Clustering financial time series to generate a new method of factor neutralization: An empirical study

Abstract

In this paper, we consider the problem of clustering the long financial time series of the Chinese A-share market, applying [Formula: see text]-means, [Formula: see text]-Shape, agglomerative hierarchical clustering, affinity propagation, and Gaussian mixture to redivide the Chinese stock market. The results after parameter tuning show that the stocks in redivided industries are more similar than those of Shenwan first-class industry. Then we generate a new method of factor neutralization, using the new industries to neutralize factors, and then constructing the investment portfolios to test the four basic factors. The experimental results show that the investment portfolio based on [Formula: see text]-means can steadily defeat the benchmark and the portfolio based on classical industry classification. This new method of factor neutralization can bring a stable and effective improvement to the returns of the factors and it is allowed be applied to other factors, which has a significant impact on factor investing.