Sequential Machine learning Approaches for Portfolio Management
This thesis considers a number of approaches to make machine learning algorithms better suited to the sequential nature of financial portfolio management tasks.  We start by considering the problem of the general composition of learning algorithms that must handle temporal learning tasks, in particular that of creating and efficiently updating the training sets in a sequential simulation framework.  We enumerate the desiderata that composition primitives should satisfy, and underscore the difficulty of rigorously and efficiently reaching them.  We follow by introducing a set of algorithms that accomplish the desired objectives, presenting a case-study of a real-world complex learning system for financial decision-making that uses those techniques.  We then describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-best paths search algorithm.  We consider an application in financial portfolio management where we train a learning algorithm to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function.  We illustrate the approach by demonstrating extensive experimental results using a neural network architecture specialized for portfolio management and compare against well-known alternatives.  Finally, we introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets.  By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available.  This information is put to use in an application to actively trade price spreads between commodity futures contracts.  The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.
Machine learning Portfolio management Artificial neural networks Gaussian processes Approximate dynamic programming Non-additive utility optimization Time-series forecasting Commodity spreads