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

Zhijie Zhang

No 127, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

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)

Pages: 19 pages
Date: 2022-07
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:toh:dssraa:127

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