Statistical arbitrage is a classic quantitative trading strategy. Its core premise relies on the assumption that two similar stocks should move broadly in tandem and, when their prices diverge, are expected to converge over time. To identify similar underlyings, statistical methods such as cointegration and correlation are commonly employed.
It is widely recognized by practitioners that similarity between two stocks is not purely statistical but also fundamental. However, relatively little research has focused on systematically incorporating fundamental similarity into pair selection. Reference [1] addresses this gap by developing a pairs trading selection framework that integrates fundamental characteristics alongside statistical measures.
To identify fundamentally similar pairs, the author utilizes variables such as return on equity, differences in log sales growth over the past five years, differences in the most recently available financial leverage ratios, geographic proximity (e.g., whether firms are headquartered in the same U.S. state), and industry alignment. Each fundamental metric is then assigned a score, and a composite indicator is constructed for pair selection.
The author pointed out,
This paper introduces a novel approach to stock pair selection in pairs trading, using a multi-factor scoring model based on significant variables identified through panel regression. These variables are weighted by their regression coefficients to construct a composite score. I benchmark this inter-pair characteristic-based method against the Gatev et al. (2006) SSD approach, analyzing both extended and updated trading strategies to account for repeated pair selections. The strategy’s robustness is tested with and without the COVID-19 crisis, and all results are reported net of transaction costs.
The key finding is that the multi-factor method consistently outperforms the benchmark with higher risk-adjusted returns. This performance edge persists beyond the COVID-19 period, with a narrower gap, highlighting the scoring model’s advantage in stressful markets. Though differences between updating and extending pairs are modest, the updated strategy delivers superior returns in volatile markets despite higher transaction costs. Nonetheless, the strategy underperforms relative to passive index investing. Even with rolling estimation windows, out-of-sample performance remains challenging.
In short, this paper introduces a novel stock pair selection framework that incorporates fundamental metrics. The results show that the proposed method delivers higher risk-adjusted returns than the benchmark, particularly in volatile markets, although it underperforms passive index investing and faces challenges in out-of-sample performance.
This article represents a meaningful step toward refining statistical arbitrage by incorporating fundamental characteristics into the pair selection process.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Lukas Reichmann, Pairs trading- Selection via scoring systems, Finance Research Letters Volume 93, March 2026, 109642
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