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Paper Abstract and Keywords
Presentation 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
Abstract (in Japanese) (See Japanese page) 
(in English) 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.
Keyword (in Japanese) (See Japanese page) 
(in English) Kaggle Challenge / Model Architecture / Boosting / Feature Selection / Liquidity Shock / High Frequency Trading / Market Resillience / Maximal Information Coefficient  
Reference Info. IEICE Tech. Rep., vol. 112, no. 83, IBISML2012-12, pp. 79-84, June 2012.
Paper # IBISML2012-12 
Date of Issue 2012-06-12 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF IBISML2012-12

Conference Information
Committee IBISML  
Conference Date 2012-06-19 - 2012-06-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Campus plaza Kyoto 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics on machine learning and its application 
Paper Information
Registration To IBISML 
Conference Code 2012-06-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering 
Sub Title (in English)  
Keyword(1) Kaggle Challenge  
Keyword(2) Model Architecture  
Keyword(3) Boosting  
Keyword(4) Feature Selection  
Keyword(5) Liquidity Shock  
Keyword(6) High Frequency Trading  
Keyword(7) Market Resillience  
Keyword(8) Maximal Information Coefficient  
1st Author's Name Ildefons Magrans de Abril  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Masashi Sugiyama  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2012-06-20 14:20:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2012-12 
Volume (vol) vol.112 
Number (no) no.83 
Page pp.79-84 
#Pages 6 
Date of Issue 2012-06-12 (IBISML) 

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