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Identify Arbitrage Using Machine Learning on Multi-stock Pair Trading Price Forecasting

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  • Zhijie Zhang

Abstract

Aims: Market neutral pair-trading strategy of two highly cointegrated stocks can be extended to a higher dimensional arbitrage algorithm. In this paper, a linear combination of multiple cointegratedstocks is introduced to overcome the limitations of a traditional one-to-one pair trading technique. Methods: First, stocks from diversified industries are pre-partitioned using clustering algorithm to break industrial boundaries. Then, cointegrated stocks will be formed using ElasticNet algorithm boosted by AdaBoost algorithm. Results: All three indicators on price prediction chosen for performance evaluation increased significantly. MSE increased by 32.21% compared to OLS, 37.06% increase on MAE, 37.73% improvement on MAPE. (Portfolio return performance is still under construction, indicators including cumulative return, draw-down and Sharpe-ratio. The comparison will be against against Buy-and-Hold strategy, a common benchmark for any portfolio)

Suggested Citation

  • Zhijie Zhang, 2022. "Identify Arbitrage Using Machine Learning on Multi-stock Pair Trading Price Forecasting," DSSR Discussion Papers 127, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:127
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    File URL: http://hdl.handle.net/10097/00135351
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