[HTML][HTML] 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 …
Quantum machine learning for finance ICCAD special session paper
Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade, and achieve disruptive impact on numerous industry sectors …
computers during this decade, and achieve disruptive impact on numerous industry sectors …
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 …
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
C Erden - International Journal of Environmental Science and …, 2023 - Springer
Since air pollution negatively affects human health and causes serious diseases, accurate
air pollution prediction is essential regarding environmental sustainability. Although …
air pollution prediction is essential regarding environmental sustainability. Although …
Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
A hybrid neuro-experimental decision support system to classify overconfidence and performance in a simulated bubble using a passive BCI
FM Toma - Expert Systems with Applications, 2023 - Elsevier
Significant advancements in brain-computer interfaces (BCIs) can lead to the development
of enhanced decision-making platforms. Irrational behavior generating potential negative …
of enhanced decision-making platforms. Irrational behavior generating potential negative …
[HTML][HTML] Optimizing automated trading systems with deep reinforcement learning
M Tran, D Pham-Hi, M Bui - Algorithms, 2023 - mdpi.com
In this paper, we propose a novel approach to optimize parameters for strategies in
automated trading systems. Based on the framework of Reinforcement learning, our work …
automated trading systems. Based on the framework of Reinforcement learning, our work …
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 …
A deep Q-learning based algorithmic trading system for commodity futures markets
M Massahi, M Mahootchi - Expert Systems with Applications, 2024 - Elsevier
Nowadays, investors seek more sophisticated decision-making tools that maximize their
profit from investing in the financial markets by suitably determining the optimal position …
profit from investing in the financial markets by suitably determining the optimal position …
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 …