The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

With artificial intelligence and data quality development, portfolio optimization has improved
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …

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 …

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 …

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific
trading order, either liquidation or acquirement, for a given instrument. Towards effective …

The financial markets, which involve over\$90 trillion market capitals, attract the attention of
innumerable profit-seeking investors globally. Recent explosion of reinforcement learning in …

Reinforcement Learning (RL) has experienced significant advancement over the past
decade, prompting a growing interest in applications within finance. This survey critically …

Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition
or liquidation for a number of trading orders of the specific assets. Recent advance in model …

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the
lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to …

In recent years, there has been a growing interest in applying reinforcement learning (RL)
techniques to order execution owing to RL's strong sequential decision-making ability …