A Time-frequency search for stock market anomalies
We carry out a time-frequency analysis of daily values of the Dow Jones Industrial Average and the S&P 500 Index of stock prices based on 62 years of data. The algorithm used is based on pruning dyadic trees to obtain optimal stationary segmentations of nonstationary time series. The resulting time-frequency representations yield insights into the frequency-domain evolution of the US stock market. Details concerning computational speed and recombination are briefly addressed.
KeywordsDepression Recombination Autocorrelation Hunt Volatility
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