Long Horizon Multifactor Investing with Reinforcement Learning

56 Pages Posted: 12 Aug 2022 Last revised: 17 Sep 2022

See all articles by Ruslan Goyenko

Ruslan Goyenko

McGill University - Desautels Faculty of Management

Chengyu Zhang

McGill University - Desautels Faculty of Management

Date Written: August 10, 2022

Abstract

We provide a novel approach to multifactor investing for long term investors who take into consideration medium- to long-term volatility and liquidity risks, while minimizing long-term portfolio level rebalancing needs. Conditioning on multiple factor characteristics and macro-economic states, reinforcement learning (RL) approach allows explicitly imposing multiperiod dynamic volatility, illiquidity and turnover constraints for the overall portfolio level. We find that training the model under explicit long-horizon holding investment period considerations and low frequency rebalancing, which can only be accommodated via RL, dramatically changes perspective of long-term investors' portfolio performances vis-à-vis their short-term peers. Not only we find that a portfolio performance of long-term investor can remain competitive to those of short-term investors, we show that it can substantially outperform the latter, while further benefiting from lower turnover and trading costs.

Suggested Citation

Goyenko, Ruslan and Zhang, Chengyu, Long Horizon Multifactor Investing with Reinforcement Learning (August 10, 2022). Available at SSRN: https://ssrn.com/abstract=4187056 or http://dx.doi.org/10.2139/ssrn.4187056

Ruslan Goyenko (Contact Author)

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Chengyu Zhang

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

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