In this paper I investigate properties of square complex matrices of the form Ak, where A is also a complex matrix, and k is a nonnegative integer. I look at several ways of representing Ak. In particular, I present an identity expressing the kth power of the Schur form T of A in terms of the elements of T, which can be used together with the Schur decomposition to provide an expression of Ak. I also explain bounds on the norm of Ak, including some based on the element-based expression of Tk. Finally, I provide a detailed exposition of the most current form of the Kreiss Matrix Theorem.

我們從計算股價的相關矩陣，然後利用隨機矩陣定理的結果，了解到股票市場並非符合隨機過程的預測，進而得知股票對股票之間具有關聯性，然其長時距下股票價格對數報酬的變化會呈現隨機行走的模式，因此我們對其結果提出二種不同的耦合隨機行走模型，試圖闡釋股票市場間的關聯性可融合到耦合隨機行走模型之中，並藉由均方對數報酬(mean square log-return，MSLR)來探討此事情。 最後，為了瞭解關聯性的關係，並利用其來了解股票市場內部結構的特性，因此我們利用股價的相關矩陣來建構最小展開樹進行分析，發現當時間尺度越大其圖形越密集，中心幾乎為「GE」這家公司，因此其股票市場具有一定的判斷指標。 / By means of calculating the correlation matrix of the price of stock and using the results of random matrix theorems，we learned that the stock market does not match the prediction of stochastic processes and the stock-stock is correlated。However，stock’s price log-return changes under long time scale will appear random walk model. Therefore，we propose two kinds of the different coupled random walk model，that try to explain the correlation between the stock markets can be integrated into the coupled random walk model，and using the mean square log-return( MSLR) to investigate this issue。 Finally，to understand the relationship of correlation matrix and by using it to know the characteristics of the underlying structure of the stock market，we use the correlation matrix of the price to construct the minimum spanning tree for analysis。The results showed that when the time scale is greater, the graphics are more intensive，and the center is almost the same company，"GE"， indicating that the stock market has a certain judgment index。