We propose an asset allocation strategy that engineers optimal weights before feeding them
to a supervised learning algorithm. In contrast to the traditional approaches, the machine is …

Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with
notable applications spreading in various domains including investment management. In …

The task of portfolio management is the selection of portfolio allocations for every single time
step during an investment period while adjusting the risk-return profile of the portfolio to the …

Reinforcement Learning (RL) has experienced significant advancement over the past
decade, prompting a growing interest in applications within finance. This survey critically …

Portfolio optimization tasks describe sequential decision problems in which the investor's
wealth is distributed across a set of assets. Allocation constraints are used to enforce …

May 2024 Abstract: We develop a novel approach to understand the dynamic diversification
of decision makers with quantile preferences. Due to unavailability of analytical solutions to …

Abstract This investigation explores Reinforcement Learning (RL) for dynamic portfolio
optimization with risk assessment. The challenges include market complexity, uncertain …

Inflation is now on everyone's mind. We have just been through a long period of benign
inflation, 2 and low (real) short term interest rates. Some would argue that this has led to a …

We present a data-driven neural network approach to find optimal dynamic (multi-period)
factor investing 6 strategies in the presence of transaction costs. The factor investing …