Abstract. Nowadays, it is not necessary for humans to conduct trade; this task is performed by trading algorithms. The speed of trading is of the most importance, however, there are relatively few aoutademic researches on the increased speed of trading from milliseconds to nanoseconds. In order to address the aforementioned shortcoming, this research measures the differences in the effectiveness of the pairs trading strategies, emerging when microsecond and nanosecond data are included. The effect of the increased speed of data is analysed. We present different pairs trading strategies and one pair selection algorithm, based on the cointegration method. These trading strategies are implemented on five different commodity futures contracts using both microsecond and nanosecond historical data. The effectiveness is measured in accordance with the profit, generated at the end of the trading period. In order to measure the effectiveness of all presented pairs trading strategies, the Sharpe Ratio method was introduced.
Abstract. Statistical arbitrage is a popular trading strategy where a profit arises from pricing inefficiencies between securities. The idea is simple: to find two stocks that move together and take long/short positions when they diverge abnormally, hoping that the prices will converge in the future. In the previous researches, the most popular statistical arbitrage strategies were tested using high frequency gas future market data. The best performance was shown with pairs trading strategy proposed by J. Caldeira and G. V. Moura. In this paper the database for testing covers 14 OMX Baltic stocks for 6 months between 2014-10-01 and 2015-03-31. During the trading period there were no predefined pairs, thus it was necessary to incorporate a pair selection algorithm in order to find best pairs for each trading period. The contribution of this paper is to test pairs trading strategy proposed by J. Caldeira and G. V. Moura with OMX Baltic stocks and to incorporate a trading pair selection algorithm.
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