完整後設資料紀錄
DC 欄位語言
dc.contributor.authorChen, Yu-Yingen_US
dc.contributor.authorChen, Wei-Lunen_US
dc.contributor.authorHuang, Szu-Haoen_US
dc.date.accessioned2019-04-02T06:04:45Z-
dc.date.available2019-04-02T06:04:45Z-
dc.date.issued2018-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/150929-
dc.description.abstractPairs trading is a statistical arbitrage strategy, which selects a set of assets with similar performance and produces profits during these asset prices far away from rational equilibrium. Once this phenomenon exists, traders can earn the spread by longing the underperforming asset and shorting the outperforming asset. This paper proposed a novel intelligent high-frequency pairs trading system in Taiwan Stock Index Futures (TX) and Mini Index Futures (MTX) market based on deep learning techniques. This research utilized the improved time series visualization method to transfer historical volatilities with different time frames into 2D images which are helpful in capturing arbitrage signals. Moreover, this research improved convolutional neural networks (CNN) model by combining the financial domain knowledge and filterbank mechanism. We proposed Filterbank CNN to extract high-quality features by replacing the random-generating filters with the arbitrage knowledge filters. In summary, the accuracy is enhanced through the proposed method, and it proves that the integrated information technology and financial knowledge could create the better pairs trading system.en_US
dc.language.isoen_USen_US
dc.subjectDeep Learningen_US
dc.subjectFilterbank CNNen_US
dc.subjectPairs Tradingen_US
dc.subjectStatistical Arbitrageen_US
dc.subjectTime seriesen_US
dc.titleDeveloping Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithmen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2018 IEEE INTERNATIONAL CONFERENCE ON AGENTS (ICA)en_US
dc.citation.spage113en_US
dc.citation.epage116en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000454758300021en_US
dc.citation.woscount0en_US
顯示於類別:會議論文