Ai in finance: challenges, techniques, and opportunities
L Cao - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
AI in finance refers to the applications of AI techniques in financial businesses. This area has
attracted attention for decades, with both classic and modern AI techniques applied to …
attracted attention for decades, with both classic and modern AI techniques applied to …
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 …
TABLE: Time-aware Balanced Multi-view Learning for stock ranking
Stock ranking is a significant and challenging problem. In recent years, the use of multi-view
data, such as price and tweet, for stock ranking has gained considerable attention in the …
data, such as price and tweet, for stock ranking has gained considerable attention in 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 …
Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach
Quantitative trading and investment decision making are intricate financial tasks that rely on
accurate stock selection. Despite advances in deep learning that have made significant …
accurate stock selection. Despite advances in deep learning that have made significant …
Model-free reinforcement learning from expert demonstrations: a survey
Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation
learning with reinforcement learning that seeks to take advantage of these two learning …
learning with reinforcement learning that seeks to take advantage of these two learning …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …
learning to learn profitable trading strategies from financial market data. However, a single …
DeepTrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions Embedding
Most existing reinforcement learning (RL)-based portfolio management models do not take
into account the market conditions, which limits their performance in risk-return balancing. In …
into account the market conditions, which limits their performance in risk-return balancing. In …
Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making
Large language models (LLMs) have demonstrated notable potential in conducting complex
tasks and are increasingly utilized in various financial applications. However, high-quality …
tasks and are increasingly utilized in various financial applications. However, high-quality …