Water levels in rivers are measured by various devices installed mostly in remote locations
along the rivers, and the collected data are then transmitted via telemetry systems to a data …

Investors make decisions about buying and selling a financial asset based on available
information. The traditional approach in Deep Learning when trying to predict the behavior …

In the field of portfolio management using reinforcement learning, existing approaches have
mainly focused on cash-only trading, overlooking the potential benefits and risks of margin …

Traditional portfolio management methods can incorporate specific investor preferences but
rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) …

Financial market predictions utilize historical data to anticipate future stock prices and
market trends. Traditionally, these predictions have focused on the statistical analysis of …

The goal of portfolio management is to simultaneously maximize the accumulated return and
also to control risk. In consecutive trading periods, portfolio manager needs to continuously …

In the past decade, the application of deep reinforcement learning (DRL) in portfolio
management has attracted extensive attention. However, most classical RL algorithms do …

The rapid development of quantitative portfolio optimization in financial engineering has
produced promising results in AI-based algorithmic trading strategies. However, the …

Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards. In
this work, we consider the RL setting where the goal is to optimize the quantile of the …

With a volume of 2 billion+ trades per day and a market capitalization of 2.56 trillion USD the
national stock exchange (NSE), India is one of the largest stock exchanges in the world …