Application of Machine Learning to Systematic Strategies

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 Allocation , Value at Risk, Expected Shortfall, in other words 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 using Fractional Brownian Motion), 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.Here, our emphasis is not a new estimator of the covariance matrix, or a variant of Mean Variance framework but an application of Machine Learning techniques to infer no-linear relationships and long range memory between the assets.It has the advantage to remove the linear projection of the assets onto the covariance framework and then capture no-linear relationships between at various time periods.Recent advances in Neural Network, Deep Learning and Machine Learning allows a more efficient modeling of the no-linear statistical relationships between data (ie price, dividends,...). Among them, we can mention Restricted Boltzman Machines, Variationnal Auto-encoders and variations of Recurrent Neural Network, Attention and Highway Long Short Term Memory as well as Factorization Machines for projection on local sub-spaces.Thus, we investigate some of the techniques to develop practical systematic allocation strategies by reducing risks and estimations biases and show the results.