ornstein-Uhlenbeck.
* Morgan, J. (2013). Hedge funds: Statistical arbitrage, high frequency trading and their consequences for the environment of businesses. critical perspectives on international business, 9(4), pp.377-397.
* Triantafyllopoulos, K. and Montana, G. (2011). Dynamic modeling of mean-reverting spreads for statistical arbitrage. Computational Management Science, 8(1), pp.23-49.
* Triantafyllopoulos, K. and Han, S. (2013). Detecting Mean-Reverted Patterns in Algorithmic Pairs Trading. In Mathematical Methodologies in Pattern Recognition and Machine Learning (pp. 127-147). Springer, New York, NY.
_||_
