One of my more arcane working papers recently hit a few top-ten lists on SSRN last week with a whopping 10 downloads.  The paper is focused on improving one of the key signals in my Quantitative Finance, A Profitable Trading and Risk Management Strategy Despite Transaction Cost.  You can get it here or read the abstract below:

We present an adjusted method for calculating the eigenvalues of a time-dependent return correlation matrix that produces a more stationary distribution of eigenvalues. First, we compare the normalized maximum eigenvalue time series of the market-adjusted return correlation matrix to that of logarithmic return correlation matrix on an 18-year dataset of 310 S&P 500-listed stocks for two (small and large) window or memory sizes. We observe that the resulting new eigenvalue time series is more stationary than time series obtained through the use of existing method for each memory. Later, we perform this analysis while sweeping the window size τ ε {5, ..., 100} in order to examine the dependence on the choice of window size. 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 the classic method.

Bommarito, Michael James and Duran, Ahmet, Spectral Analysis of Time-Dependent Market-Adjusted Return Correlation Matrix (May 26, 2010). Available at SSRN:

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