Abstract: Applying the sequence alignment method in bioinformatics to analyze financial time sequence can make it easy to capture large-scale features, remove noise and identify implicit pattern without having to rely too much on assumptions. This paper presents two methods of designing scoring matrix for financial sequence alignments, which are similarity-oriented matrix and purpose-oriented matrix. The former, focusing on the information of historical data, can be used to identify corresponding pattern. The latter, considering the purpose of alignments, can be used to extract featured segments. In the empirical analysis, similarity-oriented and purpose-oriented matrixes are constructed to study the characteristics of ups and downs of Shanghai Composite Index, Shenzhen Component Index and the relationship between them. The satisfactory results verify the feasibility and effectiveness of the methodology in financial research.

Key words: sequence alignment, similarity-oriented matrix, purpose-oriented matrix, Shanghai Composite Index, Shenzhen Component Index