While hyperbolic embedding offers valuable insights into the structure of the stock market network, there are potential limitations and drawbacks that need to be considered. One limitation could be the complexity of interpreting the hyperbolic space and its impact on traditional financial analysis methods. Additionally, the computational resources required for hyperbolic embedding may be significant, especially for large datasets. To address these limitations, future research could focus on developing user-friendly tools and algorithms that simplify the interpretation of hyperbolic structures and optimize the computational efficiency of the embedding process. Moreover, conducting robust sensitivity analyses and validation studies can help ensure the reliability and accuracy of the hyperbolic embedding results.