Fuzzy system with customized subset selection for financial trading applications

Abstract

In the financial industry, identifying useful information from big data becomes a key research topic. Since the vast number of technical indicators can be captured nowadays, the indicator selection can be used to support investment decision for different financial products concurrently; however, this process is still required experience from investors. In this article, we propose a novel recommendation system which is incorporated with technical indicators. The method of fuzzy subset selection is used to feature relevant indicators which have more impact to the variation in the transaction history. The proposed method enables automatic customization of indicators for different financial products in different markets. In particular, the least absolute distance fuzzy regression with non-symmetric lower and upper bounds is proposed to avoid extreme values in dominating the model. Furthermore, to reduce computational complexity in the subset selection, the selection algorithm operates in the frequency domain for identifying and matching key patterns and peaks in transacted volumes with the technical indicators. This method performs very effective although the number of factors is much greater than the sample size. The proposed method can benefit participants in the finance markets to customize their own trading dashboard as well as set up their own trading strategies.