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Abstract We present an adjusted method for calculating the eigenvalues of a time-dependent return correlation matrix in a moving window. First, we compare the normalized maximum eigenvalue time series of the market-adjusted return correlation matrix to that of the logarithmic return correlation matrix on an 18-year dataset of 310 S&P 500-listed stocks for small and large window or memory sizes. We observe that the resulting new eigenvalue time series is more stationary than the time series obtained without the adjustment. Second, we perform this analysis while sweeping the window size τ ∈ { 5 , ... , 100 } ∪ { 500 } in order to examine the dependence on the choice of window size. This approach demonstrates the multi-modality of the eigenvalue distributions. We find that the three dimensional distribution of the eigenvalue time series for our market-adjusted return is significantly more stationary than that produced by classic method. Finally, our model offers an approximate polarization domain characterized by a smooth L-shaped strip. The polarization with large amplitude is revealed, while there is persistence in agreement of individual stock returns' movement with market with small amplitude most of the time. Highlights • We present an adjusted method for the eigenvalues of a time-dependent correlation matrix. • We use the correlation matrix on an 18-year data set of 310 S&P 500-listed stocks. • We obtain more stationary eigenvalue time series via market-adjusted return. • Co-movement and polarization of financial variables are important. • We find an approximate polarization domain, characterized by a smooth L-shaped strip. [ABSTRACT FROM AUTHOR]