Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices

This article proposes a deep learning solution to the online portfolio selection problem based on learning a latent structure directly from a price time series. It introduces a novel wealth flow matrix for representing a latent structure that has special regular conditions to encode the knowledge about the relative strengths of assets in portfolios. Therefore, a wealth flow model (WFM) is proposed to learn wealth flow matrices and maximize portfolio wealth simultaneously. Compared with existing approaches, our work has several distinctive benefits: (1) the learning of wealth flow matrices makes our model more generalizable than models that only predict wealth proportion vectors, and (2) the exploitation of wealth flow matrices and the exploration of wealth growth are integrated into our deep reinforcement algorithm for the WFM. These benefits, in combination, lead to a highly-effective approach for generating reasonable investment behavior, including short-term trend following, the following of a few losers, no self-investment, and sparse portfolios. Extensive experiments on five benchmark datasets from real-world stock markets confirm the theoretical advantage of the WFM, which achieves the Pareto improvements in terms of multiple performance indicators and the steady growth of wealth over the state-of-the-art algorithms.

Efficient online portfolio simulation using dynamic moving average model and benchmark index

Online portfolio selection and simulation are some of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly, etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed strategy has been shown to outperform both Anticor and OLMAR — the two most prominent portfolio selection strategies in contemporary literature.

An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.