英文标题：
《A Deep Reinforcement Learning Framework for the Financial Portfolio
Management Problem》
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作者：
Zhengyao Jiang, Dixing Xu, Jinjun Liang
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最新提交年份：
2017
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英文摘要：
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.
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中文摘要：
金融投资组合管理是将一只基金不断再分配到不同金融产品中的过程。本文提出了一个无金融模型的强化学习框架，为投资组合管理问题提供了一个深入的机器学习解决方案。该框架由完全相同的独立评估器（EIIE）拓扑、投资组合向量记忆（PVM）、在线随机批量学习（OSBL）方案和充分利用的显式奖励函数组成。该框架通过卷积神经网络（CNN）、基本递归神经网络（RNN）和长短时记忆（LSTM）在三个瞬间实现。在加密货币市场上进行了三次30分钟交易周期的回测实验，对这些策略以及一些最近审查或公布的投资组合选择策略进行了检验。加密货币是ZF发行货币的电子化和去中心化替代品，比特币是最著名的加密货币。该框架的所有三个实例在所有实验中都占据了前三名的位置，与其他比较的交易算法相形见绌。尽管回溯测试中佣金率高达0.25%，但该框架能够在50天内实现至少4倍的回报。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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