Deep reinforcement learning (DRL) has achieved significant results in many machine
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …

Large language models (LLMs) have demonstrated notable potential in conducting complex
tasks and are increasingly utilized in various financial applications. However, high-quality …

In recent years, there has been a growing trend of applying reinforcement learning (RL) in
financial applications. This approach has shown great potential for decision-making tasks in …

Financial trading is a crucial component of the markets, informed by a multimodal
information landscape encompassing news, prices, and Kline charts, and encompasses …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

Existing surveys on stock market prediction often focus on traditional machine learning
methods instead of deep learning methods. This motivates us to provide a structured and …

Many studies have employed reinforcement learning (RL) techniques to successfully create
portfolio strategies in recent years. However, since financial markets are extremely noisy …

Portfolio management is a fundamental problem in finance. It involves periodic reallocations
of assets to maximize the expected returns within an appropriate level of risk exposure …

Portfolio management (PM) is a popular financial process that concerns the occasional
reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of …

High-frequency trading (HFT) is using computer algorithms to make trading decisions in
short time scales (eg, second-level), which is widely used in the Cryptocurrency (Crypto) …