Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior
results in many sequence modeling tasks. The ability of Transformer to capture long-range …

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 …

In this paper we analyze the portfolio selection problem from a novel perspective based on
the analysis and prediction of the time series corresponding to the portfolio's value. Namely …

In recent years, online portfolio selection (OLPS) has received more and more attention from
quantitative investment and artificial intelligence communities. This paper first improves a …

In the stock market, accurate trend judgment and reasonable asset distribution are effective
ways to obtain ideal return. However, the real stock market is affected by the objective …

Abstract Online Portfolio Selection (OLPS) has attracted extensive interest in recent years.
Accurate prediction of future prices and determining the optimal portfolio selection strategy …

Deep learning (DL) has made its way into many disciplines ranging from health care to self-
driving cars. In financial markets, we see a rich literature for DL applications. Particularly …

This paper proposes a novel online learning algorithm, named weak aggregating specialist
algorithm (WASA), and presents its theoretical bound. This algorithm has a flexible feature …

We systematically study the multi-period uncertain portfolio selection problem when the
security return follows the uncertain distribution assessed by experts. However, the existing …

Deep reinforcement learning (RL) has emerged as a promising approach for portfolio
management due to its ability to make sequential decisions. However, applying RL …