Multiple day biclustering of high-frequency financial time series

With recent technological advances, high-frequency transaction-by-transaction data are widely available to investors and researchers. To explore the microstructure of variability of stock prices on transaction-level intra-day data and to dynamically study patterns of comovement over multiple trading days, we propose a multiple day time series biclustering algorithm (CC-MDTSB) that extends the time series biclustering algorithm (CC-TSB). For identifying biclusters within each trading day, our algorithm provides a faster alternative to the random replacement method in the CC-TSB algorithm. Moreover, our algorithm does not require prespecification of the number of biclusters for each trading day. Instead, we set a threshold on the number of stocks within the biclusters to yield an adaptive stopping criterion for multiple day analysis. An analysis of the biclusters determined over multiple trading days enables us to study the dynamic behaviour of stocks over time. We effectively estimate the comovement probability of each m-tuple of stocks conditional on the other stocks within the dynamic biclusters and propose a method to forecast comovement days using a nonparametric double exponential smoothing procedure. Copyright (c) 2018 John Wiley & Sons, Ltd.