Health and Portfolio Choices: A Hyperplane Separation Approach

2016 ◽  
Author(s):  
David Crainich ◽  
Louis Eeckhoudt ◽  
Olivier Le Courtois
Keyword(s):  
Portfolio Choices ◽  
Hyperplane Separation

Risk-Shifting or Risk-Hedging of Portfolio Choices Under Bond Default Risks

2012 ◽  
Author(s):  
Jin-ray Lu ◽  
Chih-Ming Chan ◽  
Yi-Long Hsiao ◽  
Kai-Ping Chen
Keyword(s):  
Risk Shifting ◽  
Risk Hedging ◽  
Portfolio Choices ◽  
Default Risks

scholarly journals Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

2020 ◽  
Author(s):  
Humoud Alsabah ◽  
Agostino Capponi ◽  
Octavio Ruiz Lacedelli ◽  
Matt Stern
Keyword(s):  
Opportunity Cost ◽  
Value Function ◽  
Risk Preference ◽  
Portfolio Decisions ◽  
Learning Framework ◽  
Portfolio Choices ◽  
Trading Decisions ◽  
Exploration Exploitation ◽  
The Value Function ◽  
Over Time

Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

Sign in / Sign up

Export Citation Format

Share Document