抄録
In this paper, we investigate the investment strategy in the artificial market called U-Mart, which is designed to provide a common test bed for researchers in the fields of economics and information sciences. UMIE is the international experiment of U-Mart as a contests of trading agents. We attended UMIE 2003 and 2004, and our agent won the championship in both experiments. We examin why this agent is strong in UMIE environment. The strategy of this agent is called "on-line learning" or "real-time learning". Concretely, the agent exploits and forecasts futures price fluctuations by means of identifying the environment in reinforcement learning. We examined an efficiency of price forecasting in the classified environment. To examine the efficacy of it, we executed experiments 1000 times with UMIE open-type simulation standard toolkits, and we verified that forecasting futures price fluctuation in our strategy is useful for better trading.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| 編集者 | T.G. Kim |
| ページ | 215-223 |
| ページ数 | 9 |
| 巻 | 3397 |
| 出版物ステータス | Published - 2005 |
| 外部発表 | Yes |
| イベント | 13th International Conference on AIS 2004 - Jeju Island, Korea, Republic of 継続期間: 2004 10 4 → 2004 10 6 |
Other
| Other | 13th International Conference on AIS 2004 |
|---|---|
| 国 | Korea, Republic of |
| 市 | Jeju Island |
| 期間 | 04/10/4 → 04/10/6 |
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ASJC Scopus subject areas
- Hardware and Architecture
これを引用
The investigation of the agent in the artificial market. / Kitakubo, Takahiro; Koyama, Yusuke; Deguchi, Hiroshi.
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 版 / T.G. Kim. 巻 3397 2005. p. 215-223.研究成果: Conference contribution
}
TY - GEN
T1 - The investigation of the agent in the artificial market
AU - Kitakubo, Takahiro
AU - Koyama, Yusuke
AU - Deguchi, Hiroshi
PY - 2005
Y1 - 2005
N2 - In this paper, we investigate the investment strategy in the artificial market called U-Mart, which is designed to provide a common test bed for researchers in the fields of economics and information sciences. UMIE is the international experiment of U-Mart as a contests of trading agents. We attended UMIE 2003 and 2004, and our agent won the championship in both experiments. We examin why this agent is strong in UMIE environment. The strategy of this agent is called "on-line learning" or "real-time learning". Concretely, the agent exploits and forecasts futures price fluctuations by means of identifying the environment in reinforcement learning. We examined an efficiency of price forecasting in the classified environment. To examine the efficacy of it, we executed experiments 1000 times with UMIE open-type simulation standard toolkits, and we verified that forecasting futures price fluctuation in our strategy is useful for better trading.
AB - In this paper, we investigate the investment strategy in the artificial market called U-Mart, which is designed to provide a common test bed for researchers in the fields of economics and information sciences. UMIE is the international experiment of U-Mart as a contests of trading agents. We attended UMIE 2003 and 2004, and our agent won the championship in both experiments. We examin why this agent is strong in UMIE environment. The strategy of this agent is called "on-line learning" or "real-time learning". Concretely, the agent exploits and forecasts futures price fluctuations by means of identifying the environment in reinforcement learning. We examined an efficiency of price forecasting in the classified environment. To examine the efficacy of it, we executed experiments 1000 times with UMIE open-type simulation standard toolkits, and we verified that forecasting futures price fluctuation in our strategy is useful for better trading.
KW - Artificial Market
KW - Reinforcement Learning
KW - U-Mart
UR - http://www.scopus.com/inward/record.url?scp=26844490194&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26844490194&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:26844490194
VL - 3397
SP - 215
EP - 223
BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
A2 - Kim, T.G.
ER -