Market-neutral trading with fuzzy inference, a new method for the pairs trading strategy

Abstract

Pricing of financial instruments and stock market predictions is a specific and relatively narrow field, which has been mainly explored by mathematicians, economists and financial engineers. Prediction to make profits in a martingale domain is a hard task. Pairs trading, a market neutral arbitrage strategy, attempts to resolve the drawback of unpredictability and yield market independent returns using relative pricing idea. If two securities have similar characteristics, so should their prices. Deviation from the acceptable similarity range in price is considered an anomaly, and whenever noticed, trading is executed assuming the anomaly will correct itself.

This work proposes a fuzzy inference model for the market-neutral pairs trading strategy. Fuzzy logic lets mimicking human decision-making in a complex trading environment and taking advantage of arbitrage opportunities that the crisp models may miss to acquire for trade decision-making. Spread between two co-integrated stocks and volatility of the spread are used as decision-making inputs. The main focus of this study is the contribution of the fuzzy engine to the existing pairs trading strategies based on the spread measure. Widespread classical 'crisp' techniques are chosen and compared with the developed fuzzy' model. Significant enhancement on the performance of the trading strategies has been reported.