Application of Machine Learning to Systematic Allocation Strategies by Kevin Noel :: SSRN

We investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Parity, Conditional Value at Risk, ie convex frontier optimization. Although those methods show some efficiency to allocate assets through the convex efficient frontier, they usually rely deeply on the estimation and the usage of the covariance matrix. Being no stationary and having multiple range memory (ie FIGARCH), the statistical estimation of covariance may lead to biases and errors and in the end, bias conclusions. Very extensive literature in econo-metrics, econo-physics, quantitative allocation cover this problem in order to remedy to the statistical estimation of covariance and his bias and issues.