Since the small combined stock market has less noise information, a random matrix denoising method amended by Monte Carlo simulation was proposed. Firstly, random matrix was generated by simulation; secondly, the lower and upper bounds of the noise were corrected simultaneously by using a large number of simulated data; finally, the range of noise was determined precisely. The Dow Jones China 88 Index and the Hang Seng 50 Index were used for empirical analysis. The simulation results show that, compared with LCPB (Laloux-Cizeau-Potters-Bouchaud), PG+(Plerou-Gopikrishnan) and KR (RMT denoising method based on correlation matrix eigenvector's Krzanowski stability), rationality and validity of the noise range corrected by Monte Carlo simulation denoising method are greatly improved in eigenvalue, eigenvector and inverse participation ratio. Investment portfolio of the correlation matrix before and after denoising was given, and the results indicate that the Monte Carlo simulation denoising method has the smallest value at risk under the same expected rate of return, which can provide a certain reference for the portfolio selection, risk management and other financial applications.