List of Articles Statistical arbitrage

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  1 - Market neutral statistical arbitrage strategy by factor models in Tehran stock exchange
  Farimah Mokhatab Rafiei Kamyar Nourbakhsh
  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. More

  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. Manuscript profile
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  2 - The profitability of pairs trading strategy based on linear state-space models and the Kalman filter in Tehran Stock Exchange
  Mohammad mehdi barahimipour sayyed mohammad reza davoodi
  Statistical arbitrage as one of the subsets of algorithmic trading refers to strategies that employ some statistical model or method to take advantage of what appears to be mispricing between assets while maintaining a level of market neutrality. One of these strategies More

  Statistical arbitrage as one of the subsets of algorithmic trading refers to strategies that employ some statistical model or method to take advantage of what appears to be mispricing between assets while maintaining a level of market neutrality. One of these strategies is pair trading that implements on two related long-term(co-integration) financial assets. The pair trading strategy of the research is based on the description of the visible process, the remainder of the co-integration model in terms of an invisible mean reverting process. This representation is in a state-space model and solved by the Kalman filter approach and the time of buying and selling is calculated in terms of two probabilities of growth and fall. The profitability of pair trading strategy on 21 stocks from oil product index and basic metal index of Tehran Stock Exchange between 1390-1395 was evaluated according to return and Sharp ratio. The results of the research show that the research method has the daily returns of 0.0048 and Sharp 1.23, which is more profitable in comparison with the pair trading based cointegration and market performance but the average daily its return is in the second place after the co-integration method. Manuscript profile
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  3 - Statistical Arbitrage Strategy Based on Factor Models of Prices in Iran's stock exchange market
  Farimah Mokhatab Rafiei Kamyar Nourbakhsh
  Statistical Arbitrage Strategies are looking for profitable opportunities using statistical methods. In this paper, we use a new approach to devise a statistical arbitrage strategy in Iran’s stock exchange market. In this new approach, instead of model and forecas More

  Statistical Arbitrage Strategies are looking for profitable opportunities using statistical methods. In this paper, we use a new approach to devise a statistical arbitrage strategy in Iran’s stock exchange market. In this new approach, instead of model and forecast stock prices independently, we take them as a whole and extract their common movement patterns, which can represent the general market movements, with principal component analysis. After that, we model and forecast these patterns (factors) and through them, we forecast the stock returns. Ultimately, we construct portfolios from chosen stocks in each period. Empirical result of this paper show the profitability of these strategies. chosen strategy, with time window of 100 days and forecasting horizon of 1 day could made the average annual return of 115%, without considering the transaction costs. Manuscript profile
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  4 - Pairs trading based on wavelet decomposition
  bahareh zarintaj saeed aghasi forozan baktash
  In the current research,wavelet analysis is used to analyze the time series of prices in a pair of assets into general and detailed time series, and the property of collocation between different and corresponding levels of analysis of two series is checked in order to f More

  In the current research,wavelet analysis is used to analyze the time series of prices in a pair of assets into general and detailed time series, and the property of collocation between different and corresponding levels of analysis of two series is checked in order to find collinear pairs at different levels of analysis. And then its profitability is examined. In this research, the profitability of the pair trading system based on wavelet analysis was investigated on 14 indices of the Tehran Stock Exchange betwee 2013-2022. The results show that for the second level of detail in the wavelet analysis, the results are quite impressive and the number of trading positions is more than doubled, the daily return is increased to four times and the Sharpe ratio is also increased to about two times. The system formed based on the first level of detail also has a better profitable performance than the normal aggregation, and the performance of the third level of detail is within the limits of aggregation. In addition, the average duration of the transaction also shows significant decrease in the first and second levels. Profitability performance at the level of general series is generally weaker than the aggregate. Manuscript profile
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  5 - Optimal Pairs Trading strategy under Statistical Variability of the Spread Process
  Fatemeh Azizzadeh Nasrin Ebadi
  The appropriate investment and decision making about taking of correct long and short position needs proved strategies. In this research, pairs trading have been studied and a new non-parametric approach proposed based on Renko and Kagi construction which are two Japane More

  The appropriate investment and decision making about taking of correct long and short position needs proved strategies. In this research, pairs trading have been studied and a new non-parametric approach proposed based on Renko and Kagi construction which are two Japanese charting indicators. The proposed approach exploits information about the variability of spread process and a constant long-run mean dose not find for spread process but trade towards it like other methods of pairs trading and the only needed assumption is remaining constant of statistical properties of the spread process volatility. In this research, profitability of proposed method have been proved theoretically mean-reverting process with stochastic volatility , then pairs trading  have been performed based on this approach on selective data of Tehran stock exchange . The results of implementation show that used strategy obtain 52.91% per return in stock pair of KHTRAC and KHTOGHA, 33.645 per return in stock pair of KHMOHAREKEH and KHODRO for appropriate selection of H. Manuscript profile
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  6 - Investigate the Operation of Random forest and Deep neural networks on Statistical Arbitrage Strategy
  alireza Fazlzadeh Jafar Haghigha Faranak Pourkeivan vahid ahmadian
   In this research, the statistical analysis of random forest effects has been done. Also, to evaluate the performance of the random forest algorithm in the field of statistical arbitrage compared to other models presented in the previous research, the comparison of More

   In this research, the statistical analysis of random forest effects has been done. Also, to evaluate the performance of the random forest algorithm in the field of statistical arbitrage compared to other models presented in the previous research, the comparison of the results from the application of this algorithm with deep neural network algorithm has been done. The models are taught with stock price information and the output from this technique categorizes stocks according to the position of buying and selling. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model. Manuscript profile