NEUROEVOLUTION AND AN APPLICATION OF AN AGENT BASED MODEL FOR FINANCIAL MARKET

<p>Market prediction is one of the most difficult problems for the machine learning community. Even though, successful trading strategies can be found for the training data using various optimization methods, these strategies usually do not perform well on the test data as expected. Therefore, selection of the correct strategy becomes problematic. In this study, we propose an evolutionary algorithm that produces a variation of trader agents ensuring that the trading strategies they use are different. We discuss that because the selection of the correct strategy is difficult, a variety of agents can be used simultaneously in order to reduce risk. We simulate trader agents on real market data and attempt to optimize their actions. Agent decisions are based on Echo State Networks. The agents take various market indicators as inputs and produce an action such as: buy or sell. We optimize the parameters of the echo state networks using evolutionary algorithms.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2014

Author dc:creator

• Yaman, Anil

Contributors dc:contributor

• Stephen Lucci

• Computer Sciences
• Databases and Information Systems

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/648

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1646