摘要(英) |
In recent years, numerous researchers have proposed various investment portfolio strategies
in the field of stock market investing. To enhance portfolio returns, researchers have shown
a keen interest in understanding the interrelationships between stocks. In this paper, we utilize the DTW (Dynamic Time Warping) based on t-SNE (t-Distributed Stochastic Neighbor
Embedding) algorithm, along with hierarchical clustering, to visualize and cluster multidimensional stock market information. We extract multiple dimensions of data as features
and present the state changes of each stock across different time periods using a heat map.
The main objective of this research is to develop the “Stock State Visualization Heat Map”,
which visualizes the potential correlations among different stocks and identifies stable or
emerging clusters of stocks exhibiting similar state changes. Furthermore, by analyzing the
changes in stock states, we aim to gain insights into the flow of funds within the market. |
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