In-Depth Exploration of Sector-Based Pairs Trading Strategies and Innovative Pair Selection Techniques

Background and Research Objectives

Pairs Trading Strategy (PTS) is a widely used financial arbitrage strategy that leverages the relative performance of two highly correlated stocks to profit from temporary price deviations. The core concept of traditional pairs trading relies on the mean reversion theory, assuming that the price spread between stocks will revert to its historical average. In practice, traders commonly select stock pairs through correlation analysis or cointegration analysis and generate long/short position signals using statistical models to achieve profits.

While traditional PTS has been widely applied, it has limitations. For instance, traditional PTS often ignores sector-specific factors in stock pair selection, which can lead to pairs that are sensitive to sector-wide fluctuations or systemic market risks, thus weakening overall investment performance. Additionally, as financial markets grow increasingly complex, researchers have recognized that traditional algorithms might struggle to adapt quickly to evolving market dynamics.

To address these limitations, Pranjala G. Kolapwar and her team proposed PTSR (PTS-Return-Based Pair Selection), an improvement on the traditional strategy, and a novel Sector-Based Pairs Trading Strategy (SBPTS). These innovations aim to enhance trading strategy returns and risk management capabilities through their innovative stock-pair selection methods.

Source of the Paper and Author Background

The research article, titled “Sector-Based Pairs Trading Strategy with Novel Pair Selection Technique,” was authored by Pranjala G. Kolapwar, Uday V. Kulkarni, and Jaishri M. Waghmare and published in the January 2025 issue of IEEE Transactions on Artificial Intelligence. The three authors hail from India’s Shri Guru Gobind Singhji Institute of Engineering and Technology. With extensive expertise in machine learning, deep learning, and financial trading algorithms, they focus on combining traditional financial strategies with modern intelligent algorithms, contributing innovative insights for optimizing investment strategies.

Research Workflow and Detailed Methodology

Improving Traditional PTS: The PTSR Method

a. Workflow Description

The PTSR method retains the core framework of traditional PTS but introduces significant improvements in stock-pair selection. The steps include:

1. Stock Pair Selection
   The authors use annual cumulative returns (Cumulative Annual Returns) rather than traditional statistical distances as criteria for stock-pair selection. The specific process is as follows:

   • Data Cleaning and Preprocessing: Input stock data (e.g., historical closing prices) is normalized using Min-Max Scaling and logarithmic difference transformation.
     
   • Daily Return Calculation: Daily returns are calculated using the formula ( ri = \frac{d”{ij} - d”{i-1,j}}{d”{i-1,j}} ).
     
   • Cumulative Return Calculation: Cumulative annual returns for each stock are accumulated using ( rc_y = (1+ri) \times rc{i-1} ).
     
   • Stock Pair Selection: The two stocks with the highest overall cumulative returns are chosen as the pair.

2. Spread and Z-Score Calculation

   • Spread Formula: ( si = d”{fpij} - d”_{fqij} )
     
   • Z-Score Formula: ( z_i = \frac{s_i - \mu}{\sigma} ), where (\mu) represents the historical mean spread and (\sigma) the standard deviation.

3. Defining Entry and Exit Conditions
   Entry and exit conditions for positions are determined based on Z-Score thresholds (e.g., entry at ±1.0, exit at ±0.5).

4. Cumulative Returns Analysis
   Strategy performance is evaluated using cumulative annual return formulas ( roc = \sum_y rc_y ).

5. Risk Management and Backtesting
   Historical data is used for backtesting to evaluate net profit/loss.

b. Experimental Results and Discoveries

Based on Sensex30 and Nasdaq stock data (2013-2023), the PTSR method identified the highest-returning pairs as (Bajfinance.NS, Titan.NS) and (NVDA, TSLA) respectively. Compared to the traditional PTS, PTSR demonstrated higher cumulative returns, proving its superiority.

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Innovative Strategy: Sector-Based Pairs Trading Strategy (SBPTS)

a. New Strategy Design and Steps

SBPTS focuses on sectoral groupings, attempting to select the best-performing stock pairs from the same industry, leveraging sector-specific patterns to reduce exposure to macroeconomic fluctuations. The steps are:

1. Sector Classification
   Using Alpha Vantage API to retrieve sector information, stocks are classified into respective sectors using a Support Vector Machine (SVM) classification algorithm. After classification, Sensex30 stocks were distributed across nine sectors (e.g., Energy, Materials), while Nasdaq stocks were categorized into five sectors (e.g., Technology, Healthcare).

2. Best Sector Selection
   The best-performing sector is identified using the following key metrics:

   • Annual Returns (ASR)
     
   • Sharpe Ratio (SR)
     
   • Beta Value (measuring volatility)
     
   • Price-to-Earnings Ratio (P/E)
     The authors use the Total Scoring Weighting Method (TSWM) to determine the best sector. Among Sensex30 sectors, “Materials” stood out, while for Nasdaq, “Technology” achieved the highest score.

3. Pair Selection and Trading Strategy
   SBPTS proposes two improvement paths:

   • SBPTS-Correlation, which uses correlation (Pearson Correlation) to select pairs;
     Selected pairs: (Grasim.NS, Shreecem.NS) and (AAPL, CRM).
     
   • SBPTS-Return-Based, which selects high-return pairs;
     Selected pairs: (JSWSteel.NS, Shreecem.NS) and (NVDA, MSFT).

4. Executing Trading Strategies
   Following PTS workflows, monitoring spreads, adjusting positions, and backtesting ensues.

b. Experimental Data Support

For Nasdaq data, SBPTS-R significantly outperformed PTS/PTS-R in cumulative returns, proving its stability and profitability.

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Conclusions and Significance

The proposed SBPTS strategy and its two variants (SBPTS-C and SBPTS-R) showcase the potential of combining sector insights with historical data to enhance stock-pair selection processes, substantially improving returns and risk management in pairs trading:

1. Academic Value

   • Introduces sector dimensions to the pairs trading domain innovatively.
     
   • Provides a systematic approach (TSWM) to evaluate sector performance, extending the boundaries of theoretical research and empirical applications.

2. Practical Value

   • Offers investors sector-specific trading insights and risk-avoidance strategies, especially during sector-specific volatility or macroeconomic turmoil.
     
   • SBPTS-R’s return-based flexibility shows strong adaptability to various market conditions.

3. Research Highlights

   • Focus on intra-sector stock correlations reduces systemic risk.
     
   • Introduced return-oriented evaluation metrics address traditional strategy limitations in handling non-stationary data.

4. Future Direction
   Further enhancing the strategy may involve incorporating cross-sector approaches to diversify portfolios and balance sector-specific risks.

Through rigorous analysis and extensive empirical data, the authors have proposed a novel, structured pairs trading method that offers significant references for both academia and practice.