Stock Selection System Through Suitability Index and Fuzzy-Based Quantitative Characteristics

With the rapid development of quantitative trading, stock selection is an ongoing task that requires consideration of the characteristics of stocks and investment strategies. Fuzzy set theory is excellent for modeling and describing abstract characteristics to establish the link between stocks and strategies. In this article, we present our idea for a stock selection system called TripleS. It is based on the suitability index (SI) derived from fuzzy-set theory. The TripleS relies on the position size to extract the stock characteristics SI that describes not only the characteristics of each stock but also the extent to which they are suitable for certain strategies. Then, the SI undergoes a transformation process in the hands of the developed fuzzy-set modules for stock selection and investment. There are some other proposed methods for stock selection respectively called TripleS-T1, TripleS-T2, TripleS-T2U, TripleS-EF, and TripleS-EFT. Experimental results show that the proposed TripleS has minor improvement in precision but significant improvement in investment performance. The TripleS-EFT has outstanding profitability, the highest annual return in all strategies, and the highest Sharpe ratio in two of the four strategies, and achieves an annual return of 10.01% and a Sharpe ratio of 1.032, which significantly outperform the benchmark system. The TripleS-EFT also significantly outperforms the state-of-the-art fuzzy-based system in measuring annual return and even has double the annual return in several strategies.

Automated summary

This article introduces a stock selection system called TripleS, which is based on fuzzy set theory. It utilizes the position size to extract the suitability index (SI) that describes the characteristics of stocks and their suitability for certain strategies. Experimental results show that TripleS has minor improvement in precision but significant improvement in investment performance. TripleS-EFT, one of the proposed methods, achieves outstanding profitability and outperforms the benchmark system and state-of-the-art fuzzy-based system in terms of annual return and Sharpe ratio.