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Abstract: Pairs trading is a popular trading strategy that tries to take advantage of market inefficiencies in order to obtain profit. The idea is simple: find two stocks that move together and take long/short positions when they diverge abnormally, hoping that the prices will converge in the future. From the academic point of view of weak market efficiency theory, pairs trading strategy shouldn't present positive performance since, according to it, the actual price of a stock reflects its past trading data, including historical prices. This leaves us with a question, does pairs trading strategy presents positive performance for the Brazilian market? The main objective of this research is to verify the performance and risk of pairs trading in the Brazilian financial market for different frequencies of the database, daily, weekly and monthly prices for the same time period. The main conclusion of this simulation is that pairs trading strategy was a profitable and market neutral strategy at the Brazilian Market. Such profitability was consistent over a region of the strategy's parameters. The best results were found for the highest frequency (daily), which is an intuitive result.
Pairs Trading, Quantitative Strategy, Market Efficiency
Abstract: Pairs trading is a popular trading strategy that tries to take advantage of market inefficiencies in order to obtain profit. Such approach, on its classical formulation, uses information of only two stocks (a stock and its pairs) in the formation of the trading signals. The objective of this paper is to suggest a multivariate version of pairs trading, which will try to create an artificial pair for a particular stock based on the information of m assets, instead of just one. The performance of three different versions of the multivariate approach was assessed for the Brazilian financial market using daily data from 2000 to 2006 for 57 assets. Considering realistic transaction costs, the analysis of performance was conducted with the calculation of raw and excessive returns, beta and alpha calculation, and the use of bootstrap methods for comparing performance indicators against portfolios build with random trading signals. The main conclusion of the paper is that the proposed version was able to beat the benchmark returns and random portfolios for the majority of the parameters. The performance is also found superior to the classic version of the strategy, Perlin (2006b). Another information derived from the research is that the proposed strategy picks up volatility from the data, that is, the annualized standard deviations of the returns are quite high. But, such event is paid by high positive returns at the long and short positions. This result is also supported by the positive annualized sharpe ratios presented by the strategy. Regarding systematic risk, the results showed that the proposed strategy does have a statistically significant beta, but it isn't high in value, meaning that the relationship between return and risk for the trading rules is still attractive.
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