Recent advances in reinforcement learning in finance
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 …
revolutionized the techniques on data processing and data analysis and brought new …
An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges
Forecasting the behavior of the stock market is a classic but difficult topic, one that has
attracted the interest of both economists and computer scientists. Over the course of the last …
attracted the interest of both economists and computer scientists. Over the course of the last …
Fusing blockchain and AI with metaverse: A survey
Metaverse as the latest buzzword has attracted great attention from both industry and
academia. Metaverse seamlessly integrates the real world with the virtual world and allows …
academia. Metaverse seamlessly integrates the real world with the virtual world and allows …
Fingpt: Open-source financial large language models
Large language models (LLMs) have shown the potential of revolutionizing natural
language processing tasks in diverse domains, sparking great interest in finance. Accessing …
language processing tasks in diverse domains, sparking great interest in finance. Accessing …
Fingpt: Democratizing internet-scale data for financial large language models
Large language models (LLMs) have demonstrated remarkable proficiency in
understanding and generating human-like texts, which may potentially revolutionize the …
understanding and generating human-like texts, which may potentially revolutionize the …
FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning
Finance is a particularly challenging playground for deep reinforcement learning. However,
establishing high-quality market environments and benchmarks for financial reinforcement …
establishing high-quality market environments and benchmarks for financial reinforcement …
Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction
Advances in deep neural network (DNN) architectures have enabled new prediction
techniques for stock market data. Unlike other multivariate time-series data, stock markets …
techniques for stock market data. Unlike other multivariate time-series data, stock markets …
To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles
Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency
market has witnessed the bursting of different bubbles in the last decade, which in turn have …
market has witnessed the bursting of different bubbles in the last decade, which in turn have …
Dynamic datasets and market environments for financial reinforcement learning
The financial market is a particularly challenging playground for deep reinforcement
learning due to its unique feature of dynamic datasets. Building high-quality market …
learning due to its unique feature of dynamic datasets. Building high-quality market …