An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …

The financial market, as a complex nonlinear dynamic system frequently influenced by
various factors, such as international investment capital, is very challenging to build trading …

The interdisciplinary relationship between machine learning and financial markets has long
been a theme of great interest among both research communities. Recently, reinforcement …

Deep reinforcement learning (DRL) has attracted strong interest since AlphaGo beat human
professionals, and its applications in stock trading are widespread. In this paper, an …

Portfolio Management is the process of overseeing a group of investments, referred to as a
portfolio, with the objective of achieving predetermined investment goals and objectives …

Research on algorithmic trading using reinforcement learning has become increasingly
popular in recent years. Although most of the current reinforcement learning methods are …

A major problem of the financial industry is the ability to adapt their trading strategies at the
same rate the market evolves. This paper proposes a solution using existing Reinforcement …

Given today's rapidly changing and complex environment, crafting robust methodologies for
decision-making is essential. In algorithmic decision-making processes, the Reinforcement …

Analyzing sentiment related to financial markets helps investors gain an understanding of
the outlook toward a given security. Algorithmic trading is ubiquitous, but a generally …

Sentiment analysis is a natural language processing approach that is widely implemented
for many natural language processing applications such as translation, chatbots, and more …