Application of correlation memory matrices in high frequency asset allocation

Tactical asset allocation is one of the most important aspects of modern financial management. This paper looks at a forecasting architecture that can be used for performing asset allocation with higher frequency thus allowing better response to market changes and hence better adherence to customer´s risk-reward profiles. The architecture is based on a variant of correlation memory matrix and utilises Bayesian probabilities of recalled classes to perform better forecasts