講演抄録/キーワード |
講演名 |
2012-06-20 14:20
Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering ○Ildefons Magrans de Abril・Masashi Sugiyama(Tokyo Inst. of Tech.) IBISML2012-12 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
This paper presents the ideas and methods of the winning solution for the Kaggle Algorithmic Trading Challenge. This analysis challenge took place between 11th November 2011 and 8th January 2012, and 264 competitors submitted solutions. The objective of this competition is to develop empirical predictive models to explain stock market prizes following a liquidity shock. The system builds upon the optimal composition of several models and a feature extraction and selection strategy. We used Random Forest as a modeling technique to train all sub-models as a function of an optimal feature set. The modeling approach can cope with the highly complex and low Maximal Information Coefficient between the dependent variable and the feature set and provides a feature ranking metric which we used in our feature selection algorithm. |
キーワード |
(和) |
/ / / / / / / |
(英) |
Kaggle Challenge / Model Architecture / Boosting / Feature Selection / Liquidity Shock / High Frequency Trading / Market Resillience / Maximal Information Coefficient |
文献情報 |
信学技報, vol. 112, no. 83, IBISML2012-12, pp. 79-84, 2012年6月. |
資料番号 |
IBISML2012-12 |
発行日 |
2012-06-12 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
査読に ついて |
本技術報告は査読を経ていない技術報告であり,推敲を加えられていずれかの場に発表されることがあります. |
PDFダウンロード |
IBISML2012-12 |