Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
CLICK HERE TO DOWNLOAD THE PDF
Igor Halperin, Jiayu Liu and Xiao Zhang suggest a simple practical method combining human and artificial intelligence to learn the best investment practices of fund managers and then provide recommendations to improve them. Their approach is based on a combination of inverse reinforcement learning (IRL) and reinforcement learning (RL). First, the IRL component learns the intent of fund managers as suggested by their trading history and recovers their implied
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.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Cutting Edge
Getting more for less: better A / B testing via causal regularisation
A causal machine learning algorithm is used to estimate trades’ price impact
Analytic risk-free rates option pricing with smile and skew
An arbitrage-free short-rate model for backward-looking compounded rates is presented
Smile-consistent basket skew
An analytic approximation for the implied volatility surface of basket options is introduced
Podcast: Artur Sepp on rates volatility and decentralised finance
Quant says high volatility requires pricing and risk management models to be revisited
A robust stochastic volatility model for interest rates
A swaption pricing model based on a single-factor Cheyette model is shown to fit accurately
Podcast: Julien Guyon on volatility modelling and World Cup draws
Academic discusses option pricing, path-dependent volatility and tackling FIFA’s statistical bias
Does the term structure of the at-the-money skew really follow a power law?
A power law can fit the ATM skew, but struggles with short maturities
Obtaining arbitrage-free FX implied volatility by variational inference
An ML-based algorithm that provides implied volatilities from bid-ask prices is proposed
