Goal-based wealth management with generative reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
CLICK HERE TO DOWNLOAD THE PDF
Matthew Dixon and Igor Halperin develop a reinforcement learning (RL) approach to goal-based wealth management problems such as optimisation of retirement plans or target date funds. They present G-Learner: a reinforcement learning algorithm that does not assume a data generation process and is suitable for noisy data. Their approach is based on G-learning, a
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Investments
Supervised similarity for high-yield bonds
Quantum cognition ML is used to identify tradable alternatives for high-yield corporate bonds
Gaussian GenAI: synthetic market data generation
A method to generate financial time series with mixture models is presented
Estimating mean reversions in interest rate models
The speed of factors’ mean reversion in rate models is estimated
Overcoming Markowitz’s instability with hierarchical risk parity
Portfolio optimisation via HRP provides stable and robust weight estimates
Quantum two-sample test for investment strategies
Quantum algorithms display high discriminatory power in the classification of probability distributions
Choosing trading strategies using importance sampling
The sampling technique is more efficient than A-B testing at comparing decision rules
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
A hard exit threshold strategy for market-makers
A closed-form solution to derive optimal stop-loss and profit-taking levels is presented
