Last Updated on 22 April, 2023 by Samuelsson
In recent years, the volatility of financial markets has increased, leading to growing interest in non-directional and absolute return investment strategies. One such strategy is pairs trading, which involves exploiting the relationship between two stocks that are expected to move in tandem with each other. In this article, we will explore a research paper by Marc Schurer and Pavel Lisev titled “Pairs Trading Evaluation of profitability and risks on the Swedish stock market,” which investigates whether a pairs trading strategy based on the cointegration approach generates excess returns on the Swedish equity market.
The purpose of this research paper is to evaluate the profitability and risks of a pairs trading strategy based on the cointegration approach in the Swedish equity market. The paper employs a comprehensive analysis of the pairs trading strategy, which includes a long-term rolling window backtest, a scenario analysis of the Swedish stock market, an investigation of different in-sample pairs selection criteria, and an extended analysis of the strategy on the EUROSTOXX50 and DAX30 to support the robustness of the obtained outcomes.
The methodology used in this research paper involves implementing a long-term rolling window backtest applied on the OMX, a corresponding scenario analysis of the Swedish stock market including three different market environments, an investigation of different in-sample pairs selection criteria and their respective impact, and an extended analysis of the strategy on the EUROSTOXX50 and DAX30 to support the robustness of the obtained outcomes.
The empirical results suggest that the pairs trading technique is profitable and superior in terms of return and risk relative to its benchmarks. The long-term backtest confirms both of the predefined hypotheses, namely that the trading strategy generates excess returns and is exposed to less risk than the benchmark. Furthermore, changes in the selection criterion clearly have an impact on the strategy’s performance, but the empirical results remain inconclusive regarding the superiority of a specific ratio for all markets. The Sharpe-Ratio as a selection criterion shows the best overall performance with a historically achieved Sharpe-Ratio of 1.75.
The central conclusion following the main empirical findings is that the results of the pairs trading strategy can be considered significant and conclusive. Hence, if the trading algorithm can generate abnormal returns continuously, over a long period of time, statistical arbitrage can be used as a loss protection and portfolio diversification mechanism. The pairs trading technique shows profitable results and superior risk exposure across all markets. However, the results need to be interpreted with caution because all the results are based on continuously re-balancing the portfolio every day due to implementation purposes.
As a suggestion for future research, this framework could be used in a more realistic setting, employed by a quarterly or yearly re-balancing of the portfolio. Furthermore, this approach relies on the assumption that the cointegration relationship that is determined in-sample will persist during the whole trading period. As a result, there is a need for more adaptive trading rules taking into account the fact that the cointegration relationship may not be stable over time. Bootstrapping might also be considered a value-adding technique to be implemented in the future.
In conclusion, the pairs trading strategy based on the cointegration approach is a profitable and robust investment strategy in the Swedish equity market. The comprehensive analysis conducted in this research paper shows that the strategy generates excess returns and is exposed to less risk than its benchmarks. Furthermore, changes in the selection criterion have an impact on the strategy’s performance, but the Sharpe-Ratio as a selection criterion shows the best overall performance with a historically achieved Sharpe-Ratio of 1.75. Future research could be conducted to further refine the strategy and make it more adaptive to changes in the market.