Search
Close this search box.
Search
Close this search box.

How is reinforcement learning used in finance?

How is reinforcement learning used in finance?

Artificial Intelligence & Machine Learning

“Modern Perspectives on Reinforcement Learning in Finance” by Petter N. Kolm and Gordon Ritter, published in 2019 and revised in 2024, presents an insightful overview of the application of reinforcement learning (RL) in financial contexts.

Particularly focusing on solving dynamic optimization problems in finance. Including challenges like pricing and hedging of contingent claims, investment and portfolio allocation, in addition, managing transaction costs in securities trading.

A key highlight of this paper is its exploration of the intersection between reinforcement learning and dynamic optimization in finance.

Kolm & Ritter take a look at how RL can become used to formulate and solve complex financial problems that involve decision-making over time, where actions at one point can influence future outcomes. This approach is posited as a more flexible and less assumption-dependent alternative to traditional dynamic programming methods.

Moreover, the paper emphasizes the role of RL in addressing challenges where full models of financial systems are unavailable or where the assumption of perfect environmental dynamics doesn’t hold – commonly referred to as the curses of dimensionality and modeling. Kolm & Ritter illustrate how RL, by leveraging machine learning techniques, can circumvent these issues, enabling more effective solutions to previously intractable problems.

Kolm & Ritter also explore the mathematical foundations of RL in finance!

Moreover, discussing the significance of value functions and policy optimization in the RL framework. The authors highlight the applicability of RL to various financial tasks, demonstrating its potential through examples such as mean-reversion trading and derivative hedging in the presence of market frictions.

Despite the promise of RL in finance, Kolmm & Ritter acknowledge the challenges in its implementation, including the need for correct model specification and data acquisition for training. They also touch upon the importance of generalizing RL models to adapt trained agents to similar but different tasks, a topic of growing interest in the RL community.

Read the full paper:

Modern Perspectives on Reinforcement Learning in Finance by Petter N. Kolm, Gordon Ritter :: SSRN

How is reinforcement learning used in finance?