Utilizing short-term noise in non-efficient markets by paired assets : -Introducing the Technical AMH trader

In this paper, we present an algorithmic implementation of a pairs trading strategy on the OMXS during the years from 2010 until 2018. In our case, the trading algorithm is based on the Adaptive Market Hypothesis (AMH) theory. Hence, the algorithm scans the market for temporary inefficient behaviour, as defined by AMH. The pairs trading algorithm suggested in the paper revolves around a minimum distance paring method that evaluates different threshold measures. In the paper, we also suggest a new method for the choice of threshold as well as evaluate the efficiency of the strategy by an application on the relatively small Swedish OMXS stock exchange. In this small exchange, other forms of market inefficiency may arise compared with those on a larger international stock exchange. The paper also presents the AMH framework for behaviour in a financial market, a hypothesis that may explain the positive result of the presented application of pairs trading strategy. Hence, this paper connects AMH with the pairs trading strategy. Our result indicates that when we exercise the strategy during a period of trading, in a portfolio consisting of 10 pairs, it generates a positive aggregated return, compared with index, even when we consider a theoretical transaction cost. Furthermore, the strategy outperforms a random naïve trading strategy combined with a low market dependence, measured in correlation. However, even if the strategy is profitable, the return is volatile. This is believed to be an outcome of volatile market reactions to new information in the close chosen substitutes. Such problems with pairing and diversity may be traced back to the small sample of stocks in the example presented in the paper. As such, the algorithm suggested is as a step towards trading based on theoretically motivated algorithmic learning models, in line with trading models developed within the rapidly emerging field of artificial intelligence