Online portfolio selection is a fundamental problem in computational finance, which has
been extensively studied across several research communities, including finance, statistics …

A radically new approach to statistical modelling, which combines mathematical techniques
of Bayesian statistics with the philosophy of the theory of competitive on‐line algorithms, has …

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

In an online convex optimization problem a decision-maker makes a sequence of decisions,
ie, chooses a sequence of points in Euclidean space, from a fixed feasible set. After each …

We present an on‐line investment algorithm that achieves almost the same wealth as the
best constant‐rebalanced portfolio determined in hindsight from the actual market outcomes …

A novel algorithm for actively trading stocks is presented. While tradi-tional universal
algorithms (and technical trading heuristics) attempt to predict winners or trends, our …

This article proposes a novel online portfolio selection strategy named “Passive Aggressive
Mean Reversion”(PAMR). Unlike traditional trend following approaches, the proposed …

Portfolio analysis and optimization, together with the associated risk assessment and
management, require knowledge of the likely distributions of returns at different time scales …