Eigenvalues based on covariance matrix chunking in stock risk assessment

In the field of financial investment, accurate assessment of stock risk is crucial to the construction of a sound investment portfolio. In this paper, we divide the stock market into segments using covariance matrix blocking and use the eigenvalues of the matrix after blocking to assess the stock risk. Firstly, the role of the covariance matrix in stock risk assessment is introduced. Then the blocking technique is used to classify stocks with similar risk characteristics into the same segment. Through the eigenvalues, this paper identifies the stock segments that contribute the most to the overall market risk, and then the risk exposure of the stocks within the segments is discussed in depth. The study shows that the eigenvalue distribution of the segmentation matrix can reveal the risk structure of the stocks in the segment, which provides a new perspective for investors to understand and manage the stock risk. Finally, this paper empirically verifies the validity of stock risk assessment based on the eigenvalues of the block matrix and explores the potential of its application in investment decision-making.