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 …
[HTML][HTML] How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda
V Singh, SS Chen, M Singhania, B Nanavati… - International Journal of …, 2022 - Elsevier
Data availability and accessibility have brought in unseen changes in the finance systems
and new theoretical and computational challenges. For example, in contrast to classical …
and new theoretical and computational challenges. For example, in contrast to classical …
Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey
Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient,
and cost-effective solutions for data collection and communications. Their excellent mobility …
and cost-effective solutions for data collection and communications. Their excellent mobility …
Data science in economics: comprehensive review of advanced machine learning and deep learning methods
This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …
data science in emerging economic applications. The analysis is performed on the novel …
An application of deep reinforcement learning to algorithmic trading
This scientific research paper presents an innovative approach based on deep
reinforcement learning (DRL) to solve the algorithmic trading problem of determining the …
reinforcement learning (DRL) to solve the algorithmic trading problem of determining the …
Machine learning for quantitative finance applications: A survey
F Rundo, F Trenta, AL Di Stallo, S Battiato - Applied Sciences, 2019 - mdpi.com
Featured Application The described approaches can be used in various applications in the
field of quantitative finance from HFT trading systems to financial portfolio allocation and …
field of quantitative finance from HFT trading systems to financial portfolio allocation and …
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
The adoption of computer-aided stock trading methods is gaining popularity in recent years,
mainly because of their ability to process efficiently past information through machine …
mainly because of their ability to process efficiently past information through machine …
A systematic study on reinforcement learning based applications
K Sivamayil, E Rajasekar, B Aljafari, S Nikolovski… - Energies, 2023 - mdpi.com
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Data science and AI in FinTech: An overview
Financial technology (FinTech) has been playing an increasingly critical role in driving
modern economies, society, technology, and many other areas. Smart FinTech is the new …
modern economies, society, technology, and many other areas. Smart FinTech is the new …
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 …