Abstract
Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.
Highlights
With the development of computer technology, artificial intelligence, big data and cloud computing, an increasing number of people are relying on computer algorithms to address problems in all fields
We studied the correlation between stocks to provide helpful references for investors adjusting investment portfolios and proposed a composite similarity model that composited many different sequential features of stocks
The results show that the composite model could obtain more accurate clusters than many traditional similarity measures
Summary
People in different fields attempt to use artificial intelligent technology to make their work simpler, faster and more accurate, especially in finance [1]. Due to the digitization of financial transactions, large amounts of financial data with considerable implicit information are generated and stored. How to use these data to help people invest has become an issue of common concern for people majoring in both computer science and finance. Stock fluctuation is complicated and difficult to predict accurately, and most of the existing approaches can provide investors with only some efficient advice and cannot always provide returns. Identifying the correlation between different stocks still has meaning for investors in helping them make investment decisions
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