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 …
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 …
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 …
Deep learning in the stock market—a systematic survey of practice, backtesting, and applications
K Olorunnimbe, H Viktor - Artificial Intelligence Review, 2023 - Springer
The widespread usage of machine learning in different mainstream contexts has made deep
learning the technique of choice in various domains, including finance. This systematic …
learning the technique of choice in various domains, including finance. This systematic …
An integrated generalized TODIM model for portfolio selection based on financial performance of firms
Multi-criteria decision-making (MCDM) models are well-suited for solving portfolio selection
problems. Diversified financial indices and complex subjective preferences are important …
problems. Diversified financial indices and complex subjective preferences are important …
A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting
W Zhang, Q Chen, J Yan, S Zhang, J Xu - Energy, 2021 - Elsevier
Accurate load forecasting is challenging due to the significant uncertainty of load demand.
Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning …
Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning …
Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory
With artificial intelligence and data quality development, portfolio optimization has improved
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
Deep reinforcement learning for portfolio management of markets with a dynamic number of assets
C Betancourt, WH Chen - Expert Systems with Applications, 2021 - Elsevier
This work proposes a novel portfolio management method using deep reinforcement
learning on markets with a dynamic number of assets. This problem is especially important …
learning on markets with a dynamic number of assets. This problem is especially important …
A Q-learning agent for automated trading in equity stock markets
JB Chakole, MS Kolhe, GD Mahapurush… - Expert Systems with …, 2021 - Elsevier
Trading strategies play a vital role in Algorithmic trading, a computer program that takes and
executes automated trading decisions in the stock market. The conventional wisdom is that …
executes automated trading decisions in the stock market. The conventional wisdom is that …
Dynamic portfolio rebalancing through reinforcement learning
Portfolio managements in financial markets involve risk management strategies and
opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim …
opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim …