Clustering is one of the most common methods of data analysis. Spectral Clustering is relatively new clustering method which has become popular in recent years. For the purpose of clustering the given objects (people, pixels, points...), we represented the data in a form of a weighted undirected graph where the nodes are the points in a feature space and the edges represent similarities between the points. The most important part of this clustering method is finding Laplacian matrix that is derived from the similarity matrix and weight matrix. The similarity matrix is obtained by the correlation function and the Gauss function. From these Laplacian matrices, native vectors are obtained. Finally, k-mean method gets clusters from the matrix of degrees and the matrix of the eigenvectors. The obtained function was first tested on simulated data to pre-know the result of the grouping and thus confirmed that the function is valid. After that, the function was applied to time series data which were 9 sectors revenue from the US market. Investments in such grouped sectors have resulted to be more profitable than the S&P500 index based on sector revenue and risk taken.