An Agent-based Recommending System for Stock-Trading
نویسنده
چکیده
The recommending system is frequently used nowadays in Electronic Commerce. A lot of commercial transactions are made by recommends from trustable advisors, experts and partners. Many stock traders place their orders after their friends recommends them to do so. This report exams the effectiveness of recommending system in stock markets in the context of the Penn-Lehman Automated Trading (PLAT) simulator [1], which is a real-time, real-data market simulator. In this report, we analyze the performance of the trading agent using recommends from an online resources. Experimental results will also be provided. ∗The author would like to thank Professor Peter Stone and his CS395T Agent-Based Electronic Commerce Fall’03 Class students from University of Texas at Austin, Department of Computer Science for their valuable comments and discussions
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تاریخ انتشار 2003