There are relatively few conventions followed in reinforcement learning (RL) environments
to structure the action spaces. As a consequence the application of RL algorithms to tasks …

As financial institutions and professionals increasingly incorporate Large Language Models
(LLMs) into their workflows, substantial barriers, including proprietary data and specialized …

Deep Reinforcement Learning (DRL) approaches to Online Portfolio Selection (OLPS) have
grown in popularity in recent years. The sensitive nature of training Reinforcement Learning …

In this paper, we introduce Deep Reinforcement Learning (DRL) for congestion control in the
Transmission Control Protocol/Internet Protocol (TCP/IP) networks. We propose a weighted …

Purpose The purpose of this paper is to systematically analyze the literature around
regulatory compliance and market manipulation in capital markets through the use of …

Quantitative trading through automated systems has been vastly growing in recent years.
The advancement in machine learning algorithms has pushed that growth even further …

Financial portfolio management describes the task of distributing funds and conducting
trading operations on a set of financial assets, such as stocks, index funds, foreign exchange …

One of the most interesting topics for research and also for making a profit is stock trading.
Artificial intelligence has had a great impact on this path. A lot of research has been done to …

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making,
where an agent makes decisions based on its trading strategy developed from market …

This paper focuses on short-term load forecasting for the day ahead using an Ensemble
learning-based Random Forest method. The study uses real-time hourly load data and …