Improving asset return forecasts with wavelets

Time series that are used for forecasting asset returns can carry information on trends of different persistence. Therefore, frequency decomposition of standard signals based on wavelets can improve and expand potential predictors. Similarly, asset returns can be decomposed into parts of different persistence. These can be forecast separately and summed up eventually. This “sum-of-parts” method seems to improve forecast accuracy because its aligns predictors and return trends and helps separating signal from noise.