Quantitative trading (QT) has been a popular topic in both academia and the financial
industry since the 1970s. In the last decade, deep reinforcement learning (DRL) has …

This paper reviews 17 studies addressing dynamic option hedging in frictional markets
through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL …

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 …

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 …

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 …

We propose policy gradient algorithms for robust infinite-horizon Markov decision processes
(MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the …

Reinforcement learning (RL) techniques have shown great success in many challenging
quantitative trading tasks, such as portfolio management and algorithmic trading. Especially …

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

The financial markets, which involve more than $90 trillion market capitals, attract the
attention of innumerable investors around the world. Recently, reinforcement learning in …

News-driven quantitative trading (NQT) has been popularly studied in recent years. Most
existing NQT methods are performed in a two-step paradigm, ie, first analyzing markets by a …