The victory of portfolio construction is mostly based on the future stock market performance.
Recently, the developed machine learning techniques bring more significance of involving …

In this paper, we propose an effective online portfolio selection strategy by integrating expert
opinions, which are obtained based on mean reversion and trading volume. Existing studies …

Portfolio optimization is a pertinent topic of significant importance in the financial literature.
During the portfolio construction, an investor confronts two important steps: portfolio …

Time series analysis based on pattern discovery has received a lot of interests in the fields of
economic physics and machine learning due to its simplicity and ability to reveal complex …

Abstract Machine learning algorithms have been widely used to establish online portfolio
selection strategies. Meta-algorithm, one of the machine learning algorithms, has the …

Based on the momentum principle and adaptive learning mechanism, we design online
portfolio selection strategies, which are suitable for nonstationary financial market. Firstly, we …

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means
of the modified deep learning and hybrid meta-heuristic concept. In the past few years …

Online portfolio selection provides sequential decision-making on asset allocation without
estimating the distribution function of asset return in advance, which is suitable for complex …

We propose a portfolio optimization method with a multi-trend objective and an accelerated
quasi-Newton method (MTO-AQNM). It leverages a BFGS-based quasi-Newton algorithm …

In the realm of short-term portfolio optimization, the integration of machine learning with
exponential growth rate techniques is gaining prominence. This paper introduces a novel …