Multifractal detrended cross-correlation analysis (MFDCCA) is largely used to analyze non-stationary financial time series. Existing methods for such analysis utilize the time series itself as the detrending function with a polynomial. We propose a technique for a more accurate removal of local trends, called indicator-based MFDCCA (IMFD- CCA), which leverages market technical indicators to better determine correlations between financial time series. We evaluated our method on pair trading in the Foreign Exchange Market (Forex) and our results show that the proposed IMFDCCA compared to the MFDCCA reduces the RMSE for the Hurst exponent estimation by 30%.
Article ID: 2021S04
Publisher: Canadian Artificial Intelligence Association