Abstract
Pair trading is a widely used market neutral strategy and also a statistical arbitrage method that allows investors to take a position in two assets with similar trends in their historical data in order to gain low-risk profits. Combining both “diversification” and “pair trading”, this study proposes a statistical learning method to explore the most promising pair among multiple pair assets for each trading time. We incorporate estimated volatility into tolerance limits as a predictive function for finding buying and selling signals in order to capitalize on market inefficiencies. One-step-ahead volatility prediction follows either the exponentially weighted moving average (EWMA) method or the GARCH model. The study selects five artificial intelligence (AI) stocks in the U.S. equities market to target profitability through the proposed strategy with a rolling window training approach over two annual testing periods from April 2017 to March 2019. We recommend that conservative investors use p-content at 95%, which is less adventurous and can generate positive excess profits. The idea behind this strategy is to help investments be more diversified and also more profitable.
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