[HTML][HTML] Artificial intelligence techniques in financial trading: A systematic literature review
F Dakalbab, MA Talib, Q Nassir, T Ishak - Journal of King Saud University …, 2024 - Elsevier
Artificial Intelligence (AI) approaches have been increasingly used in financial markets as
technology advances. In this research paper, we conduct a Systematic Literature Review …
technology advances. In this research paper, we conduct a Systematic Literature Review …
[HTML][HTML] Intersecting reinforcement learning and deep factor methods for optimizing locality and globality in forecasting: A review
J Sousa, R Henriques - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Operational forecasting often requires predicting collections of related, multivariate time
series data that are high-dimensional in nature. This can be tackled by fitting a single …
series data that are high-dimensional in nature. This can be tackled by fitting a single …
The recurrent reinforcement learning crypto agent
We demonstrate a novel application of online transfer learning for a digital assets trading
agent. This agent uses a powerful feature space representation in the form of an echo state …
agent. This agent uses a powerful feature space representation in the form of an echo state …
Multi-index evaluation learning-based computation offloading optimization for power internet of things
J Lu, Z Shi, Y Wang, C Pan, S Zhang - Physical Communication, 2023 - Elsevier
Cloud–edge-device collaborative computation offloading can provide flexible and real-time
data processing services for massive resource-constrained devices in power internet of …
data processing services for massive resource-constrained devices in power internet of …
The Evolution of Reinforcement Learning in Quantitative Finance
Reinforcement Learning (RL) has experienced significant advancement over the past
decade, prompting a growing interest in applications within finance. This survey critically …
decade, prompting a growing interest in applications within finance. This survey critically …
Transfer learning for financial data predictions: a systematic review
V Lanzetta - arXiv preprint arXiv:2409.17183, 2024 - arxiv.org
Literature highlighted that financial time series data pose significant challenges for accurate
stock price prediction, because these data are characterized by noise and susceptibility to …
stock price prediction, because these data are characterized by noise and susceptibility to …
Reinforcement Learning Techniques for Stock Trading: A Survey of Current Research.
Reinforcement learning (RL) has emerged as a promising approach for developing
intelligent trading systems in the stock market. The intention of this survey article is to …
intelligent trading systems in the stock market. The intention of this survey article is to …
Reinforcement Learning-based Resilience and Decision Making in Cyber-Physical Systems
F Sangoleye - 2023 - digitalrepository.unm.edu
Cyber-physical systems (CPS) transform how humans interact with technology by integrating
sensing, computation, networking, and control with physical processes to facilitate smart …
sensing, computation, networking, and control with physical processes to facilitate smart …
[PDF][PDF] An investigation of selective classification and reinforcement learning for trading in financial markets
N Chalkidis - 2024 - livrepository.liverpool.ac.uk
Abstract Machine learning (ML) techniques have previously been applied to address the
problem of predicting financial price time series and developing automated trading …
problem of predicting financial price time series and developing automated trading …
Sequential asset ranking in nonstationary time series
We extend the research into cross-sectional momentum trading strategies. Our main result is
our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select …
our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select …