An Electronic Market-Maker
An Electronic Market-Maker
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电子做市商
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Christian R. Shelton
中科院分区：
文献类型：
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作者：
Nicholas Tung Chan;Christian R. Shelton
在强化学习框架下提出了一种做市商自适应学习模型。强化学习是一种学习技术，其中代理的目标是最大化长期积累的奖励。假设不了解市场环境，如订单到达或价格过程。相反，代理人从实时市场经验中学习，并制定明确的做市策略，实现多个目标，包括利润最大化和买卖价差最小化。模拟结果表明，初步成功地把学习技术建立做市算法。本报告描述了在脑与认知科学系生物与计算学习中心和马萨诸塞州理工学院人工智能实验室进行的研究。本研究由以下赠款资助：合同号为N 00014 -93-1-3085的海军研究办公室，合同号为N 00014 -00-1-0907的海军研究办公室（DARPA），合同号为IIS-0085836的国家科学基金会（ITR），合同号为DMS-9872936的国家科学基金会（KDI），和国家科学基金会根据合同号IIS-9800032这项研究部分由电子商务中心（MIT）资助。提供额外支持的有：中央电力工业研究所、伊士曼柯达公司、戴姆勒克莱斯勒公司、康柏公司、本田研发公司、有限公司、小松株式会社，美林证券、NEC基金、日本电报电话公司、西门子企业研究公司、惠特克基金会The Whitaker Foundation
This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from realtime market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building marketmaking algorithms. This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and in the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research was sponsored by grants from: Office of Naval Research under contract No. N00014-93-1-3085, Office of Naval Research (DARPA) under contract No. N00014-00-1-0907, National Science Foundation (ITR) under contract No. IIS-0085836, National Science Foundation (KDI) under contract No. DMS-9872936, and National Science Foundation under contract No. IIS-9800032 This research was partially funded by the Center for e-Business (MIT). Additional support was provided by: Central Research Institute of Electric Power Industry, Eastman Kodak Company, DaimlerChrysler AG, Compaq, Honda R&D Co., Ltd., Komatsu Ltd., Merrill-Lynch, NEC Fund, Nippon Telegraph & Telephone, Siemens Corporate Research, Inc., and The Whitaker Foundation.