ESG driven pairs algorithm for sustainable trading: Analysis from the Indian market

Paper: arXiv 2401.14761

Abstract

This paper proposes an algorithmic trading framework integrating Environmental, Social, and Governance (ESG) ratings with a pairs trading strategy. It addresses the demand for socially responsible investment solutions by developing a unique algorithm blending ESG data with methods for identifying co-integrated stocks. This allows selecting profitable pairs adhering to ESG principles. Further, it incorporates technical indicators for optimal trade execution within this sustainability framework. Extensive back-testing provides evidence of the model’s effectiveness, consistently generating positive returns exceeding conventional pairs trading strategies, while upholding ESG principles. This paves the way for a transformative approach to algorithmic trading, offering insights for investors, policymakers, and academics.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses advanced statistical tests like Engle-Granger cointegration and ADF unit root tests (high math) while providing detailed backtesting results on real Indian market data with specific performance metrics (high rigor).

Research Flowchart

  flowchart TD
  A["Research Goal: Develop ESG-integrated<br>pairs trading algorithm for Indian equities"] --> B["Data Input: Indian Stock Data<br>+ ESG Ratings"]
  B --> C["Core Methodology: Identify<br>Cointegrated Stock Pairs"]
  C --> D["Algorithm Filter: Select Pairs<br>with High ESG Scores"]
  D --> E["Execution: Apply Technical Indicators<br>for Optimal Trade Timing"]
  E --> F["Back-testing & Analysis"]
  F --> G{"Key Findings & Outcomes"}
  G --> H["Outperforms Conventional<br>Pairs Trading Strategies"]
  G --> I["Generates Consistent<br>Positive Returns"]
  G --> J["Validates Sustainable<br>Investment Framework"]