Selecting Pairs Using Principal Component Analysis

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Pairs trading is a market-neutral strategy that involves identifying two correlated securities and taking positions based on their relative price movements. The concept behind pairs trading is to identify pairs of assets that historically exhibit a high degree of correlation, meaning they tend to move in tandem. However, when a temporary divergence occurs between the prices of the two assets, a pairs trader will take a long position in the underperforming asset and a short position in the outperforming asset, anticipating that the prices will converge again. This strategy seeks to profit from the reversion to the mean in the relationship between the two assets, regardless of the overall direction of the broader market.

Pairs selection is one of the most critical steps in pairs trading. The success of this trading strategy heavily relies on the careful identification of suitable pairs of assets that exhibit a high correlation and a historically stable relationship. The selection process involves analysis of historical price data, statistical measures such as cointegration or correlation coefficients, and fundamental factors that drive the performance of the assets.

Usually, stocks from the same industry are chosen for pairs trading. Reference [1] proposed a pairs selection method utilizing clusters identified by Principal Component Analysis (PCA). It pointed out,

We applied the unsupervised learning technique DB-SCAN algorithm for efficient pair selection which gives more number of pairs and better results than other algorithms like KNN algorithm.

We also used moving averages over 30 days rather than overall averages for more efficient prediction due to more relevant and recent results. We optimized our strategy at each step of pair trading computation to obtain overall optimized results which can be seen in our results.

The results show that our strategy is very effective against the standard Nifty-50 leading to good profits and pair selections. But, the model has slightly better performance in prediction spread and pairs than profitability. This is because profitability depends on various other market factors.

In short, PCA is an efficient method for identifying suitable pairs. Although it appears that the authors did not perform out-of-sample testing, we believe that their result has merits.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Vanshika Gupta, Vineet Kumar, Yuvraj Yuvraj, Manoj Kumar, Optimized Pair Trading Strategy using Unsupervised Machine Learning, 2023 IEEE 8th International Conference for Convergence in Technology (I2CT)

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