**Authors:** Simão Moraes Sarmento, Nuno Horta **Published:** _Expert Systems with Applications_, 2020 **Link:** [DOI](https://doi.org/10.1016/j.eswa.2020.113490) --- ### Summary This study enhances the traditional **Pairs Trading** strategy by integrating **Machine Learning** techniques to: 1. **Find Profitable Pairs Efficiently** 2. **Avoid Long Decline Periods** caused by divergent price movements --- ### Key Points - **Pairs Trading:** A market-neutral strategy where an investor buys an undervalued security and sells an overvalued one, profiting when prices converge. - **Problem 1 - Finding Pairs Efficiently:** - Traditional methods are exhaustive or sector-limited, which can miss opportunities. - Solution: Apply **PCA (Principal Component Analysis)** for dimensionality reduction followed by **OPTICS Clustering** to identify high-potential pairs with reduced noise. - Results: This approach achieved an average **Sharpe Ratio of 3.79**, outperforming traditional methods. - **Problem 2 - Reducing Decline Periods:** - Divergent price movements can lead to long drawdowns. - Solution: Use **time-series forecasting** models like **ARMA**, **LSTM**, and **LSTM Encoder-Decoder** to predict spread movements and adjust trades dynamically. - Results: Reduced portfolio decline periods by **75%**, although profitability decreased slightly. - **ETFs as the Asset Class:** The study focuses on 208 commodity-linked ETFs, trading at 5-minute intervals, which showed strong mean-reversion characteristics suitable for Pairs Trading. --- ### How It Works 1. **PCA + OPTICS Clustering:** Identifies robust, co-integrated pairs with minimal noise. 2. **Forecasting Models:** Predicts spread changes to optimize entry and exit points. 3. **Trading Simulation:** Uses real 5-minute data from 2009 to 2018 to validate models and strategies. --- ### Strengths - Enhanced pair selection with clustering...less noise, better matches. - Machine learning forecasting reduces bad trades and decline periods. - Market-neutral approach suitable for volatile environments. --- ### Limitations - Slightly lower profitability when optimizing for reduced drawdowns. - Performance drops with fewer available pairs during longer formation periods. - Needs more exploration with different asset classes like stocks or futures.