• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Economic Dynamics and Control >Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach
【24h】

Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach

机译：具有自适应模糊Actor-Critic强化学习的异构交易策略：一种行为方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The present study addresses the learning mechanism of boundedly rational agents in the dynamic and noisy environment of financial markets. The main objective is the development of a system that "decodes" the knowledge-acquisition strategy and the decision-making process of technical analysts called "chartists". It advances the literature on heterogeneous learning in speculative markets by introducing a trading system wherein market environment and agent beliefs are represented by fuzzy inference rules. The resulting functionality leads to the derivation of the parameters of the fuzzy rules by means of adaptive training. In technical terms, it expands the literature that has utilized Actor-Critic reinforcement learning and fuzzy systems in agent-based applications, by presenting an adaptive fuzzy reinforcement learning approach that provides with accurate and prompt identification of market turning points and thus higher predictability. The purpose of this paper is to illustrate this concretely through a comparative investigation against other well-established models. The results indicate that with the inclusion of transaction costs, the profitability of the novel system in case of NASDAQ Composite, FTSE100 and NIKKEI255 indices is consistently superior to that of a Recurrent Neural Network, a Markov-switching model and a Buy and Hold strategy. Overall, the proposed system via the reinforcement learning mechanism, the fuzzy rule-based state space modeling and the adaptive action selection policy, leads to superior predictions upon the direction-of-change of the market.
机译：本研究探讨了在金融市场动态和嘈杂的环境中有限理性主体的学习机制。主要目标是开发一种系统，该系统“解码”称为“图表专家”的技术分析师的知识获取策略和决策过程。通过引入一个交易系统，其中市场环境和代理商信念由模糊推理规则表示，它推动了投机市场中异构学习的文献。所得的功能导致通过自适应训练推导模糊规则的参数。从技术上讲，它通过提出一种自适应的模糊强化学习方法，提供了准确，迅速的市场转折点识别以及更高的可预测性，从而扩展了在基于代理的应用程序中利用Actor-Critic强化学习和模糊系统的文献。本文的目的是通过与其他公认的模型进行比较研究来具体说明这一点。结果表明，考虑到交易成本，在纳斯达克综合指数，FTSE100和NIKKEI255指数的情况下，新系统的盈利能力始终优于递归神经网络，马尔可夫切换模型和买入和持有策略。总体而言，所提出的系统通过强化学习机制，基于模糊规则的状态空间建模和自适应动作选择策略，可以对市场的变化方向做出更好的预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号