# A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

Source: [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](<https://www.sota2.com/research/paper/68fa63851e209131ce9ff365>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:43:17.087Z

Authors: Zhengyao Jiang, Dixing Xu, Jinjun Liang

Published: 2017-06-30T08:31:28.000Z

## Abstract

Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Portfolio Management on DJIA 29 constituents 2014-2024 (test)](<https://www.sota2.com/research/sota/portfolio-management-on-djia-29-constituents-2014-2024-test.md>): Portfolio Management; DJIA 29 constituents 2014-2024 (test); Return: 34.49.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1706.10059>)

[PDF](<https://arxiv.org/pdf/1706.10059.pdf>)