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 …
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 …
Fingpt: Instruction tuning benchmark for open-source large language models in financial datasets
N Wang, H Yang, CD Wang - arXiv preprint arXiv:2310.04793, 2023 - arxiv.org
In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-
based models for the financial sector is increasingly evident. However, the integration of …
based models for the financial sector is increasingly evident. However, the integration of …
GraphSAGE with deep reinforcement learning for financial portfolio optimization
Q Sun, X Wei, X Yang - Expert Systems with Applications, 2024 - Elsevier
Portfolio optimization is an active management strategy that aims to maximize returns and
control risk within reasonable limits. The Proximal Policy Optimization (PPO), a robust on …
control risk within reasonable limits. The Proximal Policy Optimization (PPO), a robust on …
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 …
TradeMaster: a holistic quantitative trading platform empowered by reinforcement learning
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 …
innumerable profit-seeking investors globally. Recent explosion of reinforcement learning in …
FinMem: A performance-enhanced LLM trading agent with layered memory and character design
Abstract Recent advancements in Large Language Models (LLMs) have exhibited notable
efficacy in question-answering (QA) tasks across diverse domains. Their prowess in …
efficacy in question-answering (QA) tasks across diverse domains. Their prowess in …
Lob-based deep learning models for stock price trend prediction: a benchmark study
The recent advancements in Deep Learning (DL) research have notably influenced the
finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL …
finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL …
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
This paper comprehensively reviews the application of machine learning (ML) and AI in
finance, specifically in the context of asset pricing. It starts by summarizing the traditional …
finance, specifically in the context of asset pricing. It starts by summarizing the traditional …