Home  |   Login  |   Logout  |   Access Information  |   Alerts  |   Purchase History  |   Cart  |   Sitemap  |   Help   
 
CrossRef Search
BROWSE SEARCH IEEE XPLORE GUIDE SUPPORT
You requested this document:
1. ADMIRAL: A Data Mining Based Financial Trading System
Rachlin, G.; Last, M.; Alberg, D.; Kandel, A.;
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
March 1 2007-April 5 2007 Page(s):720 - 725
Abstract:

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions
Abstract | Full Text: PDF(6495 KB)    IEEE CNF
 
» Key
IEEE JNL IEEE Journal or Magazine
IEE JNL IEE Journal or Magazine
IEEE CNF IEEE Conference Proceeding
IEE CNF IEE Conference Proceeding
IEEE STD IEEE Standard
 
 
Indexed by IEE Inspec
© Copyright 2008 IEEE – All Rights Reserved