# Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices

**Authors:** Humoud Alsabah, Agostino Capponi, Octavio Ruiz Lacedelli, Matt Stern  
**arXiv:** [1911.02067](https://arxiv.org/abs/1911.02067)  
**Date:** 2019-11-05  
**Summary page:** https://www.summarizepaper.com/en/paper/1911.02067v2/

## Key points

- Reinforcement learning framework for retail robo-advising
- Main challenge: robo-advisors not knowing investor's risk preference initially
- Learning investor's risk preference through observing portfolio choices over time
- Exploration-exploitation algorithm to balance solicitations and autonomous trading decisions
- Aims to converge to optimal value function within a polynomial number of periods
- Correcting for investor mistakes can potentially outperform stand-alone investor
- Novel approach using reinforcement learning techniques in retail robo-advising

## Summary

The paper "Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices" by Humoud Alsabah, Agostino Capponi, Octavio Ruiz Lacedelli, and Matt Stern introduces a reinforcement learning framework for retail robo-advising. The authors address the main challenge of robo-advisors not initially knowing an investor's risk preference. However, through observing portfolio choices in different market environments over time, the robo-advisor can learn this preference. To tackle this problem, the authors develop an exploration-exploitation algorithm that balances costly solicitations from the investor with autonomous trading decisions based on stale estimates of their risk aversion. This algorithm aims to converge to the optimal value function of an omniscient robo-advisor within a polynomial number of periods in both state and action space. The key contribution of this research is demonstrating that by correcting for investor mistakes, the robo-advisor can potentially outperform a stand-alone investor regardless of opportunity cost. Overall, this study presents a novel approach to addressing the challenge of learning investors' risk preferences in retail robo-advising by leveraging reinforcement learning techniques and developing an effective exploration-exploitation algorithm.