Algorithmic pairs trading with expert inputs, a fuzzy statistical arbitrage framework

Abstract

Pairs trading is a widespread market-neutral trading strategy aiming to utilize the relationship between pairs of financial instruments in efficient markets, where predictability of separate asset movements is theoretically not possible. The implication of trading pairs, following statistical analysis, is to buy the underpriced asset while short selling the overpriced. The predicted price relationship is determined through analysis of historical spread data between the members of the corresponding pair. The investor expects the price difference, in an efficient market, should converge and stocks return to their 'fair value', where the positions are closed and profit is realized. The main focus of this study is the contribution of the fuzzy engine to the existing pairs trading strategy. Widespread classical 'crisp' technique is chosen, utilized and compared with the developed 'fuzzy' model throughout the paper. In order to further improve this contribution, the expert opinions extracted from the Bloomberg database are also integrated into the fuzzy decision-making process. In most studies, transaction costs are simply ignored. As a final robustness check, the transaction costs are also considered. The improvement reached by the developed fuzzy technique is observed to be even more remarkable in this case.