Abstract

http://ssrn.com/abstract=937847
 
 

References (68)



 
 

Citations (1)



 


 



Automated Trading with Boosting and Expert Weighting


Germán Creamer


Stevens Institute of Technology - Wesley J. Howe School of Technology Management

Yoav Freund


University of California, San Diego

April 1, 2010

Quantitative Finance, Vol. 4, No. 10, pp. 401–420

Abstract:     
We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003–2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.

Number of Pages in PDF File: 18

Keywords: Automated trading, machine learning, algorithmic trading, boosting

JEL Classification: C49, C63, G24

Accepted Paper Series


Download This Paper

Date posted: October 17, 2006 ; Last revised: February 20, 2013

Suggested Citation

Creamer, Germán and Freund, Yoav, Automated Trading with Boosting and Expert Weighting (April 1, 2010). Quantitative Finance, Vol. 4, No. 10, pp. 401–420 . Available at SSRN: http://ssrn.com/abstract=937847

Contact Information

German (Herman) G. Creamer (Contact Author)
Stevens Institute of Technology - Wesley J. Howe School of Technology Management ( email )
1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)
HOME PAGE: http://www.creamer-co.com

Yoav Freund
University of California, San Diego ( email )
9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0502
United States
Feedback to SSRN


Paper statistics
Abstract Views: 6,738
Downloads: 2,712
Download Rank: 1,930
References:  68
Citations:  1

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo8 in 0.313 seconds