Factor graphs for universal portfolios

We consider the sequential portfolio investment problem. Building on results in signal processing, machine learning, and other areas, we combine the insights of Cover and Ordentlich´s side information portfolio with those of Blum and Kalai´s transaction costs algorithm to construct one that performs well under transaction costs while taking advantage of side information. We introduce factor graphs as a computational tool for analysis and design of universal (low regret) algorithms, and develop our algorithm with this insight. Finally, we demonstrate that, in contrast to other algorithms, our portfolio performs well over the full range of costs.